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Gholami S, Scheppke L, Kshirsagar M, Wu Y, Dodhia R, Bonelli R, Leung I, Sallo FB, Muldrew A, Jamison C, Peto T, Ferres JL, Weeks WB, Friedlander M, Lee AY, Lowy Medical Research Institute. Enhanced Macular Telangiectasia Type 2 Detection: Leveraging Self-Supervised Learning and Ensemble Models. OPHTHALMOLOGY SCIENCE 2025; 5:100710. [PMID: 40225407 PMCID: PMC11987621 DOI: 10.1016/j.xops.2025.100710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 12/05/2024] [Accepted: 01/07/2025] [Indexed: 04/15/2025]
Abstract
Objective To investigate an ensemble-based approach utilizing deep learning models for accurate and interpretable detection of macular telangiectasia (MacTel) type 2 on OCT imaging. Design Retrospective analysis of OCT scans, model development, and assessment. Participants A total of 5200 OCT images from participants in the MacTel Registry conducted by the Lowy Medical Research Institute and from the University of Washington (780 MacTel patients and 1900 non-MacTel patients). Methods Intervention or Testing We trained multiple individual MacTel vs. non-MacTel classification models using traditional supervised learning and self-supervised learning (SSL) and ensembled them using average weighting methods. We investigated diverse methodologies for constructing the ensemble, including varied architectural configurations and learning paradigms of individual models, and manipulating the amount of labeled data accessible for training. Model performance was compared against human expert graders on held-out test set data. Model interpretability was investigated using gradient-weighted class activation maps (Grad-CAM) visualization and by evaluating interrater agreement. Main Outcome Measures For model performance, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, sensitivity, and specificity were reported. For interpretability, interrater agreements and Grad-CAM visualization results were evaluated. Results Despite access to only 419 OCT volumes, including 185 MacTel patients within the 10% labeled training dataset, the ensemble model demonstrated a performance level (AUROC 0.972 [95% confidence interval (CI), 0.971-0.973], AUPRC 0.967 [95% CI, 0.965-0.969], accuracy 91.7%, sensitivity 0.905, and specificity 0.925) comparable to the human experts ensemble (AUROC 0.977 [95% CI, 0.975-0.978], AUPRC 0.987 [95% CI, 0.986-0.987], accuracy 96.8%, sensitivity 0.929, and specificity 1) on a test set of 500 patients. The individual models did not achieve the same performance levels when evaluated separately. Conclusions Even with limited data, combining SSL with ensemble approaches improved MacTel classification accuracy and interpretation compared to the individual models. Self-supervised learning captures meaningful representations from unlabeled data, a key benefit in the setting of limited data such as with rare diseases. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
| | - Lea Scheppke
- The Lowy Medical Research Institute, La Jolla, California
| | | | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, Washington
- Roger and Angie Karalis Johnson Retina Center, Seattle, Washington
| | - Rahul Dodhia
- AI for Good Research Lab, Microsoft, Redmond, Washington
| | | | - Irene Leung
- Moorfields Eye Hosptial NHS Foundation Trust, London, United Kingdom
| | - Ferenc B. Sallo
- Department of Ophthalmology, Jules Gonin Eye Hospital, University of Lausanna, Lausanna, Switzerland
| | - Alyson Muldrew
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Catherine Jamison
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | | | | | - Martin Friedlander
- The Lowy Medical Research Institute, La Jolla, California
- Department of Cellular and Molecular Biology, the Scripps Research Institute, La Jolla, California
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
- Roger and Angie Karalis Johnson Retina Center, Seattle, Washington
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Liu LL, Jing BZ, Liu X, Li RG, Wan Z, Zhang JY, Ouyang XM, Kong QN, Kang XL, Wang DD, Chen HH, Zhao ZH, Liang HY, Huang MY, Zheng CY, Yang X, Zheng XY, Zhang XK, Wei LJ, Cao C, Gao HY, Luo RZ, Cai MY. MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images. Cell Rep Med 2025; 6:102099. [PMID: 40306276 DOI: 10.1016/j.xcrm.2025.102099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2024] [Revised: 11/05/2024] [Accepted: 04/08/2025] [Indexed: 05/02/2025]
Abstract
Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying immunotherapy candidates. An affordable and accessible tool is urgently needed to determine MMR status in EC patients. We introduce MMRNet, a deep convolutional neural network designed to predict MMR-deficient EC from whole-slide images stained with hematoxylin and eosin. MMRNet demonstrates strong performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.897, with a sensitivity of 0.628 and a specificity of 0.949 in internal cross-validation. External validation using three additional datasets results in AUROCs of 0.790, 0.807, and 0.863. Employing a human-machine fusion approach notably improves diagnostic accuracy. MMRNet presents an effective method for identifying EC cases for confirmatory MMR testing and may assist in selecting candidates for immunotherapy.
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Affiliation(s)
- Li-Li Liu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Bing-Zhong Jing
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xuan Liu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Rong-Gang Li
- Department of Pathology, Jiangmen Central Hospital, Jiangmen 529000, China
| | - Zhao Wan
- Department of Pathology, Zhuhai Maternal and Child Health Care Hospital, Zhuhai 519000, China
| | - Jiang-Yu Zhang
- Department of Pathology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, China
| | - Xiao-Ming Ouyang
- Department of Pathology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China
| | - Qing-Nuan Kong
- Department of Pathology, Qingdao Municipal Hospital, Qingdao 266071, China
| | - Xiao-Ling Kang
- Department of Pathology, Guangdong Women and Children Hospital, Guangzhou 511400, China
| | - Dong-Dong Wang
- Department of Pathology, Guangdong Women and Children Hospital, Guangzhou 511400, China
| | - Hao-Hua Chen
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zi-Han Zhao
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hao-Yu Liang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ma-Yan Huang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Cheng-You Zheng
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xia Yang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xue-Yi Zheng
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xin-Ke Zhang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Li-Jun Wei
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chao Cao
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hong-Yi Gao
- Department of Pathology, Guangdong Women and Children Hospital, Guangzhou 511400, China.
| | - Rong-Zhen Luo
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
| | - Mu-Yan Cai
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
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Chai X, Zhang Y, Shi Z, Yang R, Liu X, Zhou Y, Li C, Li Z. An Overview of Predictive Biomarkers and Detection Approaches for Immunotherapy Response in GI Malignancies. J Gastroenterol Hepatol 2025; 40:1059-1069. [PMID: 40074558 DOI: 10.1111/jgh.16930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2025] [Revised: 02/25/2025] [Accepted: 03/01/2025] [Indexed: 03/14/2025]
Abstract
This review provides an in-depth exploration of the evolving role of immunotherapy in gastrointestinal (GI) cancers, with a particular focus on immune checkpoint inhibitors (ICIs) and their associated predictive biomarkers. We present a detailed analysis of established biomarkers, such as PD-L1, microsatellite instability (MSI), tumor mutational burden (TMB), and the tumor microenvironment (TME), as well as emerging biomarkers, including gut microbiota and Epstein-Barr virus (EBV). The predictive value of these biomarkers in guiding clinical decision-making and optimizing immunotherapy outcomes is thoroughly discussed. Additionally, we highlight recent advancements in biomarker evaluation technologies, including next-generation sequencing (NGS), multiplex immunohistochemistry, and artificial intelligence (AI)-driven models. These technologies are instrumental in advancing precision medicine by enhancing the accuracy and efficiency of biomarker detection and facilitating personalized treatment approaches. The integration of these predictive biomarkers with advanced detection technologies has significantly improved the clinical efficacy of immunotherapy in GI cancers by addressing challenges such as tumor heterogeneity, immune evasion, and variable patient responses. By providing a deeper understanding of tumor biology and patient-specific factors, these tools offer the potential to optimize patient selection, treatment regimens, and, ultimately, clinical outcomes. This review underscores the transformative impact of combining predictive biomarkers with cutting-edge technologies, marking a significant step forward in the field of precision oncology for GI cancer treatment.
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Affiliation(s)
- Xinyu Chai
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yiwen Zhang
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhihui Shi
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ruiling Yang
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xumin Liu
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yueting Zhou
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Caiyang Li
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhenhui Li
- Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
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Ding YW, Chen G, Hua HJ, Lu C. Correlation of Epstein-Barr virus infection with pathological characteristics of colorectal cancer and its short-term prognosisr after chemotherapy. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2025; 33:316-323. [DOI: https:/dx.doi.org/10.11569/wcjd.v33.i4.316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2025]
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Ding YW, Chen G, Hua HJ, Lu C. Correlation of Epstein-Barr virus infection with pathological characteristics of colorectal cancer and its short-term prognosisr after chemotherapy. Shijie Huaren Xiaohua Zazhi 2025; 33:316-323. [DOI: 10.11569/wcjd.v33.i4.316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2025] [Revised: 04/04/2025] [Accepted: 04/18/2025] [Indexed: 04/28/2025] Open
Abstract
BACKGROUND In recent years, studies have demonstrated that Epstein-Barr virus (EBV) may play a role in the occurrence of colorectal cancer. Currently, clinical reports on EBV involved in tumor cell proliferation, differentiation, and migration are mainly concentrated in gastric cancer, nasopharyngeal carcinoma, B-cell lymphoma, etc., which have the functions of promoting tumor development and regulating the body's immune microenvironment. The relationship between EBV and colorectal cancer has been less explored.
AIM To analyze the correlation between EBV infection and the pathological characteristics of colorectal cancer as well as its short-term prognosis after chemotherapy to provide reference for its clinical diagnosis, treatment, and prognosis evaluation.
METHODS A total of 45 EBV-positive and 180 EBV-negative colorectal cancer patients admitted to Jinhua Central Hospital from May 2021 to March 2024 were selected. The relationship between EBV infection and the clinicopathological characteristics of colorectal cancer was analyzed, and the EBV-DNA load was compared in patients with different pathological characteristics in the EBV-positive group. The correlation between EBV-DNA load and pathological characteristics of colorectal cancer was analyzed. The short-term prognosis of colorectal cancer patients after chemotherapy was recorded. The pathological characteristics and EBV infection status was compared in patients with different short-term prognoses. The factors that affect the patients' short-term prognosis were analyzed. The predictive value of EBV infection and pathological characteristics for the short-term prognosis of colorectal cancer patients was analyzed.
RESULTS The proportions of patients with stage Ⅲ + Ⅳ, low differentiation, deep invasion (T3 + T4), and lymph node metastasis in the EBV-positive group were higher than those of the EBV-negative group (P < 0.05). EBV-DNA load differed significantly in patients with different degrees of differentiation, infiltration depth, tumor stages, and lymph node metastasis in the EBV-positive group (P < 0.05). EBV-DNA load in patients with EBV-infected colorectal cancer was negatively correlated with the degree of differentiation, and positively correlated with tumor stage, lymph node metastasis, and infiltration depth (P < 0.05). Lymph node metastasis, degree of differentiation, tumor stage, infiltration depth, and EBV infection were all independent factors affecting the short-term prognosis of colorectal cancer patients (P < 0.05). The area under the curve of the combination of tumor stage, infiltration depth, lymph node metastasis, degree of differentiation, and EBV infection in predicting the short-term prognosis of colorectal cancer patients was 0.882 (95% confidence interval: 0.832-0.921), with a sensitivity of 84.93% and specificity of 91.45% (P < 0.05).
CONCLUSION EBV infection is significantly correlated with tumor stage, differentiation degree, depth of invasion, lymph node metastasis, and short-term prognosis after chemotherapy in colorectal cancer patients. The combination of pathological characteristics with EBV detection has high predictive value for the short-term prognosis of colorectal cancer patients.
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Affiliation(s)
- Ying-Wei Ding
- Department of Emergency Medicine, Jinhua Municipal Central Hospital, Jinhua 321000, Zhejiang Province, China
| | - Gang Chen
- Department of Emergency Medicine, Jinhua Municipal Central Hospital, Jinhua 321000, Zhejiang Province, China
| | - Hong-Jun Hua
- Department of Emergency Medicine, Jinhua Municipal Central Hospital, Jinhua 321000, Zhejiang Province, China
| | - Chong Lu
- Department of Emergency Medicine, Jinhua Municipal Central Hospital, Jinhua 321000, Zhejiang Province, China
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Zubair M, Owais M, Hassan T, Bendechache M, Hussain M, Hussain I, Werghi N. An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images. Sci Rep 2025; 15:13087. [PMID: 40240457 PMCID: PMC12003787 DOI: 10.1038/s41598-025-97256-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Accepted: 04/03/2025] [Indexed: 04/18/2025] Open
Abstract
The importance of gastric cancer (GC) and the role of deep learning techniques in categorizing GC histopathology images have recently increased. Identifying the drawbacks of traditional deep learning models, including lack of interpretability, inability to capture complex patterns, lack of adaptability, and sensitivity to noise. A multi-channel attention mechanism-based framework is proposed that can overcome the limitations of conventional deep learning models by dynamically focusing on relevant features, enhancing extraction, and capturing complex relationships in medical data. The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. The proposed framework's strong performance is confirmed by competitive experiments conducted on a publicly available Gastric Histopathology Sub-size Image Database, which yielded remarkable classification accuracies of 99.07% and 98.48% on the validation and testing sets, respectively. Additionally, on the HCRF dataset, the framework achieved high classification accuracy of 99.84% and 99.65% on the validation and testing sets, respectively. The effectiveness and interchangeability of the three channels are further confirmed by ablation and interchangeability experiments, highlighting the remarkable performance of the framework in GC histopathological image classification tasks. This offers an advanced and pragmatic artificial intelligence solution that addresses challenges posed by unique medical image characteristics for intricate image analysis. The proposed approach in artificial intelligence medical engineering demonstrates significant potential for enhancing diagnostic precision by achieving high classification accuracy and treatment outcomes.
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Affiliation(s)
- Muhammad Zubair
- Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia
| | - Muhammad Owais
- Department of Mechanical & Nuclear Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
| | - Taimur Hassan
- Departement of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Malika Bendechache
- ADAPT Research Centre, School of Computer Science, University of Galway, H91 TK33, Galway, Ireland
| | - Muzammil Hussain
- Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan
| | - Irfan Hussain
- Department of Mechanical & Nuclear Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Naoufel Werghi
- Department of Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
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Hu Y, Sirinukunwattana K, Li B, Gaitskell K, Domingo E, Bonnaffé W, Wojciechowska M, Wood R, Alham NK, Malacrino S, Woodcock DJ, Verrill C, Ahmed A, Rittscher J. Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology. Med Image Anal 2025; 101:103437. [PMID: 39798526 DOI: 10.1016/j.media.2024.103437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 10/06/2024] [Accepted: 12/09/2024] [Indexed: 01/15/2025]
Abstract
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales. This work proposes a novel multiple-instance learning (MIL) framework capable of WSI-based cancer morpho-molecular subtyping by fusion of different-scale features. Our method, debuting as Inter-MIL, follows a weakly-supervised scheme. It enables the training of the patch-level encoder for WSI in a task-aware optimisation procedure, a step normally not modelled in most existing MIL-based WSI analysis frameworks. We demonstrate that optimising the patch-level encoder is crucial to achieving high-quality fine-grained and tissue-level subtyping results and offers a significant improvement over task-agnostic encoders. Our approach deploys a pseudo-label propagation strategy to update the patch encoder iteratively, allowing discriminative subtype features to be learned. This mechanism also empowers extracting fine-grained attention within image tiles (the small patches), a task largely ignored in most existing weakly supervised-based frameworks. With Inter-MIL, we carried out four challenging cancer molecular subtyping tasks in the context of ovarian, colorectal, lung, and breast cancer. Extensive evaluation results show that Inter-MIL is a robust framework for cancer morpho-molecular subtyping with superior performance compared to several recently proposed methods, in small dataset scenarios where the number of available training slides is less than 100. The iterative optimisation mechanism of Inter-MIL significantly improves the quality of the image features learned by the patch embedded and generally directs the attention map to areas that better align with experts' interpretation, leading to the identification of more reliable histopathology biomarkers. Moreover, an external validation cohort is used to verify the robustness of Inter-MIL on molecular trait prediction.
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Affiliation(s)
- Yang Hu
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Korsuk Sirinukunwattana
- Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Bin Li
- Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Kezia Gaitskell
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Enric Domingo
- Department of Oncology, University of Oxford, Oxford, UK
| | - Willem Bonnaffé
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Marta Wojciechowska
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Ruby Wood
- Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nasullah Khalid Alham
- Department of Engineering Science, University of Oxford, Oxford, UK; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Stefano Malacrino
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Dan J Woodcock
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK; Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK
| | - Ahmed Ahmed
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Nuffield Department of Womenś and Reproductive Health, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK
| | - Jens Rittscher
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK.
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Huang X, Qin M, Fang M, Wang Z, Hu C, Zhao T, Qin Z, Zhu H, Wu L, Yu G, De Cobelli F, Xie X, Palumbo D, Tian J, Dong D. The application of artificial intelligence in upper gastrointestinal cancers. JOURNAL OF THE NATIONAL CANCER CENTER 2025; 5:113-131. [PMID: 40265096 PMCID: PMC12010392 DOI: 10.1016/j.jncc.2024.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 09/17/2024] [Accepted: 12/20/2024] [Indexed: 04/24/2025] Open
Abstract
Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.
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Affiliation(s)
- Xiaoying Huang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Minghao Qin
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- University of Science and Technology Beijing, Beijing, China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Zipei Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Chaoen Hu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tongyu Zhao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- University of Science and Technology of China, Hefei, China
| | - Zhuyuan Qin
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- Beijing University of Chinese Medicine, Beijing, China
| | | | - Ling Wu
- KiangWu Hospital, Macau, China
| | | | | | | | - Diego Palumbo
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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9
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Li R, Li J, Wang Y, Liu X, Xu W, Sun R, Xue B, Zhang X, Ai Y, Du Y, Jiang J. The artificial intelligence revolution in gastric cancer management: clinical applications. Cancer Cell Int 2025; 25:111. [PMID: 40119433 PMCID: PMC11929235 DOI: 10.1186/s12935-025-03756-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Accepted: 03/18/2025] [Indexed: 03/24/2025] Open
Abstract
Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.
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Affiliation(s)
- Runze Li
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China
| | - Jingfan Li
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China
| | - Yuman Wang
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China
| | - Xiaoyu Liu
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China
| | - Weichao Xu
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China
- Hebei Hospital of Traditional Chinese Medicine, Hebei, 050011, China
| | - Runxue Sun
- Hebei Hospital of Traditional Chinese Medicine, Hebei, 050011, China
| | - Binqing Xue
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China
| | - Xinqian Zhang
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China
| | - Yikun Ai
- North China University of Science and Technology, Tanshan 063000, China
| | - Yanru Du
- Hebei Hospital of Traditional Chinese Medicine, Hebei, 050011, China.
- Hebei Provincial Key Laboratory of Integrated Traditional and Western Medicine Research on Gastroenterology, Hebei, 050011, China.
- Hebei Key Laboratory of Turbidity and Toxicology, Hebei, 050011, China.
| | - Jianming Jiang
- Hebei University of Traditional Chinese Medicine, Hebei, 050011, China.
- Hebei Hospital of Traditional Chinese Medicine, Hebei, 050011, China.
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10
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Liao Y, Chen X, Hu S, Chen B, Zhuo X, Xu H, Wu X, Zeng X, Zeng H, Zhang D, Zhi Y, Zhao L. Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole-Slide Histopathology Images: A Retrospective Multicenter Study. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2408451. [PMID: 39792693 PMCID: PMC11904990 DOI: 10.1002/advs.202408451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 12/15/2024] [Indexed: 01/12/2025]
Abstract
Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response to the combined therapy based HER2-targeted therapy. The application of these therapies is highly dependent on the evaluation of tumor HER2 status. However, there are many risks and challenges in HER2 assessment in GC. Therefore, an economically viable and readily available instrument is requisite for distinguishing HER2 status among patients diagnosed with GC. The study has innovatively developed a deep learning model, HER2Net, which can predict the HER2 status by quantitatively calculating the proportion of HER2 high-expression regions. The HER2Net is trained on an internal training set derived from 531 hematoxylin & eosin (H&E) whole slide images (WSI) of 520 patients. Subsequently, the performance of HER2Net is validated on an internal test set from 115 H&E WSI of 111 patients and an external multi-center test set from 102 H&E WSI of 101 patients. The HER2Net achieves an accuracy of 0.9043 on the internal test set, and an accuracy of 0.8922 on an external test set from multiple institutes. This discovery indicates that the HER2Net can potentially offer a novel methodology for the identification of HER2-positive GC.
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Affiliation(s)
- Yuhan Liao
- Department of PathologyNanfang HospitalSouthern Medical UniversityGuangzhou510515China
- Department of PathologyGuangdong Province Key Laboratory of Molecular Tumor PathologySchool of Basic Medical SciencesSouthern Medical UniversityGuangzhou510515China
| | - Xinhua Chen
- Department of General SurgeryNanfang HospitalSouthern Medical UniversityGuangzhou510515China
| | - Shupeng Hu
- School of Computer ScienceUniversity of ManchesterManchesterM13 9PLUK
| | - Bing Chen
- Department of PathologyNanfang HospitalSouthern Medical UniversityGuangzhou510515China
- Department of PathologyGuangdong Province Key Laboratory of Molecular Tumor PathologySchool of Basic Medical SciencesSouthern Medical UniversityGuangzhou510515China
| | - Xinghua Zhuo
- Department of PathologyNanfang HospitalSouthern Medical UniversityGuangzhou510515China
- Department of PathologyGuangdong Province Key Laboratory of Molecular Tumor PathologySchool of Basic Medical SciencesSouthern Medical UniversityGuangzhou510515China
| | - Hao Xu
- Department of PathologyNanfang HospitalSouthern Medical UniversityGuangzhou510515China
- Department of PathologyGuangdong Province Key Laboratory of Molecular Tumor PathologySchool of Basic Medical SciencesSouthern Medical UniversityGuangzhou510515China
| | - Xiaojin Wu
- Department of PathologyShunde Hospital of Southern Medical University (The First People's Hospital of Shunde)FoshanGuangdong528399China
| | - Xiaofeng Zeng
- Department of PathologyShunde Hospital of Southern Medical University (The First People's Hospital of Shunde)FoshanGuangdong528399China
| | - Huimin Zeng
- Department of PathologyShunde Hospital of Southern Medical University (The First People's Hospital of Shunde)FoshanGuangdong528399China
| | - Donghui Zhang
- Department of PathologyAffiliated Cancer Hospital and Institute of Guangzhou Medical UniversityGuangzhou510095China
| | - Yunfei Zhi
- Department of GastroenterologyChinese Academy of Medical SciencesPeking Union Medical College HospitalBeijing100730China
| | - Liang Zhao
- Department of PathologyNanfang HospitalSouthern Medical UniversityGuangzhou510515China
- Department of PathologyGuangdong Province Key Laboratory of Molecular Tumor PathologySchool of Basic Medical SciencesSouthern Medical UniversityGuangzhou510515China
- Department of PathologyShunde Hospital of Southern Medical University (The First People's Hospital of Shunde)FoshanGuangdong528399China
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11
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Wang W, Liu L. Advances in the application of human-machine collaboration in healthcare: insights from China. Front Public Health 2025; 13:1507142. [PMID: 39975778 PMCID: PMC11835885 DOI: 10.3389/fpubh.2025.1507142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Accepted: 01/23/2025] [Indexed: 02/21/2025] Open
Abstract
In the context of the technological revolution and the digital intelligence era, the contradiction between the rising incidence of diseases and the uneven distribution of quality medical resources is highlighted, and the diagnosis and prevention of diseases, and the prognosis and management of diseases are particularly important in the current society of aging population. "Human-machine collaboration" is based on an intelligent algorithmic system that utilizes the complementary strengths of humans and machines for data exchange, task allocation, decision making and collaborative work to provide more decision support. The traditional healthcare model is highly dependent on the unified management of hospitals, which further increases the burden on the healthcare system and often makes it difficult to formulate and implement personalized and precise rehabilitation programs for patients, which seriously affects their prognosis and quality of life, and increases the risk of re-admission to hospitals. In view of this, human-computer collaboration, an innovation-driven technology, is a groundbreaking solution to the outstanding healthcare issues of today. We use the subject words "Human-machine collaboration" OR "Human-Computer Interaction" OR "HCI" AND "chronic disease" OR "Health management" OR "Precision medicine "were searched for CNKI, Wanfang Data, VIP, CBM, PubMed, Web of science, Embase, Cochrane Library and other Chinese and English databases to identify all relevant studies and compare their results, and finally include 68 relevant literature articles, we identified the broad application of HCI in five main areas: disease screening and treatment, health management, medical education, traditional medicine, and the integration and processing of medical data. The aim is to review the concept of human-computer collaboration, its application in global healthcare environments, and the challenges it faces, with a view to continually driving innovation in healthcare models, optimizing the allocation of healthcare resources, and providing new paradigms for the development and application of innovative technologies in healthcare.
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Affiliation(s)
| | - Liangji Liu
- School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
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12
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Wu Z, Wang T, Lan J, Wang J, Chen G, Tong T, Zhang H. Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features. J Transl Med 2025; 23:13. [PMID: 39762854 PMCID: PMC11702172 DOI: 10.1186/s12967-024-06034-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Accepted: 12/25/2024] [Indexed: 01/11/2025] Open
Abstract
BACKGROUND First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combined with chemotherapy. In clinical practice, HER2 positivity is identified through immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH), whereas deep learning (DL) can predict HER2 status based on tumor histopathological features. However, it remains uncertain whether these deep learning-derived features can predict the efficacy of anti-HER2 therapy. METHODS We analyzed a cohort of 300 consecutive surgical specimens and 101 biopsy specimens, all undergoing HER2 testing, along with 41 biopsy specimens receiving trastuzumab-based therapy for HER2-positive GAC. RESULTS We developed a convolutional neural network (CNN) model using surgical specimens that achieved an area under the curve (AUC) value of 0.847 in predicting HER2 amplification, and achieved an AUC of 0.903 in predicting HER2 status specifically in patients with HER2 2 + expression. The model also predicted HER2 status in gastric biopsy specimens, achieving an AUC of 0.723. Furthermore, our classifier was trained using 41 HER2-positive gastric biopsy specimens that had undergone trastuzumab treatment, our model demonstrated an AUC of 0.833 for the (CR + PR) / (SD + PD) subgroup. CONCLUSION This work explores an algorithm that utilizes hematoxylin and eosin (H&E) staining to accurately predict HER2 status and assess the response to trastuzumab in GAC, potentially facilitating clinical decision-making.
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Affiliation(s)
- Zhida Wu
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Tao Wang
- College of Physics and Information Engineering, Fuzhou University, Xueyuan Road No.2, Fuzhou, Fujian, 350108, China
| | - Junlin Lan
- College of Physics and Information Engineering, Fuzhou University, Xueyuan Road No.2, Fuzhou, Fujian, 350108, China
| | - Jianchao Wang
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Gang Chen
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Xueyuan Road No.2, Fuzhou, Fujian, 350108, China.
| | - Hejun Zhang
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China.
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13
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Sun L, Liu C, Peng J. Specific Immune Responses and Oncolytic Effects Induced by EBV LMP2A-Armed Modified Ankara-Vaccinia Virus Vectored Vaccines in Nasopharyngeal Cancer. Pharmaceutics 2025; 17:52. [PMID: 39861700 PMCID: PMC11768126 DOI: 10.3390/pharmaceutics17010052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Revised: 12/12/2024] [Accepted: 12/18/2024] [Indexed: 01/27/2025] Open
Abstract
BACKGROUND The Epstein-Barr virus (EBV) is intricately linked to a range of human malignancies, with EBV latent membrane protein 2A (LMP2A) emerging as a potential target antigen for immunotherapeutic strategies in the treatment of nasopharyngeal carcinoma (NPC). METHODS The modified vaccinia virus Ankara (MVA) is universally used in vector vaccine research because of its excellent safety profile and highly efficient recombinant gene expression. Here, we constructed a novel MVA-LMP2A recombinant virus and investigated its specific immune response induction and oncolytic effect. RESULTS An immunization dose of 2 × 107 PFU induced the highest specific immune response, which was no longer increased by boost injections after four doses. Three weeks post-final immunization, the specific immune response reached its peak. The MVA-LMP2A vaccine-induced LMP2A-specific cytotoxic T lymphocytes (CTLs), which exhibited substantial efficacy against target cells and effectively inhibited tumor growth. CONCLUSIONS Thus, the MVA-LMP2A recombinant virus effectively induces strong LMP2A-specific cellular and humoral immune responses and anti-tumor activity. This work provides a promising therapeutic strategy for developing NPC candidate vaccines, as well as a reference for the treatment of EBV LMP2-associated malignancies.
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Affiliation(s)
- Liying Sun
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China;
| | - Chao Liu
- State Key Laboratory of Stress Biology, Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361102, China
| | - Junping Peng
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China;
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Nguyen MH, Tran ND, Le NQK. Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives. Curr Med Chem 2025; 32:1968-1986. [PMID: 37711014 DOI: 10.2174/0929867331666230913105829] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 07/04/2023] [Accepted: 08/04/2023] [Indexed: 09/16/2023]
Abstract
Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate for advanced-stage GC patients remains low. Consequently, there is an urgent need to identify novel drug targets and develop effective therapies. However, traditional drug discovery approaches are associated with high costs, time-consuming processes, and a high failure rate, posing challenges in meeting this critical need. In recent years, there has been a rapid increase in the utilization of artificial intelligence (AI) algorithms and big data in drug discovery, particularly in cancer research. AI has the potential to improve the drug discovery process by analyzing vast and complex datasets from multiple sources, enabling the prediction of compound efficacy and toxicity, as well as the optimization of drug candidates. This review provides an overview of the latest AI algorithms and big data employed in drug discovery for GC. Additionally, we examine the various applications of AI in this field, with a specific focus on therapeutic discovery. Moreover, we discuss the challenges, limitations, and prospects of emerging AI methods, which hold significant promise for advancing GC research in the future.
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Affiliation(s)
- Mai Hanh Nguyen
- International Ph.D. Program in Cell Therapy and Regenerative Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
- Pathology and Forensic Medicine Department, 103 Military Hospital, Hanoi, Vietnam
| | - Ngoc Dung Tran
- Pathology and Forensic Medicine Department, 103 Military Hospital, Hanoi, Vietnam
| | - Nguyen Quoc Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
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Sun Q, Li T, Wei Z, Ye Z, Zhao X, Jing J. Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer. Ann Med 2024; 56:2426758. [PMID: 39527470 PMCID: PMC11556273 DOI: 10.1080/07853890.2024.2426758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 09/19/2024] [Accepted: 09/26/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. METHODS In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. RESULTS We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. CONCLUSION The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.
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Affiliation(s)
- Qiuyan Sun
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang, China
| | - Tan Li
- Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, Shenyang, China
| | - Zheng Wei
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang, China
| | - Zhiyi Ye
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang, China
| | - Xu Zhao
- Mathematical Computer Teaching and Research Office, Liaoning Vocational College of Medicine, Shenyang, China
| | - Jingjing Jing
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang, China
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Han G, Liu X, Gao T, Zhang L, Zhang X, Wei X, Lin Y, Yin B. Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics. Mol Cell Probes 2024; 78:101983. [PMID: 39299554 DOI: 10.1016/j.mcp.2024.101983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 09/15/2024] [Accepted: 09/16/2024] [Indexed: 09/22/2024]
Abstract
AIM In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics. METHODS Transcriptome data, pathological images, and clinical data from 443 cases were retrieved from TCGA (The Cancer Genome Atlas Program) for survival analysis. The images were segmented using the Otsu algorithm, and features were extracted using the PyRadiomics package. Subsequently, the cases were randomly divided into a training cohort of 165 cases and a validation cohort of 69 cases. Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves. Additionally, the correlation between the Pathomics score (PS) and immune genes was examined. RESULTS In the multivariate analysis, heightened infiltration of activated CD4 memory T cells was strongly associated with improved overall survival (HR = 0.505, 95 % CI = 0.342-0.745, P < 0.001). The pathomic model, exhibiting robust predictive capability, demonstrated impressive AUC values of 0.844 and 0.750 in both study cohorts. The Decision Curve Analysis (DCA) unequivocally underscored the model's exceptional clinical utility. In a subsequent multivariate analysis, heightened infiltration of the PS also emerged as a significant protective factor for overall survival (HR = 0.506, 95 % CI = 0.329-0.777, P = 0.002). CONCLUSION The pathomic model based on H&E slides for predicting the infiltration degree of activated CD4 memory T cells, along with integrated bioinformatics analysis elucidating potential molecular mechanisms, offers novel prognostic indicators for the precise stratification and individualized prognosis of gastric cancer patients.
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Affiliation(s)
- Guoda Han
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China.
| | - Xu Liu
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Tian Gao
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Lei Zhang
- Department of Clinical Laboratory, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Xiaoling Zhang
- Pathology Department, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Xiaonan Wei
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Yecheng Lin
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
| | - Bohong Yin
- First Department of Gastrointestinal Surgery, Cangzhou Central Hospital, Cangzhou, 061001, Hebei, China
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Alshardan A, Saeed MK, Alotaibi SD, Alashjaee AM, Salih N, Marzouk R. Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection. Health Inf Sci Syst 2024; 12:35. [PMID: 38764569 PMCID: PMC11096294 DOI: 10.1007/s13755-024-00294-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 04/29/2024] [Indexed: 05/21/2024] Open
Abstract
Gastrointestinal (GI) cancer detection includes the detection of cancerous or potentially cancerous lesions within the GI tract. Earlier diagnosis is critical for increasing the success of treatment and improving patient outcomes. Medical imaging plays a major role in diagnosing and detecting GI cancer. CT scans, endoscopy, MRI, ultrasound, and positron emission tomography (PET) scans can help detect lesions, abnormal masses, and changes in tissue structure. Artificial intelligence (AI) and machine learning (ML) methods are being gradually applied to medical imaging for cancer diagnosis. ML algorithms, including deep learning methodologies like convolutional neural network (CNN), are applied frequently for cancer diagnosis. These models learn features and patterns from labelled datasets to discriminate between normal and abnormal areas in medical images. This article presents a new Harbor Seal Whiskers Optimization Algorithm with Deep Learning based Medical Imaging Analysis for Gastrointestinal Cancer Detection (HSWOA-DLGCD) technique. The goal of the HSWOA-DLGCD algorithm is to explore the GI images for the cancer diagnosis. In order to accomplish this, the HSWOA-DLGCD system applies bilateral filtering (BF) approach for the removal of noise. In addition, the HSWOA-DLGCD technique makes use of HSWOA with Xception model for feature extraction. For cancer recognition, the HSWOA-DLGCD technique applies extreme gradient boosting (XGBoost) model. Finally, the parameters compared with the XGBoost system can be selected by moth flame optimization (MFO) system. The experimental results of the HSWOA-DLGCD technique could be verified on the Kvasir database. The simulation outcome demonstrated a best possible solution of the HSWOA-DLGCD method than other recent methods.
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Affiliation(s)
- Amal Alshardan
- Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671 Riyadh, Saudi Arabia
| | - Muhammad Kashif Saeed
- Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia
| | - Shoayee Dlaim Alotaibi
- Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia
| | - Abdullah M. Alashjaee
- Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, 91911 Rafha, Saudi Arabia
| | - Nahla Salih
- Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Radwa Marzouk
- Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671 Riyadh, Saudi Arabia
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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S. Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N. Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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Chen S, Ding P, Guo H, Meng L, Zhao Q, Li C. Applications of artificial intelligence in digital pathology for gastric cancer. Front Oncol 2024; 14:1437252. [PMID: 39529836 PMCID: PMC11551048 DOI: 10.3389/fonc.2024.1437252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 10/07/2024] [Indexed: 11/16/2024] Open
Abstract
Gastric cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in worldwide. Early diagnosis and treatment are essential for a positive outcome. The integration of artificial intelligence in the pathology field is increasingly widespread, including histopathological images analysis. In recent years, the application of digital pathology technology emerged as a potential solution to enhance the understanding and management of gastric cancer. Through sophisticated image analysis algorithms, artificial intelligence technologies facilitate the accuracy and sensitivity of gastric cancer diagnosis and treatment and personalized therapeutic strategies. This review aims to evaluate the current landscape and future potential of artificial intelligence in transforming gastric cancer pathology, so as to provide ideas for future research.
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Affiliation(s)
- Sheng Chen
- School of Clinical Medicine, Hebei University, Affiliated Hospital of Hebei University, Baoding, China
| | - Ping’an Ding
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center, Shijiazhuang, Hebei, China
| | - Honghai Guo
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center, Shijiazhuang, Hebei, China
| | - Lingjiao Meng
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center, Shijiazhuang, Hebei, China
| | - Qun Zhao
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Big Data Analysis and Mining Application for Precise Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center, Shijiazhuang, Hebei, China
| | - Cong Li
- School of Clinical Medicine, Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, China
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20
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Elste J, Saini A, Mejia-Alvarez R, Mejía A, Millán-Pacheco C, Swanson-Mungerson M, Tiwari V. Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions. Biomolecules 2024; 14:911. [PMID: 39199298 PMCID: PMC11352483 DOI: 10.3390/biom14080911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 07/11/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.
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Affiliation(s)
- James Elste
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Akash Saini
- Hinsdale Central High School, 5500 S Grant St, Hinsdale, IL 60521, USA;
| | - Rafael Mejia-Alvarez
- Department of Physiology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA;
| | - Armando Mejía
- Departamento de Biotechnology, Universidad Autónoma Metropolitana-Iztapalapa, Ciudad de Mexico 09340, Mexico;
| | - Cesar Millán-Pacheco
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Av. Universidad No. 1001, Col Chamilpa, Cuernavaca 62209, Mexico;
| | - Michelle Swanson-Mungerson
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Vaibhav Tiwari
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
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21
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Asadi-Aghbolaghi M, Darbandsari A, Zhang A, Contreras-Sanz A, Boschman J, Ahmadvand P, Köbel M, Farnell D, Huntsman DG, Churg A, Black PC, Wang G, Gilks CB, Farahani H, Bashashati A. Learning generalizable AI models for multi-center histopathology image classification. NPJ Precis Oncol 2024; 8:151. [PMID: 39030380 PMCID: PMC11271637 DOI: 10.1038/s41698-024-00652-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 07/11/2024] [Indexed: 07/21/2024] Open
Abstract
Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA's potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets.
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Affiliation(s)
| | - Amirali Darbandsari
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Allen Zhang
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Vancouver General Hospital, Vancouver, BC, Canada
| | | | - Jeffrey Boschman
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Pouya Ahmadvand
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Martin Köbel
- Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada
| | - David Farnell
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Vancouver General Hospital, Vancouver, BC, Canada
| | - David G Huntsman
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- BC Cancer Research Institute, Vancouver, BC, Canada
| | - Andrew Churg
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Vancouver General Hospital, Vancouver, BC, Canada
| | - Peter C Black
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Gang Wang
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - C Blake Gilks
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Vancouver General Hospital, Vancouver, BC, Canada
| | - Hossein Farahani
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Ali Bashashati
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
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22
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Ye Y, Xia L, Yang S, Luo Y, Tang Z, Li Y, Han L, Xie H, Ren Y, Na N. Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images. Front Immunol 2024; 15:1438247. [PMID: 39034991 PMCID: PMC11257957 DOI: 10.3389/fimmu.2024.1438247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024] Open
Abstract
Background Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection. Method We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations. Results In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively. Conclusion We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.
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Affiliation(s)
- Yongrong Ye
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liubing Xia
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shicong Yang
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - You Luo
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zuofu Tang
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Research Center for Brain-Computer Interface, Pazhou Lab, Guangzhou, China
| | - Lanqing Han
- Center for Artificial Intelligence in Medicine, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
| | - Hanbin Xie
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yong Ren
- Scientific Research Project Department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou Lab, Guangzhou, China
- Shensi lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen, China
- The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Ning Na
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Sun S, Li L, Xu M, Wei Y, Shi F, Liu S. Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction. Abdom Radiol (NY) 2024; 49:1779-1791. [PMID: 38656367 DOI: 10.1007/s00261-024-04306-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC. METHODS This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM). RESULTS T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively. CONCLUSION A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.
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Affiliation(s)
- Shuangshuang Sun
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210008, China
| | - Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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24
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Pan Y, Dai H, Wang S, Wang L, Li Q, Wang W, Li J, Qi D, Yang Z, Jia J, Wang Y, Fang Q, Li L, Zhou W, Song Z, Zou S. Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology. Pathobiology 2024; 91:345-358. [PMID: 38718783 DOI: 10.1159/000539010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/09/2024] [Indexed: 06/13/2024] Open
Abstract
INTRODUCTION Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent. METHODS Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers. RESULTS It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925. CONCLUSION Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.
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Affiliation(s)
- Yi Pan
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,
| | - Hongtian Dai
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuhao Wang
- Thorough Lab, Thorough Future, Beijing, China
| | - Lang Wang
- Thorough Lab, Thorough Future, Beijing, China
| | - Qiting Li
- R&D Department, China Academy of Launch Vehicle Technology, Beijing, China
| | - Wenmiao Wang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangtao Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dan Qi
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhaoyang Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jia Jia
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yaxi Wang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qing Fang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weixun Zhou
- Department of Pathology, Peking Union Medical College Hospital, Beijing, China
| | - Zhigang Song
- Department of Pathology, The Chinese PLA General Hospital, Beijing, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Zhang X, Zhao Z, Wang R, Chen H, Zheng X, Liu L, Lan L, Li P, Wu S, Cao Q, Luo R, Hu W, Lyu S, Zhang Z, Xie D, Ye Y, Wang Y, Cai M. A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma. Nat Commun 2024; 15:3768. [PMID: 38704409 PMCID: PMC11069536 DOI: 10.1038/s41467-024-48171-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 04/18/2024] [Indexed: 05/06/2024] Open
Abstract
Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.
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Affiliation(s)
- Xinke Zhang
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Zihan Zhao
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Ruixuan Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Haohua Chen
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Xueyi Zheng
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Lili Liu
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Lilong Lan
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Peng Li
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Shuyang Wu
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Qinghua Cao
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Rongzhen Luo
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Wanming Hu
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Shanshan Lyu
- Department of Pathology, Guangdong Provincial People's Hospital, Guangzhou, 510080, China
| | - Zhengyu Zhang
- Department of Pathology, Nanfang Hospital, Soutern Medical University, Guangzhou, 510515, China
| | - Dan Xie
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
| | - Yaping Ye
- Department of Pathology, Nanfang Hospital, Soutern Medical University, Guangzhou, 510515, China.
| | - Yu Wang
- Department of Pathology, Zhujiang Hospital, Soutern Medical University, Guangzhou, 510280, China.
| | - Muyan Cai
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
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26
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Tian F, Liu D, Wei N, Fu Q, Sun L, Liu W, Sui X, Tian K, Nemeth G, Feng J, Xu J, Xiao L, Han J, Fu J, Shi Y, Yang Y, Liu J, Hu C, Feng B, Sun Y, Wang Y, Yu G, Kong D, Wang M, Li W, Chen K, Li X. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat Med 2024; 30:1309-1319. [PMID: 38627559 PMCID: PMC11108774 DOI: 10.1038/s41591-024-02915-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 03/07/2024] [Indexed: 04/26/2024]
Abstract
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.
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Affiliation(s)
- Fei Tian
- Department of Abdominal Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Dong Liu
- Department of Radiology, The First Affiliated Hospital of Suzhou University, Suzhou, China
| | - Na Wei
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qianqian Fu
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Lin Sun
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Wei Liu
- Department of Pathology, The First Affiliated Hospital of Suzhou University, Suzhou, China
| | - Xiaolong Sui
- Department of Pathology, Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Kathryn Tian
- Harvard Dunster House, Harvard University, Cambridge, MA, USA
| | | | - Jingyu Feng
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Jingjing Xu
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Xiao
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junya Han
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingjie Fu
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yinhua Shi
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yichen Yang
- Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jia Liu
- Department of Abdominal Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Suzhou University, Suzhou, China
| | - Bin Feng
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yan Sun
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yunjun Wang
- Department of Pathology, Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Guohua Yu
- Department of Pathology, Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Dalu Kong
- Department of Abdominal Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Wencai Li
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
| | - Xiangchun Li
- Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
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27
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Jin D, Liang S, Shmatko A, Arnold A, Horst D, Grünewald TGP, Gerstung M, Bai X. Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides. Nat Commun 2024; 15:3063. [PMID: 38594278 PMCID: PMC11004138 DOI: 10.1038/s41467-024-46764-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.
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Affiliation(s)
- Darui Jin
- Image Processing Center, Beihang University, Beijing, 102206, China
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Shen Yuan Honors College, Beihang University, Beijing, 100191, China
| | - Shangying Liang
- Image Processing Center, Beihang University, Beijing, 102206, China
| | - Artem Shmatko
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Arnold
- Charité - Universitätsmedizin Berlin, Institute of Pathology, 10117, Berlin, Germany
| | - David Horst
- Charité - Universitätsmedizin Berlin, Institute of Pathology, 10117, Berlin, Germany
- German Cancer Consortium (DKTK), partner site Berlin, a partnership between DKFZ and Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas G P Grünewald
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
- Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany.
- Hopp Children's Cancer Center (KiTZ) Heidelberg, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.
| | - Moritz Gerstung
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Xiangzhi Bai
- Image Processing Center, Beihang University, Beijing, 102206, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China.
- Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.
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28
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Zheng X, Jing B, Zhao Z, Wang R, Zhang X, Chen H, Wu S, Sun Y, Zhang J, Wu H, Huang D, Zhu W, Chen J, Cao Q, Zeng H, Duan J, Luo Y, Li Z, Lin W, Nie R, Deng Y, Yun J, Li C, Xie D, Cai M. An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer. iScience 2024; 27:109243. [PMID: 38420592 PMCID: PMC10901137 DOI: 10.1016/j.isci.2024.109243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/29/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.
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Affiliation(s)
- Xueyi Zheng
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Bingzhong Jing
- Artificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zihan Zhao
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ruixuan Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Xinke Zhang
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Haohua Chen
- Artificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Shuyang Wu
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yan Sun
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300000, China
| | - Jiangyu Zhang
- Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China
| | - Hongmei Wu
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Wenbiao Zhu
- Department of Pathology, Shantou University medical college Meizhou clinical school, Meizhou People's Hospital, Meizhou 514011, China
| | - Jianning Chen
- Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510635, China
| | - Qinghua Cao
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Jinling Duan
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yuanliang Luo
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zhicheng Li
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Wuhao Lin
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Runcong Nie
- Department of Department of Gastric Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yishu Deng
- Artificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Jingping Yun
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chaofeng Li
- Artificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Dan Xie
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Muyan Cai
- Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
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29
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Dokanei S, Minai‐Tehrani D, Moghoofei M, Rostamian M. Investigating the relationship between Epstein-Barr virus infection and gastric cancer: A systematic review and meta-analysis. Health Sci Rep 2024; 7:e1976. [PMID: 38505684 PMCID: PMC10948593 DOI: 10.1002/hsr2.1976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 02/06/2024] [Accepted: 02/28/2024] [Indexed: 03/21/2024] Open
Abstract
Background and Aims Gastric cancer (GC) is a common cancer type worldwide, and various factors can be involved in its occurrence. One of these factors is Epstein-Barr virus (EBV) infection. In this regard, a systematic review and meta-analysis was conducted to achieve a better understanding of the EBV prevalence in GC samples. Methods English databases were searched and studies that reported the prevalence and etiological factors of EBV related to GC from July 2007 to November 2022 were retrieved. The reported data were selected based on the inclusion and exclusion criteria. The pooled prevalence of EBV infection with 95% confidence intervals was calculated. Quality assessment, heterogeneity testing, and publication bias assessment were also performed. The literature search showed 953 studies, of which 87 studies met our inclusion criteria and were used for meta-analysis. Results The pooled prevalence of EBV infection related to GC was estimated to be 9.5% (95% confidence interval [CI]: 8.2%-11%) in the general population. The prevalence of EBV infection related to GC by gender was 13.5% (95% CI: 11.1%-16.3%) in males and 7.6% (95% CI: 5.4%-10.6%) in females. No significant differences were observed in terms of geographical region. Out of the 87 studies included in the meta-analysis, the most common diagnostic test was in situ hybridization (58 cases). Conclusions Altogether, the results indicated that EBV infection is one of the important factors in the development of GC. However, this does not necessarily mean that EBV infection directly causes GC since other factors may also be involved in the development of GC. Therefore, it is recommended to conduct extensive epidemiological studies on various aspects of the relationship between this virus and GC, which can provide valuable information for understanding the relationship between EBV and GC.
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Affiliation(s)
- Saman Dokanei
- Faculty of Life Sciences and BiotechnologyShahid Beheshti University (GC)TehranIran
| | | | - Mohsen Moghoofei
- Department of Microbiology, Faculty of MedicineKermanshah University of Medical SciencesKermanshahIran
| | - Mosayeb Rostamian
- Infectious Diseases Research Center, Health InstituteKermanshah University of Medical SciencesKermanshahIran
- Student Research CommitteeKermanshah University of Medical SciencesKermanshahIran
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30
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Yu F, Moehring A, Banerjee O, Salz T, Agarwal N, Rajpurkar P. Heterogeneity and predictors of the effects of AI assistance on radiologists. Nat Med 2024; 30:837-849. [PMID: 38504016 PMCID: PMC10957478 DOI: 10.1038/s41591-024-02850-w] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 02/01/2024] [Indexed: 03/21/2024]
Abstract
The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician-AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice.
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Affiliation(s)
- Feiyang Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Alex Moehring
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Oishi Banerjee
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tobias Salz
- Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nikhil Agarwal
- Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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31
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Cai X, Zhang H, Wang Y, Zhang J, Li T. Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts. Int J Oral Sci 2024; 16:16. [PMID: 38403665 PMCID: PMC10894880 DOI: 10.1038/s41368-024-00287-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 12/24/2023] [Accepted: 02/01/2024] [Indexed: 02/27/2024] Open
Abstract
Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.
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Affiliation(s)
- Xinjia Cai
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
| | - Heyu Zhang
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, China
| | - Yanjin Wang
- Department of Oral Pathology, Shanghai Stomatological Hospital & School of Stomatology, Fudan University, Shanghai, China
| | - Jianyun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China.
| | - Tiejun Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China.
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32
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Zhang M, Huang Y, Xie D, Huang R, Zeng G, Liu X, Deng H, Wang H, Lin Z. Machine learning constructs color features to accelerate development of long-term continuous water quality monitoring. JOURNAL OF HAZARDOUS MATERIALS 2024; 461:132612. [PMID: 37801971 DOI: 10.1016/j.jhazmat.2023.132612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 10/08/2023]
Abstract
Long-term continuous water quality monitoring (LTCM) is crucial to ensure the safety of water resources. However, lab-based pollutant detection via machine learning (ML) usually involves colorimetric materials or sensors, and it cannot be ignored that sensor limitations prevent their use for LTCM. To address this challenge, we propose a novel method that leverages image recognition to establish a relationship between pollutant concentration and color. By extracting efficient color variation features from raw pixel matrices using a combination of Kmeans clustering and RGB average features, the concentrations of pollutants that are difficult to distinguish by the naked eyes can be directly captured without the need for sensors and preprocessing. Four ML models (XGBoost, Linear, support vector regression (SVR), and Ridge) achieved up to a 95.9% increase in coefficient of determination (R2) compared to principal component analysis (PCA). In the prediction of the concentration of simulated pollutants such as Cu2+, Co2+, Rhodamine B, and the concentration of Cr(VI) in actual electroplating wastewater, natural resource water and drinking water, over 95% R2 was achieved. The method reported in our work can effectively capture subtle color changes that cannot be observed by the naked eyes without any preprocessing of water samples, providing a reliable method for LTCM.
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Affiliation(s)
- Mengyuan Zhang
- School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China
| | - Yanquan Huang
- School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China
| | - Dongsheng Xie
- School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China
| | - Renfeng Huang
- School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China
| | - Gongchang Zeng
- School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China
| | - Xueming Liu
- School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China.
| | - Hong Deng
- School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China.
| | - Haiying Wang
- School of Metallurgy and Environment, Central South University, Changsha 410083, China
| | - Zhang Lin
- School of Metallurgy and Environment, Central South University, Changsha 410083, China
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Wang R, Khurram SA, Walsh H, Young LS, Rajpoot N. A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images. Mod Pathol 2023; 36:100320. [PMID: 37652399 DOI: 10.1016/j.modpat.2023.100320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/28/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023]
Abstract
The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens, such as alcohol, tobacco, and infection with human papillomavirus (HPV). Because HPV infection influences the prognosis, treatment, and survival of patients with HNSCC, it is important to determine the HPV status of these tumors. In this article, we propose a novel deep learning pipeline for HPV infection status prediction with state-of-the-art performance in HPV detection using only whole-slide images of routine hematoxylin and eosin-stained HNSCC sections. We show that our Digital-HPV score generated from hematoxylin and eosin slides produces statistically significant patient stratifications in terms of overall and disease-specific survival. In addition, quantitative profiling of the spatial tumor microenvironment and analysis of the immune profiles show relatively high levels of lymphocytic infiltration in tumor and tumor-associated stroma. High levels of B cells and T cells and low macrophage levels were also identified in HPV-positive patients compared to HPV-negative patients, confirming different immune response patterns elicited by HPV infection in patients with HNSCC.
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Affiliation(s)
- Ruoyu Wang
- Department of Computer Science, Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom
| | - Syed Ali Khurram
- Unit of Oral and Maxillofacial Pathology, School of Clinical Dentistry, the University of Sheffield, Sheffield, United Kingdom
| | - Hannah Walsh
- Unit of Oral and Maxillofacial Pathology, School of Clinical Dentistry, the University of Sheffield, Sheffield, United Kingdom
| | - Lawrence S Young
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom
| | - Nasir Rajpoot
- Department of Computer Science, Tissue Image Analytics Centre, University of Warwick, Coventry, United Kingdom; the Alan Turing Institute, London, United Kingdom.
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Gong Z, Li X, Shi M, Cai G, Chen S, Ye Z, Gan X, Yang R, Wang R, Chen Z. Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network. Quant Imaging Med Surg 2023; 13:8053-8066. [PMID: 38106266 PMCID: PMC10722026 DOI: 10.21037/qims-23-744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/28/2023] [Indexed: 12/19/2023]
Abstract
Background The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations. Methods This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN) backbones and a bilinear pooling module to predict the binary thickness of buccal bone (thick or thin) of the anterior maxilla in an end-to-end manner. The methods of 5-fold cross-validation and model ensemble were adopted at the training and testing stages. The visualization methods of Gradient Weighted Class Activation Mapping (Grad-CAM), Guided Grad-CAM, and layer-wise relevance propagation (LRP) were used for revealing the important features on which the model focused. The performance metrics and efficacy were compared between BCNN, dentists of different clinical experience (i.e., dental student, junior dentist, and senior dentist), and the fusion of BCNN and dentists to investigate the clinical feasibility of BCNN. Results Based on the dataset of 4,000 CBCT images from 1,000 patients (aged 36.15±13.09 years), the BCNN with visual geometry group (VGG)16 backbone achieved an accuracy of 0.870 [95% confidence interval (CI): 0.838-0.902] and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.924 (95% CI: 0.896-0.948). Compared with the conventional CNNs, BCNN precisely located the buccal bone wall over irrelevant regions. The BCNN generally outperformed the expert-level dentists. The clinical diagnostic performance of the dentists was improved with the assistance of BCNN. Conclusions The application of BCNN to the quantitative analysis of binary buccal bone thickness validated the model's excellent ability of subtle feature extraction and achieved expert-level performance. This work signals the potential of fine-grained image recognition networks to the precise quantitative analysis of micro-scale structures.
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Affiliation(s)
- Zhuohong Gong
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Xiaohui Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Mengru Shi
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Gengbin Cai
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Shijie Chen
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Zejun Ye
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Xuejing Gan
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Ruihan Yang
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Ruixuan Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Zetao Chen
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
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Hu M, Xia X, Chen L, Jin Y, Hu Z, Xia S, Yao X. Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma. Ann Hepatol 2023; 28:101137. [PMID: 37451515 DOI: 10.1016/j.aohep.2023.101137] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/17/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023]
Abstract
Most cases of hepatocellular carcinoma (HCC) are able to be diagnosed through regular surveillance in an identifiable patient population with chronic hepatitis B or cirrhosis. Nevertheless, 50% of global cases might present incidentally owing to symptomatic advanced-stage HCC after worsening of liver dysfunction. A systematic search based on PUBMED was performed to identify relevant outcomes, covering newer surveillance modalities including secretory proteins, DNA methylation, miRNAs, and genome sequencing analysis which proposed molecular expression signatures as ideal tools in the early-stage HCC detection. In the face of low accuracy without harmonization on the analytical approaches and data interpretation for liquid biopsy, a more accurate incidence of HCC will be unveiled by using deep machine learning system and multiplex immunohistochemistry analysis. A combination of molecular-secretory biomarkers, high-definition imaging and bedside clinical indexes in a surveillance setting offers a comprehensive range of HCC potential indicators. In addition, the sequential use of numerous lines of systemic anti-HCC therapies will simultaneously benefit more patients in survival. This review provides an overview on the most recent developments in HCC theranostic platform.
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Affiliation(s)
- Miner Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Xiaojun Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Lichao Chen
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yunpeng Jin
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Zhenhua Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
| | - Shudong Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Xudong Yao
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
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Yang Y, Shao Y, Wang J, Cheng Q, Yang H, Li Y, Liu J, Zhou Y, Zhou Z, Wang M, Ji B, Yao J. Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer. Front Oncol 2023; 13:1185240. [PMID: 37746295 PMCID: PMC10516559 DOI: 10.3389/fonc.2023.1185240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/07/2023] [Indexed: 09/26/2023] Open
Abstract
Purpose To explore the predictive value of multiple immune-inflammatory biomarkers including serum VEGFA and systemic immune-inflammation index (SII) in HER2-negative advanced gastric cancer (AGC) and establish nomograms for predicting the first-line chemotherapeutic efficacy, progression-free survival (PFS) and overall survival (OS) of patients with this fatal disease. Methods From November 2017 to April 2022, 102 and 34 patients with a diagnosis of HER2-negative AGC at the First Affiliated Hospital of Bengbu Medical College were enrolled as development and validation cohorts, respectively. Univariate and multivariate analyses were performed to evaluate the clinical value of the candidate indicators. The variables were screened using LASSO regression analysis. Predictive models were developed using significant predictors and are displayed as nomograms. Results Baseline VEGFA expression was significantly higher in HER2-negative AGC patients than in nonneoplastic patients and was associated with malignant serous effusion and therapeutic efficacy (all p<0.001). Multivariate analysis indicated that VEGFA was an independent predictor for first-line therapeutic efficacy and PFS (both p<0.01) and SII was an independent predictor for first-line PFS and OS (both p<0.05) in HER2-negative AGC patients. The therapeutic efficacy model had an R2 of 0.37, a Brier score of 0.15, and a Harrell's C-index of 0.82 in the development cohort and 0.90 in the validation cohort. The decision curve analysis indicated that the model added more net benefits than VEGFA assessment alone. The PFS/OS models had Harrell's C-indexes of 0.71/0.69 in the development cohort and 0.71/0.62 in the validation cohort. Conclusion The established nomograms integrating serum VEGFA/SII and commonly available baseline characteristics provided satisfactory performance in predicting the therapeutic efficacy and prognosis of HER2-negative AGC patients.
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Affiliation(s)
- Yan Yang
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yu Shao
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Junjun Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Qianqian Cheng
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Hanqi Yang
- Department of Clinical Laboratory, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yulong Li
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Jing Liu
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yangyang Zhou
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Zhengguang Zhou
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Mingxi Wang
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Baoan Ji
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, United States
| | - Jinghao Yao
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
- Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Li XH, Luo MM, Wang ZX, Wang Q, Xu B. The role of fungi in the diagnosis of colorectal cancer. Mycology 2023; 15:17-29. [PMID: 38558845 PMCID: PMC10977015 DOI: 10.1080/21501203.2023.2249492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/14/2023] [Indexed: 04/04/2024] Open
Abstract
Colorectal cancer (CRC) is a prevalent tumour with high morbidity rates worldwide, and its incidence among younger populations is rising. Early diagnosis of CRC can help control the associated mortality. Fungi are common microorganisms in nature. Recent studies have shown that fungi may have a similar association with tumours as bacteria do. As an increasing number of tumour-associated fungi are discovered, this provides new ideas for the diagnosis and prognosis of tumours. The relationship between fungi and colorectal tumours has also been recently identified by scientists. Therefore, this paper describes the limitations and prospects of the application of fungi in diagnosing CRC and predicting CRC prognosis.
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Affiliation(s)
- Xu-Huan Li
- Department of General Practice, The Fourth Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ming-Ming Luo
- Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Zu-Xiu Wang
- Department of General Practice, The Fourth Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qi Wang
- Department of Health Statistics, School of PubliHealth and Health Management, Gannan Medical University, Ganzhou, China
| | - Bin Xu
- Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China
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Zeng W, Zhu J, Zeng D, Guo J, Huang G, Zeng Y, Wang L, Bin J, Liao Y, Shi M, Liao W. Epigenetic Modification-Associated Molecular Classification of Gastric Cancer. J Transl Med 2023; 103:100170. [PMID: 37150296 DOI: 10.1016/j.labinv.2023.100170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 04/02/2023] [Accepted: 04/20/2023] [Indexed: 05/09/2023] Open
Abstract
Epigenetic modification is involved in tumorigenesis and cancer progression. We developed an epigenetic modification-associated molecular classification of gastric cancer (GC) to identify signature genes that accurately predict prognosis and the efficacy of immunotherapy. Least absolute shrinkage and selection operator and multivariate Cox regression analysis were conducted to develop an epigenetic modification-associated molecular classification. We investigated the significance of PIP4P2, an independent prognostic factor of the classification system, in predicting the prognosis and immunotherapy efficacy of patients with GC. The epigenetic modification-associated molecular classification was highly associated with the clinicopathological characteristics of patients and the existing classification of GC. PIP4P2 was highly expressed in GC tissue and tumor-associated macrophages. High PIP4P2 expression in GC tissue-induced tumor progression by activating PI3K/AKT signal transduction had a negative impact on immunotherapy efficacy. High expression of PIP4P2 in macrophages was correlated with poor prognosis in patients with GC. PIP4P2 is an independent unfavorable prognostic factor of epigenetic modification-associated molecular classification, is involved in tumorigenic progression, and is essential for assessing the prognosis and immunotherapy efficacy of GC.
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Affiliation(s)
- Wei Zeng
- Department of Oncology, First Peoples Hospital of Shunde, Shunde Hospital of Southern Medical University, Shunde, China; Department of Hematology and Oncology, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Jinfeng Zhu
- Department of General Surgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Dongqiang Zeng
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China
| | - Jian Guo
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China
| | - Genjie Huang
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China
| | - Yu Zeng
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China
| | - Ling Wang
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China
| | - Jianping Bin
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yulin Liao
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Min Shi
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China
| | - Wangjun Liao
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University, Guangzhou, China.
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Cai X, Li L, Yu F, Guo R, Zhou X, Zhang F, Zhang H, Zhang J, Li T. Development of a Pathomics-Based Model for the Prediction of Malignant Transformation in Oral Leukoplakia. J Transl Med 2023; 103:100173. [PMID: 37164265 DOI: 10.1016/j.labinv.2023.100173] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/14/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Accurate prognostic stratification of oral leukoplakia (OLK) with risk of malignant transformation into oral squamous cell carcinoma is crucial. We developed an objective and powerful pathomics-based model for the prediction of malignant transformation in OLK using hematoxylin and eosin (H&E)-stained images. In total, 759 H&E-stained images from multicenter cohorts were included. A training set (n = 489), validation set (n = 196), and testing set (n = 74) were used for model development. Four deep learning methods were used to train and validate the model constructed using H&E-stained images. Pathomics features generated through deep learning combined with machine learning algorithms were used to develop a pathomics-based model. Immunohistochemical staining of Ki67, p53, and PD-L1 was used to interpret the black box of the model. Pathomics-based models predicted the malignant transformation of OLK (validation set area under curve [AUC], 0.899; testing set AUC, 0.813) and significantly identified high-risk and low-risk populations. The prediction performance of malignant transformation from dysplasia grading (validation set AUC, 0.743) was lower than that of the pathomics-based model. The expressions of Ki67, p53, and PD-L1 were correlated with various pathomics features. The pathomics-based model accurately predicted the malignant transformation of OLK and may be useful for the objective and rapid assessment of the prognosis of patients with OLK.
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Affiliation(s)
- Xinjia Cai
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China; Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China
| | - Long Li
- Hunan Key Laboratory of Oral Health Research, Xiangya Stomatological Hospital, Central South University, Changsha, China
| | - Feiyan Yu
- Department of Oral Medicine, Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China
| | - Rongrong Guo
- Department of Oral Medicine, Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China
| | - Xuan Zhou
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
| | - Fang Zhang
- Department of Oral Medicine, Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China.
| | - Heyu Zhang
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, China.
| | - Jianyun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China; Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China.
| | - Tiejun Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China; Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China.
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Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Semin Cancer Biol 2023; 93:83-96. [PMID: 37116818 DOI: 10.1016/j.semcancer.2023.04.009] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/12/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.
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Affiliation(s)
- Zhe Wang
- Department of Digestive Diseases 1, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning, China
| | - Yang Liu
- Department of Gastric Surgery, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning, China.
| | - Xing Niu
- China Medical University, Shenyang 110122, Liaoning, China.
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Zhang Y, Lyu H, Guo R, Cao X, Feng J, Jin X, Lu W, Zhao M. Epstein‒Barr virus-associated cellular immunotherapy. Cytotherapy 2023:S1465-3249(23)00099-3. [PMID: 37149797 DOI: 10.1016/j.jcyt.2023.04.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 03/24/2023] [Accepted: 04/10/2023] [Indexed: 05/08/2023]
Abstract
Epstein‒Barr virus (EBV) is a human herpes virus that is saliva-transmissible and universally asymptomatic. It has been confirmed that more than 90% of the population is latently infected with EBV for life. EBV can cause a variety of related cancers, such as nasopharyngeal carcinoma, diffuse large B-cell lymphoma, and Burkitt lymphoma. Currently, many clinical studies have demonstrated that EBV-specific cytotoxic T lymphocytes and other cell therapies can be safely and effectively transfused to prevent and treat some diseases caused by EBV. This review will mainly focus on discussing EBV-specific cytotoxic T lymphocytes and will touch on therapeutic EBV vaccines and chimeric antigen receptor T-cell therapy briefly.
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Affiliation(s)
- Yi Zhang
- First Center Clinic College of Tianjin Medical University, Tianjin, China.
| | - Hairong Lyu
- Department of Hematology, Tianjin First Central Hospital, Tianjin, China
| | - Ruiting Guo
- First Center Clinic College of Tianjin Medical University, Tianjin, China
| | - Xinping Cao
- First Center Clinic College of Tianjin Medical University, Tianjin, China
| | - Juan Feng
- Tianjin Jizhou District People's Hospital, Tianjin, China
| | - Xin Jin
- Department of Hematology, Tianjin First Central Hospital, Tianjin, China
| | - Wenyi Lu
- Department of Hematology, Tianjin First Central Hospital, Tianjin, China.
| | - Mingfeng Zhao
- Department of Hematology, Tianjin First Central Hospital, Tianjin, China.
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Li W, Duan X, Chen X, Zhan M, Peng H, Meng Y, Li X, Li XY, Pang G, Dou X. Immunotherapeutic approaches in EBV-associated nasopharyngeal carcinoma. Front Immunol 2023; 13:1079515. [PMID: 36713430 PMCID: PMC9875085 DOI: 10.3389/fimmu.2022.1079515] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
Epstein-Barr virus (EBV) was the first tumor virus in humans. Nasopharyngeal carcinoma (NPC) accounts for approximately 60% of the 200,000 new tumor cases caused by EBV infection worldwide each year. NPC has an insidious onset and is highly malignant, with more than 70% of patients having intermediate to advanced disease at the time of initial diagnosis, and is strongly implicated in epithelial cancers as well as malignant lymphoid and natural killer/T cell lymphomas. Over 90% of patients with confirmed undifferentiated NPC are infected with EBV. In recent decades, much progress has been made in understanding the molecular mechanisms of NPC and developing therapeutic approaches. Radiotherapy and chemotherapy are the main treatment options for NPC; however, they have a limited efficacy in patients with locally advanced or distant metastatic tumors. Tumor immunotherapy, including vaccination, adoptive cell therapy, and immune checkpoint blockade, represents a promising therapeutic approach for NPC. Significant breakthroughs have recently been made in the application of immunotherapy for patients with recurrent or metastatic NPC (RM-NPC), indicating a broad prospect for NPC immunotherapy. Here, we review important research findings regarding immunotherapy for NPC patients and provide insights for future research.
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Affiliation(s)
- Wenting Li
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Xiaobing Duan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Xingxing Chen
- Department of Urology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Meixiao Zhan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Haichuan Peng
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Ya Meng
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China,Faculty of Health Sciences, University of Macau, Macau, Macau SAR, China
| | - Xiaobin Li
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Xian-Yang Li
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China,Department of R&D, OriCell Therapeutics Co. Ltd, Pudong, Shanghai, China,*Correspondence: Xiaohui Dou, ; Guofu Pang, ; Xian-Yang Li,
| | - Guofu Pang
- Department of Urology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China,*Correspondence: Xiaohui Dou, ; Guofu Pang, ; Xian-Yang Li,
| | - Xiaohui Dou
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China,Health Management Center, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China,*Correspondence: Xiaohui Dou, ; Guofu Pang, ; Xian-Yang Li,
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Yavuz A, Alpsoy A, Gedik EO, Celik MY, Bassorgun CI, Unal B, Elpek GO. Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology. Artif Intell Gastroenterol 2022; 3:142-162. [DOI: 10.35712/aig.v3.i5.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/25/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.
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Affiliation(s)
- Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Elif Ocak Gedik
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | | | | | - Betul Unal
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
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Hu W, Chen H, Liu W, Li X, Sun H, Huang X, Grzegorzek M, Li C. A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer. Front Med (Lausanne) 2022; 9:1072109. [PMID: 36569152 PMCID: PMC9767945 DOI: 10.3389/fmed.2022.1072109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Gastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. Therefore, this paper compares the performance of multiple algorithms in anticipation of applying ensemble learning to a practical gastric cancer classification problem. Methods The complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier. Results The experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity. Discussion Suitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.
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Affiliation(s)
- Weiming Hu
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Haoyuan Chen
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Wanli Liu
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Xiaoyan Li
- Department of Pathology, Liaoning Cancer Hospital and Institute, Cancer Hospital, China Medical University, Shenyang, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital, China Medical University, Shenyang, China
| | - Xinyu Huang
- Institute of Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Luebeck, Luebeck, Germany
- Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
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Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology. Sci Rep 2022; 12:18466. [PMID: 36323712 PMCID: PMC9630260 DOI: 10.1038/s41598-022-22731-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/18/2022] [Indexed: 11/20/2022] Open
Abstract
The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model's performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.
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