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1
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Li B, Xiao M, Zeng R, Zhang L. Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis. BMC Med Inform Decis Mak 2025; 25:188. [PMID: 40375082 DOI: 10.1186/s12911-025-03012-9] [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: 07/08/2023] [Accepted: 04/11/2025] [Indexed: 05/18/2025] Open
Abstract
BACKGROUND Colorectal cancer is the fourth most deadly cancer, with a high mortality rate and a high probability of recurrence and metastasis. Since continuous examinations and disease monitoring for patients after surgery are currently difficult to perform, it is necessary for us to develop a predictive model for colorectal cancer metastasis and recurrence to improve the survival rate of patients. RESULTS Previous studies mostly used only clinical or radiological data, which are not sufficient to explain the in-depth mechanism of colorectal cancer recurrence and metastasis. Therefore, this study proposes such a multiomics data-based predictive model for the recurrence and metastasis of colorectal cancer. LR, SVM, Naïve-bayes and ensemble learning models are used to build this predictive model. CONCLUSIONS The experimental results indicate that our proposed multiomics data-based ensemble learning model effectively predicts the recurrence and metastasis of colorectal cancer.
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Affiliation(s)
- Bing Li
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
- CAS Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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2
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Chen L, Xu F, Chen L. Diagnostic accuracy of artificial intelligence based on imaging data for predicting distant metastasis of colorectal cancer: a systematic review and meta-analysis. Front Oncol 2025; 15:1558915. [PMID: 40421093 PMCID: PMC12104061 DOI: 10.3389/fonc.2025.1558915] [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: 01/11/2025] [Accepted: 04/17/2025] [Indexed: 05/28/2025] Open
Abstract
Background Colorectal cancer is the third most common malignant tumor with the third highest incidence rate. Distant metastasis is the main cause of death in colorectal cancer patients. Early detection and prognostic prediction of colorectal cancer has improved with the widespread use of artificial intelligence technologies. Purpose The aim of this study was to comprehensively evaluate the accuracy and validity of AI-based imaging data for predicting distant metastasis in colorectal cancer patients. Methods A systematic literature search was conducted to find relevant studies published up to January, 2024, in different databases. The quality of articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The predictive value of AI-based imaging data for distant metastasis in colorectal cancer patients was assessed using pooled sensitivity, specificity. To explore the reasons for heterogeneity, subgroup analyses were performed using different covariates. Results Seventeen studies were included in the systematic evaluation. The pooled sensitivity, specificity, and AUC of AI-based imaging data for predicting distant metastasis in colorectal cancer patients were 0.86, 0.82, and 0.91. Based on QUADAS-2, risk of bias was detected in patient selection, diagnostic tests to be evaluated, and gold standard. Based on the results of subgroup analyses, found that the duration of follow-up, site of metastasis, etc. had a significant impact on the heterogeneity. Conclusion Imaging data images based on artificial intelligence algorithms have good diagnostic accuracy for predicting distant metastasis in colorectal cancer patients and have potential for clinical application. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42024516063).
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Affiliation(s)
- Lulin Chen
- Postgraduate Affairs Department, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- Department of Ultrasound, Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China
| | - Fei Xu
- Department of Ultrasound, Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China
| | - Lujiao Chen
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
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3
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Li Y, Li S, Xiao R, Li X, Yi Y, Zhang L, Zhou Y, Wan Y, Wei C, Zhong L, Yang W, Yao L. A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer. Front Oncol 2025; 15:1496820. [PMID: 39980546 PMCID: PMC11841465 DOI: 10.3389/fonc.2025.1496820] [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: 09/15/2024] [Accepted: 01/20/2025] [Indexed: 02/22/2025] Open
Abstract
Objective Accurate preoperative evaluation of rectal cancer lung metastases (RCLM) is critical for implementing precise medicine. While artificial intelligence (AI) methods have been successful in detecting liver and lymph node metastases using magnetic resonance (MR) images, research on lung metastases is still limited. Utilizing MR images to classify RCLM could potentially reduce ionizing radiation exposure and the costs associated with chest CT in patients without metastases. This study aims to develop and validate a transformer-based deep learning (DL) model based on pelvic MR images, integrated with clinical features, to predict RCLM. Methods A total of 819 patients with histologically confirmed rectal cancer who underwent preoperative pelvis MRI and carcinoembryonic antigen (CEA) tests were enrolled. Six state-of-the-art DL methods (Resnet18, EfficientNetb0, MobileNet, ShuffleNet, DenseNet, and our transformer-based model) were trained and tested on T2WI and DWI to predict RCLM. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. Results Our transformer-based DL model achieved impressive results in the independent test set, with an AUC of 83.74% (95% CI, 72.60%-92.83%), a sensitivity of 80.00%, a specificity of 78.79%, and an accuracy of 79.01%. Specifically, for stage T4 and N2 rectal cancer cases, the model achieved AUCs of 96.67% (95% CI, 87.14%-100%, 93.33% sensitivity, 89.04% specificity, 94.74% accuracy), and 96.83% (95% CI, 88.67%-100%, 100% sensitivity, 83.33% specificity, 88.00% accuracy) respectively, in predicting RCLM. Our DL model showed a better predictive performance than other state-of-the-art DL methods. Conclusion The superior performance demonstrates the potential of our work for predicting RCLM, suggesting its potential assistance in personalized treatment and follow-up plans.
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Affiliation(s)
- Yin Li
- Department of Information, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Information, The Sixth Affiliated Hospital, Sun Yat-sen University Yuexi Hospital, Maoming, China
| | - Shuang Li
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of General Practice, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ruolin Xiao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Xi Li
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University Yuexi Hospital, Maoming, China
| | - Yongju Yi
- Department of Information, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Liangyou Zhang
- Department of Information, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - You Zhou
- Department of Information, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yun Wan
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University Yuexi Hospital, Maoming, China
| | - Chenhua Wei
- Department of Information, The Sixth Affiliated Hospital, Sun Yat-sen University Yuexi Hospital, Maoming, China
| | - Liming Zhong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Lin Yao
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Information, The Sixth Affiliated Hospital, Sun Yat-sen University Yuexi Hospital, Maoming, China
- Department of General Practice, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Naemi A, Tashk A, Sorayaie Azar A, Samimi T, Tavassoli G, Bagherzadeh Mohasefi A, Nasiri Khanshan E, Heshmat Najafabad M, Tarighi V, Wiil UK, Bagherzadeh Mohasefi J, Pirnejad H, Niazkhani Z. Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review. Cancers (Basel) 2025; 17:558. [PMID: 39941923 PMCID: PMC11817159 DOI: 10.3390/cancers17030558] [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: 12/10/2024] [Revised: 01/18/2025] [Accepted: 02/05/2025] [Indexed: 02/16/2025] Open
Abstract
BACKGROUND/OBJECTIVES This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers. METHODS The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers. RESULTS forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool. CONCLUSIONS AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.
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Affiliation(s)
- Amin Naemi
- Nordcee, Department of Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Ashkan Tashk
- Cognitive Systems, DTU Compute, The Technical University of Denmark (DTU), 2800 Copenhagen, Denmark;
| | - Amir Sorayaie Azar
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Tahereh Samimi
- Student Research Committee, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Department of Medical Informatics, Urmia University of Medical Sciences, Urmia 1138, Iran
| | - Ghanbar Tavassoli
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 969, Iran;
| | - Anita Bagherzadeh Mohasefi
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Elaheh Nasiri Khanshan
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Mehrdad Heshmat Najafabad
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Vafa Tarighi
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Uffe Kock Wiil
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
| | - Jamshid Bagherzadeh Mohasefi
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Habibollah Pirnejad
- Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Department of Family Medicine, Amsterdam University Medical Center, 7057 Amsterdam, The Netherlands
| | - Zahra Niazkhani
- Nephrology and Kidney Transplant Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Erasmus School of Health Policy and Management (ESHPM), Erasmus University Rotterdam, 3000 Rotterdam, The Netherlands
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Zheng J, Wang T, Wang H, Yan B, Lai J, Qiu K, Zhou X, Tan J, Wang S, Ji H, Feng M, Jiang W, Wang H, Yan J. Use of a Pathomics Nomogram to Predict Postoperative Liver Metastasis in Patients with Stage III Colorectal Cancer. Ann Surg Oncol 2024:10.1245/s10434-024-16519-8. [PMID: 39614006 DOI: 10.1245/s10434-024-16519-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Accepted: 10/30/2024] [Indexed: 12/01/2024]
Abstract
BACKGROUND Approximately 25% of patients with stage III colorectal cancer experience liver metastasis after radical resection; however, there is currently a lack of methods to predict liver metastasis. This study aims to develop and validate a pathomics nomogram to predict liver metastasis in patients with stage III colorectal cancer. METHODS A total of 318 enrolled patients were divided into three cohorts: a training cohort (n = 139), a validation cohort (n = 69), and an external cohort (n = 110). A competitive risk nomogram was established by the pathomics signature and clinicopathological characteristics and assessed by calibration, discrimination, and clinical usefulness. RESULTS A significant correlation between the pathomics signature and liver metastasis in stage III colorectal cancer was found. Multivariate Fine-Gray analysis indicated that preoperative carcinoembryonic antigen level, postoperative chemotherapy, and pathomics signature were independent predictors of liver metastasis. A competitive risk nomogram was developed to predict liver metastasis in patients with stage III colorectal cancer. The predicting nomogram shows good discrimination and calibration, with C-indexes of 0.811 (95% confidence interval [CI] 0.651-0.971), 0.759 (95% CI 0.531-0.987), and 0.845 (95% CI 0.641-0.999), with area under the receiver operating characteristic (AUROC) curves at 5 years of 0.833 (95% CI 0.742-0.925), 0.760 (95% CI 0.652-0.893), and 0.812 (95% CI 0.692-0.931) in the training, validation, and external cohorts, respectively. Compared with the clinicopathological nomogram, the nomogram combined with the pathomics signature had better performance (AUROC 0.823 [95% CI 0.764-0.881] vs. 0.678 [95% CI 0.606-0.751]; p < 0.001). CONCLUSIONS The pathomics signature is a predictive indicator for liver metastasis in patients with stage III colorectal cancer, and the integrated nomogram can be used to predict liver metastasis better than the clinicopathological nomogram alone.
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Affiliation(s)
- Jixiang Zheng
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Ting Wang
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Huaiming Wang
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou, People's Republic of China
| | - Botao Yan
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Jianbo Lai
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Kemao Qiu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Xinyi Zhou
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Jie Tan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Shijie Wang
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Hongli Ji
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Mingyuan Feng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Wei Jiang
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China.
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
| | - Hui Wang
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou, People's Republic of China.
| | - Jun Yan
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China.
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
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Ma J, Nie X, Kong X, Xiao L, Liu H, Shi S, Wu Y, Li N, Hu L, Li X. MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer. BMC Med Imaging 2024; 24:262. [PMID: 39367333 PMCID: PMC11453062 DOI: 10.1186/s12880-024-01439-6] [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: 05/31/2024] [Accepted: 09/24/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. METHODS A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. RESULTS KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. CONCLUSIONS Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. CLINICAL TRIAL NUMBER Not applicable.
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Affiliation(s)
- Jiaqi Ma
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Xinsheng Nie
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Xiangjiang Kong
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Lingqing Xiao
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Han Liu
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Shengming Shi
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Yupeng Wu
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Na Li
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Linlin Hu
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Xiaofu Li
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China.
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Luo X, Deng H, Xie F, Wang L, Liang J, Zhu X, Li T, Tang X, Liang W, Xiang Z, He J. Prognostication of colorectal cancer liver metastasis by CE-based radiomics and machine learning. Transl Oncol 2024; 47:101997. [PMID: 38889522 PMCID: PMC11231534 DOI: 10.1016/j.tranon.2024.101997] [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: 01/07/2024] [Revised: 04/28/2024] [Accepted: 05/11/2024] [Indexed: 06/20/2024] Open
Abstract
The liver is the most common organ for the formation of colorectal cancer metastasis. Non-invasive prognostication of colorectal cancer liver metastasis (CRLM) may better inform clinicians for decision-making. Contrast-enhanced computed tomography images of 180 CRLM cases were included in the final analyses. Radiomics features, including shape, first-order, wavelet, and texture, were extracted with Pyradiomics, followed by feature engineering by penalized Cox regression. Radiomics signatures were constructed for disease-free survival (DFS) by both elastic net (EN) and random survival forest (RSF) algorithms. The prognostic potential of the radiomics signatures was demonstrated by Kaplan-Meier curves and multivariate Cox regression. 11 radiomics features were selected for prognostic modelling for the EN algorithm, with 835 features for the RSF algorithm. Survival heatmap indicates a negative correlation between EN or RSF risk scores and DFS. Radiomics signature by EN algorithm successfully separates DFS of high-risk and low-risk cases in the training dataset (log-rank test: p < 0.01, hazard ratio: 1.45 (1.07-1.96), p < 0.01) and test dataset (hazard ratio: 1.89 (1.17-3.04), p < 0.05). RSF algorithm shows a better prognostic implication potential for DFS in the training dataset (log-rank test: p < 0.001, hazard ratio: 2.54 (1.80-3.61), p < 0.0001) and test dataset (log-rank test: p < 0.05, hazard ratio: 1.84 (1.15-2.96), p < 0.05). Radiomics features have the potential for the prediction of DFS in CRLM cases.
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Affiliation(s)
- Xijun Luo
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Hui Deng
- Department of Gastroenterology, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Fei Xie
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Liyan Wang
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Junjie Liang
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Xianjun Zhu
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Tao Li
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Xingkui Tang
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Weixiong Liang
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China
| | - Zhiming Xiang
- Department of Radiology, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China.
| | - Jialin He
- Department of General Surgery, The Affiliated Panyu Central Hospital of Guangzhou Medical University, 8 East Fuyu Road Qiaonan Street, Panyu District, Guangzhou, 511400, China.
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Seow-En I, Koh YX, Zhao Y, Ang BH, Tan IEH, Chok AY, Tan EJKW, Au MKH. Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature. Ann Hepatobiliary Pancreat Surg 2024; 28:14-24. [PMID: 38129965 PMCID: PMC10896689 DOI: 10.14701/ahbps.23-078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/16/2023] [Indexed: 12/23/2023] Open
Abstract
This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.
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Affiliation(s)
- Isaac Seow-En
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
| | - Ye Xin Koh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore
- Liver Transplant Service, SingHealth Duke-National University of Singapore Transplant Centre, Singapore
| | - Yun Zhao
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
- Group Finance Analytics, Singapore Health Services, Singapore
| | - Boon Hwee Ang
- Group Finance Analytics, Singapore Health Services, Singapore
| | | | - Aik Yong Chok
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
| | - Emile John Kwong Wei Tan
- Department of Colorectal Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore
| | - Marianne Kit Har Au
- Group Finance Analytics, Singapore Health Services, Singapore
- Finance, SingHealth Community Hospitals, Singapore
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9
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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10
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Avella P, Cappuccio M, Cappuccio T, Rotondo M, Fumarulo D, Guerra G, Sciaudone G, Santone A, Cammilleri F, Bianco P, Brunese MC. Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives. Life (Basel) 2023; 13:2027. [PMID: 37895409 PMCID: PMC10608483 DOI: 10.3390/life13102027] [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: 09/07/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM. METHODS A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened. RESULTS We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT). CONCLUSIONS Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.
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Affiliation(s)
- Pasquale Avella
- HPB Surgery Unit, Pineta Grande Hospital, Castel Volturno, 81030 Caserta, Italy;
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
| | - Micaela Cappuccio
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
| | - Teresa Cappuccio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Marco Rotondo
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Daniela Fumarulo
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Germano Guerra
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Guido Sciaudone
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Antonella Santone
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | | | - Paolo Bianco
- HPB Surgery Unit, Pineta Grande Hospital, Castel Volturno, 81030 Caserta, Italy;
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
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11
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Radiya K, Joakimsen HL, Mikalsen KØ, Aahlin EK, Lindsetmo RO, Mortensen KE. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. Eur Radiol 2023; 33:6689-6717. [PMID: 37171491 PMCID: PMC10511359 DOI: 10.1007/s00330-023-09609-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? METHODS A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. RESULTS One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. CONCLUSION Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. KEY POINTS • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.
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Affiliation(s)
- Keyur Radiya
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway.
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.
| | - Henrik Lykke Joakimsen
- Institute of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
| | - Karl Øyvind Mikalsen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
- UiT Machine Learning Group, Department of Physics and Technology, UiT the Arctic University of Norway, Tromso, Norway
| | - Eirik Kjus Aahlin
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
| | - Rolv-Ole Lindsetmo
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Head Clinic of Surgery, Oncology and Women Health, University Hospital of North Norway, Tromso, Norway
| | - Kim Erlend Mortensen
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
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12
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Spinelli A, Carrano FM, Laino ME, Andreozzi M, Koleth G, Hassan C, Repici A, Chand M, Savevski V, Pellino G. Artificial intelligence in colorectal surgery: an AI-powered systematic review. Tech Coloproctol 2023; 27:615-629. [PMID: 36805890 DOI: 10.1007/s10151-023-02772-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/07/2023] [Indexed: 02/23/2023]
Abstract
Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.
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Affiliation(s)
- A Spinelli
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy.
| | - F M Carrano
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M E Laino
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Andreozzi
- Department of Clinical Medicine and Surgery, University "Federico II" of Naples, Naples, Italy
| | - G Koleth
- Department of Gastroenterology and Hepatology, Hospital Selayang, Selangor, Malaysia
| | - C Hassan
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - A Repici
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Chand
- Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
| | - V Savevski
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - G Pellino
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
- Colorectal Surgery, Vall d'Hebron University Hospital, Universitat Autonoma de Barcelona UAB, Barcelona, Spain
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13
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Fu X, Sahai E, Wilkins A. Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response. J Pathol 2023; 260:578-591. [PMID: 37551703 PMCID: PMC10952145 DOI: 10.1002/path.6153] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 08/09/2023]
Abstract
In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Xiao Fu
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
| | - Erik Sahai
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
| | - Anna Wilkins
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Division of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
- Royal Marsden Hospitals NHS TrustLondonUK
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14
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Li ZF, Kang LQ, Liu FH, Zhao M, Guo SY, Lu S, Quan S. Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis. Abdom Radiol (NY) 2023; 48:833-843. [PMID: 36529807 DOI: 10.1007/s00261-022-03773-1] [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: 09/29/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE At present, there are few effective method to predict metachronous liver metastasis (MLM) from rectal cancer. We aim to investigate the efficacy of radiomics based on multiparametric MRI of first diagnosed rectal cancer in predicting MLM from rectal cancer. METHODS From 301 consecutive histopathologically confirmed rectal cancer patients, 130 patients who have no distant metastasis detected at the time of diagnosis were enrolled and divided into MLM group (n = 49) and non-MLM group (n = 81) according to whether liver metastasis be detected later than 6 month after the first diagnosis of rectal cancer within 3 years' follow-up. The 130 patients were divided into a training set (n = 91) and a testing set (n = 39) at a ratio of 7:3 by stratified sampling using SPSS 24.0 software. The DWI model, HD T2WI model, and DWI + HD T2WI model were constructed respectively. The best performing model was selected and combined with the screened clinical features (including non-radiomics MRI features) to construct a fusion model. The testing set was used to evaluate the performance of the models, and the area under the curve (AUC) of receiver operating characteristics (ROC) was calculated for both the training set and the testing set. RESULTS The AUC of the DWI + HD T2WI model in the testing set was higher than that of the DWI or the HD T2 model alone with statistically significance (P < 0.05). The screened clinical features were extramural vascular invasion (EMVI), T and N stages in MRI (mrT, mrN), and the distance from the lower edge of the tumor to the anal verge. The AUC of the fusion model in the testing set was 0.911. Decision curves and nomogram also showed that the fusion model had excellent clinical performance. CONCLUSION The fusion model of primary rectal cancer MRI based radiomics combing clinical features can effectively predict MLM from rectal cancer, which may assist clinicians in formulating individualized monitoring and treatment plans.
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Affiliation(s)
- Zhuo-Fu Li
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Li-Qing Kang
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China.
| | - Feng-Hai Liu
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Meng Zhao
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Su-Yin Guo
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Shan Lu
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Shuai Quan
- GE HealthCare China (Shanghai), Shanghai, 210000, China
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15
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Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023; 13:149-161. [PMID: 36647407 PMCID: PMC9840075 DOI: 10.1016/j.jceh.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.
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Key Words
- ACLF, acute on chronic liver failure
- AI, artificial intelligence
- ALD, alcoholic liver disease
- ALT, alanine transaminase
- ANN, artificial neural network
- AST, aspartate aminotransferase
- AUD, alcohol use disorder
- CHB, chronic hepatitis B
- CHC, chronic hepatitis C
- CLD, chronic liver disease
- CNN, convolutional neural network
- DL, deep learning
- FIB-4, fibrosis-4 score
- GGTP, gamma glutamyl transferase
- HCC, hepatocellular carcinoma
- HDL, high density lipoprotein
- ML, machine learning
- MLR, multi-nomial logistic regressions
- NAFLD
- NAFLD, non-alcoholic fatty liver disease
- NASH, non-alcoholic steatohepatitis
- NLP, natural language processing
- RF, random forest
- RTE, real-time tissue elastography
- SOLs, space-occupying lesions
- SVM, support vector machine
- artificial intelligence
- deep learning
- hepatology
- machine learning
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Affiliation(s)
- Rakesh Kalapala
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | - Hardik Rughwani
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
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Guan X, Lu N, Zhang J. Accurate preoperative staging and HER2 status prediction of gastric cancer by the deep learning system based on enhanced computed tomography. Front Oncol 2022; 12:950185. [PMID: 36452488 PMCID: PMC9702985 DOI: 10.3389/fonc.2022.950185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/24/2022] [Indexed: 10/24/2023] Open
Abstract
Purpose To construct the deep learning system (DLS) based on enhanced computed tomography (CT) images for preoperative prediction of staging and human epidermal growth factor receptor 2 (HER2) status in gastric cancer patients. Methods The raw enhanced CT image dataset consisted of CT images of 389 patients in the retrospective cohort, The Cancer Imaging Archive (TCIA) cohort, and the prospective cohort. DLS was developed by transfer learning for tumor detection, staging, and HER2 status prediction. The pre-trained Yolov5, EfficientNet, EfficientNetV2, Vision Transformer (VIT), and Swin Transformer (SWT) were studied. The tumor detection and staging dataset consisted of 4860 enhanced CT images and annotated tumor bounding boxes. The HER2 state prediction dataset consisted of 38900 enhanced CT images. Results The DetectionNet based on Yolov5 realized tumor detection and staging and achieved a mean Average Precision (IoU=0.5) (mAP_0.5) of 0.909 in the external validation cohort. The VIT-based PredictionNet performed optimally in HER2 status prediction with the area under the receiver operating characteristics curve (AUC) of 0.9721 and 0.9995 in the TCIA cohort and prospective cohort, respectively. DLS included DetectionNet and PredictionNet had shown excellent performance in CT image interpretation. Conclusion This study developed the enhanced CT-based DLS to preoperatively predict the stage and HER2 status of gastric cancer patients, which will help in choosing the appropriate treatment to improve the survival of gastric cancer patients.
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Affiliation(s)
| | | | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Liang M, Ma X, Wang L, Li D, Wang S, Zhang H, Zhao X. Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery. Cancer Imaging 2022; 22:50. [PMID: 36089623 PMCID: PMC9465956 DOI: 10.1186/s40644-022-00485-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background To develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery. Methods This study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value. Results In total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76–0.93), clinical model 0.65 (95% CI, 0.55–0.75), combined model 0.85 (95% CI, 0.77–0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70–0.98), clinical model 0.58 (95% CI, 0.40–0.76), combined model 0.85 (95% CI, 0.71–0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale. Conclusions The radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00485-z.
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Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:9322196. [PMID: 35360262 PMCID: PMC8958061 DOI: 10.1155/2022/9322196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022]
Abstract
This study was aimed at exploring the application of image segmentation based on full convolutional neural network (FCN) in liver computed tomography (CT) image segmentation and analyzing the clinical features of acute liver injury caused by sepsis. The Sigmoid function, encoder-decoder, and weighted cross entropy loss function were introduced and optimized based on FCN. The Dice value, precision, recall rate, volume overlap error (VOE), relative volume difference (RVD), and root mean square error (RMSE) values of the optimized algorithms were compared and analyzed. 92 patients with sepsis were selected as the research objects, and they were divided into a nonacute liver injury group (50 cases) and acute liver injury group (42 cases) based on whether they had acute liver injury. The differences in the proportion of patients with different disease histories, the proportion of patients with different infection sites, the number of organ failure, and the time of admission to intensive care unit (ICU) were compared between the two groups. It was found that the optimized window CT image Dice value after preprocessing (0.704 ± 0.06) was significantly higher than the other two methods (P < 0.05). The Dice value, precision, and recall rate of the optimized-FCN algorithm were (0.826 ± 0.06), (0.91 ± 0.08), and (0.88 ± 0.09), respectively, which were significantly higher than other algorithms (P < 0.05). The VOE, RVD, and RMSE values were (21.19 ± 1.97), (10.45 ± 1.02), and (0.25 ± 0.02), respectively, which were significantly lower than other algorithms (P < 0.05). The proportion of patients with a history of drinking in the nonacute liver injury group was lower than that in the acute liver injury group (P < 0.05), and the proportion of patients with a history of hypotension was greatly higher than that in the nonacute liver injury group (P < 0.01). CT images of sepsis patients with acute liver injury showed that large areas of liver parenchyma mixed with high-density hematoma, the number of organ failures, and the length of stay in ICU were significantly higher than those in the nonacute liver injury group (P < 0.05). It showed that the optimization algorithm based on FCN greatly improved the performance of CT image segmentation. Long-term drinking, low blood pressure, number of organ failures, and length of stay in ICU were all related to sepsis and acute liver injury. Conclusion in this study could provide a reference basis for the diagnosis and prognosis of acute liver injury caused by sepsis.
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Xiao C, Zhou M, Yang X, Wang H, Tang Z, Zhou Z, Tian Z, Liu Q, Li X, Jiang W, Luo J. Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images. Front Oncol 2022; 12:844067. [PMID: 35433467 PMCID: PMC9010865 DOI: 10.3389/fonc.2022.844067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/10/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesMetachronous liver metastasis (LM) significantly impacts the prognosis of stage I-III colorectal cancer (CRC) patients. An effective biomarker to predict LM after surgery is urgently needed. We aimed to develop deep learning-based models to assist in predicting LM in stage I-III CRC patients using digital pathological images.MethodsSix-hundred eleven patients were retrospectively included in the study and randomly divided into training (428 patients) and validation (183 patients) cohorts according to the 7:3 ratio. Digital HE images from training cohort patients were used to construct the LM risk score based on a 50-layer residual convolutional neural network (ResNet-50). An LM prediction model was established by multivariable Cox analysis and confirmed in the validation cohort. The performance of the integrated nomogram was assessed with respect to its calibration, discrimination, and clinical application value.ResultsPatients were divided into low- and high-LM risk score groups according to the cutoff value and significant differences were observed in the LM of the different risk score groups in the training and validation cohorts (P<0.001). Multivariable analysis revealed that the LM risk score, VELIPI, pT stage and pN stage were independent predictors of LM. Then, the prediction model was developed and presented as a nomogram to predict the 1-, 2-, and 3-year probability of LM. The integrated nomogram achieved satisfactory discrimination, with C-indexes of 0.807 (95% CI: 0.787, 0.827) and 0.812 (95% CI: 0.773, 0.850) and AUCs of 0.840 (95% CI: 0.795, 0.885) and 0.848 (95% CI: 0.766, 0.931) in the training and validation cohorts, respectively. Favorable calibration of the nomogram was confirmed in the training and validation cohorts. Integrated discrimination improvement and net reclassification index indicated that the integrated nomogram was superior to the traditional clinicopathological model. Decision curve analysis confirmed that the nomogram has clinical application value.ConclusionsThe LM risk score based on ResNet-50 and digital HE images was significantly associated with LM. The integrated nomogram could identify stage I-III CRC patients at high risk of LM after primary colectomy, so it may serve as a potential tool to choose the appropriate treatment to improve the prognosis of stage I-III CRC patients.
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Affiliation(s)
- Chanchan Xiao
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Microbiology and Immunology, Institute of Geriatric Immunology, School of Medicine, Jinan University, Guangzhou, China
| | - Meihua Zhou
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Xihua Yang
- Department of Surgical Oncology, Chenzhou No. 1 People’s Hospital, Chenzhou, China
| | - Haoyun Wang
- Department of Microbiology and Immunology, Institute of Geriatric Immunology, School of Medicine, Jinan University, Guangzhou, China
| | - Zhen Tang
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Zheng Zhou
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Zeyu Tian
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Qi Liu
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Xiaojie Li
- Department of Pathology, Chenzhou No. 1 People’s Hospital, Chenzhou, China
| | - Wei Jiang
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Surgical Oncology, Chenzhou No. 1 People’s Hospital, Chenzhou, China
- *Correspondence: Jihui Luo, ; Wei Jiang,
| | - Jihui Luo
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
- *Correspondence: Jihui Luo, ; Wei Jiang,
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Li Y, Gong J, Shen X, Li M, Zhang H, Feng F, Tong T. Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis. Front Oncol 2022; 12:861892. [PMID: 35296011 PMCID: PMC8919043 DOI: 10.3389/fonc.2022.861892] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 02/07/2022] [Indexed: 12/18/2022] Open
Abstract
ObjectivesTo establish and validate a machine learning-based CT radiomics model to predict metachronous liver metastasis (MLM) in patients with colorectal cancer.MethodsIn total, 323 patients were retrospectively recruited from two independent institutions to develop and evaluate the CT radiomics model. Then, 1288 radiomics features were extracted to decode the imaging phenotypes of colorectal cancer on CT images. The optimal radiomics features were selected using a recursive feature elimination selector configured by a support vector machine. To reduce the bias caused by an unbalanced dataset, the synthetic minority oversampling technique was applied to resample the minority samples in the datasets. Then, both radiomics and clinical features were used to train the multilayer perceptron classifier to develop two classification models. Finally, a score-level fusion model was developed to further improve the model performance.ResultsThe area under the curve (AUC) was 0.78 ± 0.07 for the tumour feature model and 0.79 ± 0.08 for the clinical feature model. The fusion model achieved the best performance, with AUCs of 0.79 ± 0.08 and 0.72 ± 0.07 in the internal and external validation cohorts.ConclusionsRadiomics models based on baseline colorectal contrast-enhanced CT have high potential for MLM prediction. The fusion model combining radiomics and clinical features can provide valuable biomarkers to identify patients with a high risk of colorectal liver metastases.
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Affiliation(s)
- Yue Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xigang Shen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China
- *Correspondence: Feng Feng, ; Tong Tong,
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- *Correspondence: Feng Feng, ; Tong Tong,
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Rompianesi G, Pegoraro F, Ceresa CDL, Montalti R, Troisi RI. Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J Gastroenterol 2022; 28:108-122. [PMID: 35125822 PMCID: PMC8793013 DOI: 10.3748/wjg.v28.i1.108] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/12/2021] [Accepted: 12/25/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients' survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients' exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies' role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.
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Affiliation(s)
- Gianluca Rompianesi
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
| | - Francesca Pegoraro
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
| | - Carlo DL Ceresa
- Department of Hepato-Pancreato-Biliary Surgery, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9ES, United Kingdom
| | - Roberto Montalti
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Public Health, Federico II University Hospital, Naples 80125, Italy
| | - Roberto Ivan Troisi
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
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Wang Y, Ma LY, Yin XP, Gao BL. Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis. Front Oncol 2022; 11:689509. [PMID: 35070948 PMCID: PMC8776634 DOI: 10.3389/fonc.2021.689509] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.
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Affiliation(s)
| | | | - Xiao-Ping Yin
- CT-MRI Room, Affiliated Hospital of Hebei University, Baoding, China
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Bektaş M, Tuynman JB, Costa Pereira J, Burchell GL, van der Peet DL. Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review. World J Surg 2022; 46:3100-3110. [PMID: 36109367 PMCID: PMC9636121 DOI: 10.1007/s00268-022-06728-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided. METHODS Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models. RESULTS A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration. CONCLUSIONS Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.
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Affiliation(s)
- Mustafa Bektaş
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jurriaan B. Tuynman
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jaime Costa Pereira
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
| | - George L. Burchell
- Medical Library, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Donald L. van der Peet
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
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Tanabe S, Perkins EJ, Ono R, Sasaki H. Artificial intelligence in gastrointestinal diseases. Artif Intell Gastroenterol 2021; 2:69-76. [DOI: 10.35712/aig.v2.i3.69] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/09/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
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