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Felici A, Peduzzi G, Pellungrini R, Campa D. Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types. Eur J Cancer 2025; 222:115440. [PMID: 40273730 DOI: 10.1016/j.ejca.2025.115440] [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: 03/10/2025] [Accepted: 03/25/2025] [Indexed: 04/26/2025]
Abstract
Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk prediction models have played a crucial role in epidemiology of several cancer types, they have limitations especially in the ability to process complex and multidimensional data. In contrast, artificial intelligence (AI) approaches represent a promising solution to overcome this limitation. AI techniques have the potential to identify complex patterns and relationships in data that traditional methods might overlook, making them especially useful for handling large and heterogeneous datasets analysed in cancer research. This review first examines the current state of the art of AI techniques, highlighting their differences and suitability for various data types. Then, offers a comprehensive analysis of the literature, focusing on the application of AI approaches in nineteen cancer types (bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastric cancer, gynaecological cancers, head and neck cancer, haematological cancers, kidney cancer, liver cancer, lung cancer, melanoma, ovarian cancer, pancreatic cancer, prostate cancer, thyroid cancer and overall cancer), evaluating the models, metrics, and exposure variables used. Finally, the review discusses the application of AI in the clinical practice, along with an assessment of its potential limitations and future directions.
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Affiliation(s)
- Alessio Felici
- Department of Biology, University of Pisa, Via Luca Ghini, 13, Pisa 56126, Italy
| | - Giulia Peduzzi
- Department of Biology, University of Pisa, Via Luca Ghini, 13, Pisa 56126, Italy
| | - Roberto Pellungrini
- Classe di scienze, Scuola Normale Superiore, Piazza dei Cavalieri, 7, Pisa 56126, Italy
| | - Daniele Campa
- Department of Biology, University of Pisa, Via Luca Ghini, 13, Pisa 56126, Italy.
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Tang XS, Xu CL, Li N, Zhang JQ, Tang Y. Landscape of four different stages of human gastric cancer revealed by single-cell sequencing. World J Gastrointest Oncol 2025; 17:97125. [PMID: 39958562 PMCID: PMC11756019 DOI: 10.4251/wjgo.v17.i2.97125] [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: 05/23/2024] [Revised: 10/12/2024] [Accepted: 11/08/2024] [Indexed: 01/18/2025] Open
Abstract
BACKGROUND Gastric cancer (GC) poses a substantial risk to human health due to its high prevalence and mortality rates. Nevertheless, current therapeutic strategies remain insufficient. Single-cell RNA sequencing (scRNA-seq) offers the potential to provide comprehensive insights into GC pathogenesis. AIM To explore the distribution and dynamic changes of cell populations in the GC tumor microenvironment using scRNA-seq techniques. METHODS Cancerous tissues and paracancerous tissues were obtained from patients diagnosed with GC at various stages (I, II, III, and IV). Single-cell suspensions were prepared and analyzed using scRNA-seq to examine transcriptome profiles and cell-cell interactions. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) and flow cytometry were applied for measuring the expression of cluster of differentiation (CD) 2, CD3D, CD3E, cytokeratin 19, cytokeratin 8, and epithelial cell adhesion molecules. RESULTS Transcriptome data from 73645 single cells across eight tissues of four patients were categorized into 25 distinct cell clusters, representing 10 different cell types. Variations were observed in these cell type distribution. The adjacent epithelial cells in stages II and III exhibited a degenerative trend. Additionally, the quantity of CD4 T cells and CD8 T cells were evidently elevated in cancerous tissues. Interaction analysis displayed a remarkable increase in interaction between B cells and other mast cells in stages II, III, and IV of GC. These findings were further validated through qRT-PCR and flow cytometry, demonstrating elevated T cells and declined epithelial cells within the cancerous tissues. CONCLUSION This study provides a comprehensive analysis of cell dynamics across GC stages, highlighting key interactions within the tumor microenvironment. These findings offer valuable insights for developing novel therapeutic strategies.
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Affiliation(s)
- Xu-Shan Tang
- Department of Gastroenterology, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi 830011, Xinjiang Uighur Autonomous Region, China
| | - Chun-Lei Xu
- Department of Gastroenterology, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi 830011, Xinjiang Uighur Autonomous Region, China
| | - Na Li
- Department of Gastroenterology, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi 830011, Xinjiang Uighur Autonomous Region, China
| | - Jian-Qing Zhang
- Department of Outpatient, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang Uighur Autonomous Region, China
| | - Yong Tang
- Department of Gastroenterology, Tumor Hospital Affiliated to Xinjiang Medical University, Urumqi 830011, Xinjiang Uighur Autonomous Region, China
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Zhang T, Zhang Y, Leng X. Global, regional, and national trends in gastric cancer burden: 1990-2021 and projections to 2040. Front Oncol 2024; 14:1468488. [PMID: 39726708 PMCID: PMC11669584 DOI: 10.3389/fonc.2024.1468488] [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: 07/22/2024] [Accepted: 11/29/2024] [Indexed: 12/28/2024] Open
Abstract
Background Gastric cancer (GC) is a common malignancy of the digestive system, with significant geographical variation in its disease burden. Methods This study used data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to analyze three key indicators: incidence, mortality, and disability-adjusted life years (DALYs). Initially, a detailed analysis of the GC burden was conducted from global, regional, national, gender, and age perspectives. Subsequently, the percentage change and average annual percent change (AAPC) of GC were calculated to understand the trends in disease burden. Decomposition analysis and frontier analysis were then performed. Finally, the Bayesian age-period-cohort model was used to predict the trends in age-standardized rates (ASRs) of GC up to 2040. Results In 2021, there were 1.23 million (95% UI: 1.05-1.41 million) new cases of GC globally, with 0.95 million (95% UI: 0.82-1.10million) deaths and 22.79 million (95% UI: 19.58-26.12 million) DALYs. Compared to 1990, the global ASRs of GC has declined, but new cases and deaths have increased. For females, age-standardized incidence rate, age-standardized mortality rate, and age-standardized DALYs rate were 8.6, 7.1, and 165.6 per 100,000, with AAPCs of -2.1, -2.4, and -2.6. For males, they were 20.9, 16.0, and 371.2 per 100,000, with AAPCs of -1.6, -2.1, and -2.3. ASRs fluctuated with increasing Socio-demographic Index (SDI), being higher in middle and high-middle SDI regions. Decomposition analysis indicated negative effects from epidemiological trends on GC burden, while population growth and aging had positive effects. Frontier analysis showed that middle and high-middle SDI regions had more potential for reducing ASRs. Predictions indicate a continued decline in ASRs for both genders by 2040. Conclusion Despite progress in controlling GC, the number of new cases and deaths globally is rising due to population growth and aging. This highlights the need for effective prevention and control strategies.
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Affiliation(s)
- Tao Zhang
- Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yiqun Zhang
- Department of Gynecology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
- State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing, China
- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Xiaofei Leng
- Department of Gynecology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
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Wang DQ, Xu WH, Cheng XW, Hua L, Ge XS, Liu L, Gao X. Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer. Front Immunol 2024; 15:1407632. [PMID: 38840913 PMCID: PMC11150638 DOI: 10.3389/fimmu.2024.1407632] [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: 03/27/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024] Open
Abstract
Background Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions. Materials and methods A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions. Results The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS. Conclusion A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.
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Affiliation(s)
- Dan-qi Wang
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Wen-huan Xu
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiao-wei Cheng
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Lei Hua
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiao-song Ge
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Li Liu
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiang Gao
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
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Islam W, Abdoli N, Alam TE, Jones M, Mutembei BM, Yan F, Tang Q. A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients. Diagnostics (Basel) 2024; 14:954. [PMID: 38732368 PMCID: PMC11083029 DOI: 10.3390/diagnostics14090954] [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: 01/10/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. OBJECTIVE This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients. METHODS A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method. RESULTS Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models. CONCLUSIONS This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.
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Affiliation(s)
- Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (W.I.); (N.A.)
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (W.I.); (N.A.)
| | - Tasfiq E. Alam
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Meredith Jones
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Bornface M. Mutembei
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Feng Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Qinggong Tang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
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Wang S, Bao C, Pei D. Application of Data Mining Technology in the Screening for Gallbladder Stones: A Cross-Sectional Retrospective Study of Chinese Adults. Yonsei Med J 2024; 65:210-216. [PMID: 38515358 PMCID: PMC10973557 DOI: 10.3349/ymj.2023.0246] [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: 06/26/2023] [Revised: 08/21/2023] [Accepted: 11/07/2023] [Indexed: 03/23/2024] Open
Abstract
PURPOSE The purpose of this study was to use data mining methods to establish a simple and reliable predictive model based on the risk factors related to gallbladder stones (GS) to assist in their diagnosis and reduce medical costs. MATERIALS AND METHODS This was a retrospective cross-sectional study. A total of 4215 participants underwent annual health examinations between January 2019 and December 2019 at the Physical Examination Center of Shengjing Hospital Affiliated to China Medical University. After rigorous data screening, the records of 2105 medical examiners were included for the construction of J48, multilayer perceptron (MLP), Bayes Net, and Naïve Bayes algorithms. A ten-fold cross-validation method was used to verify the recognition model and determine the best classification algorithm for GS. RESULTS The performance of these models was evaluated using metrics of accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve. Comparison of the F-measure for each algorithm revealed that the F-measure values for MLP and J48 (0.867 and 0.858, respectively) were not statistically significantly different (p>0.05), although they were significantly higher than the F-measure values for Bayes Net and Naïve Bayes (0.824 and 0.831, respectively; p<0.05). CONCLUSION The results of this study showed that MLP and J48 algorithms are effective at screening individuals for the risk of GS. The key attributes of data mining can further promote the prevention of GS through targeted community intervention, improve the outcome of GS, and reduce the burden on the medical system.
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Affiliation(s)
- Shuang Wang
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chenhui Bao
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dongmei Pei
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China.
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Zhou G, Lee MC, Wang X, Zhong D, Githeko AK, Yan G. Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches. Am J Trop Med Hyg 2024; 110:421-430. [PMID: 38350135 PMCID: PMC10919169 DOI: 10.4269/ajtmh.23-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/03/2023] [Indexed: 02/15/2024] Open
Abstract
Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.
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Affiliation(s)
- Guofa Zhou
- Program in Public Health, University of California, Irvine, California
| | - Ming-Chieh Lee
- Program in Public Health, University of California, Irvine, California
| | - Xiaoming Wang
- Program in Public Health, University of California, Irvine, California
| | - Daibin Zhong
- Program in Public Health, University of California, Irvine, California
| | - Andrew K. Githeko
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Guiyun Yan
- Program in Public Health, University of California, Irvine, California
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Liu Y, Wang L, Du W, Huang Y, Guo Y, Song C, Tian Z, Niu S, Xie J, Liu J, Cheng C, Shen W. Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis. Front Cell Infect Microbiol 2023; 13:1207235. [PMID: 37325512 PMCID: PMC10264693 DOI: 10.3389/fcimb.2023.1207235] [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: 04/17/2023] [Accepted: 05/10/2023] [Indexed: 06/17/2023] Open
Abstract
Background Combining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment. Methods Within the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models. Results In comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection. Conclusion The XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.
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Affiliation(s)
- Yuan Liu
- Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Lanyu Wang
- Department of Urology, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Wenyi Du
- Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Yukang Huang
- Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Yi Guo
- Department of General Practice, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chen Song
- Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Zhiqiang Tian
- Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Sen Niu
- Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Jiaheng Xie
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jinhui Liu
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Cheng
- Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Wei Shen
- Department of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, China
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Deng Y, Qin HY, Zhou YY, Liu HH, Jiang Y, Liu JP, Bao J. Artificial intelligence applications in pathological diagnosis of gastric cancer. Heliyon 2022; 8:e12431. [PMID: 36619448 PMCID: PMC9816967 DOI: 10.1016/j.heliyon.2022.e12431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/29/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
Globally, gastric cancer is the third leading cause of death from tumors. Prevention and individualized treatment are considered to be the best options for reducing the mortality rate of gastric cancer. Artificial intelligence (AI) technology has been widely used in the field of gastric cancer, including diagnosis, prognosis, and image analysis. Eligible papers were identified from PubMed and IEEE up to April 13, 2022. Through the comparison of these articles, the application status of AI technology in the diagnosis of gastric cancer was summarized, including application types, application scenarios, advantages and limitations. This review presents the current state and role of AI in the diagnosis of gastric cancer based on four aspects: 1) accurate sampling from early diagnosis (endoscopy), 2) digital pathological diagnosis, 3) molecules and genes, and 4) clinical big data analysis and prognosis prediction. AI plays a very important role in facilitating the diagnosis of gastric cancer; however, it also has shortcomings such as interpretability. The purpose of this review is to provide assistance to researchers working in this domain.
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Affiliation(s)
- Yang Deng
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hang-Yu Qin
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yan-Yan Zhou
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hong-Hong Liu
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yong Jiang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Jian-Ping Liu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Ji Bao
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China,Corresponding author.
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Huang Z, Liu W, Marzo RR, Hu Z, Wong LP, Lin Y. High-risk population's knowledge of risk factors and warning symptoms and their intention toward gastric cancer screening in Southeastern China. Front Public Health 2022; 10:974923. [PMID: 36033804 PMCID: PMC9403326 DOI: 10.3389/fpubh.2022.974923] [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: 06/21/2022] [Accepted: 07/21/2022] [Indexed: 01/25/2023] Open
Abstract
Background As the incidence of gastric cancer (GC) increases sharply in adults aged over 40 years, screening of this high-risk population is important. This study aimed to explore knowledge level of GC related risk factors and symptoms, and to identify influencing factors associated with intention toward GC screening among people aged 40 years old and above in China. Methods A cross-sectional, web-based survey was conducted among people aged 40 years old and above between October 2021 and March 2022 in Southeastern China. The participants' knowledge was assessed by a series of questions about risk factors (24-item scale) and warning symptoms (14-item scale). Results A total of 2547 complete responses were received. The mean age was 47.72 (±7.20) years and near 60% were male. Respondents had a moderate level of knowledge about risk factors and warning symptoms of GC. The total mean knowledge score was 23.9 (±9.8) out of a possible score of 38. Majority (80%) of respondents reported intention to be screened for GC in the next 5 years. The most influential predictors of screening intention were income level (OR = 2.13, 95% CI: 1.36-3.32), perceived benefits (OR = 1.99, 95% CI: 1.33-2.73), perceived severity (OR = 1.68, 95% CI: 1.20-2.34), ever took GC screening (OR = 1.63, 95% CI: 1.28-2.08), perceived poor overall health (OR = 1.59, 95% CI: 1.19-2.11), and perceived barriers (OR = 1.56, 95% CI: 1.17-2.09). Other significant factors were ever diagnosed with chronic gastric diseases, total knowledge score, and cues-to-action. The major reasons for not willing to take screening were "endoscopy is uncomfortable" (29.6%), "worry about screening results" (23.6%), and "have no symptoms" (21.3%). Conclusion High-risk population aged 40 years and above expressed high intention to receive GC screening. Intervention to improve health promotion and reduce the barriers to uptake of GC screening among high-risk populations in China is warranted.
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Affiliation(s)
- Zhiwen Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Wei Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Roy Rillera Marzo
- Department of Community Medicine, International Medical School, Management and Science University, Shah Alam, Selangor, Malaysia,Global Public Health, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Subang Jaya, Malaysia,Roy Rillera Marzo
| | - Zhijian Hu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Li Ping Wong
- Department of Social and Preventive Medicine, Faculty of Medicine, Centre for Epidemiology and Evidence-Based Practice, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yulan Lin
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China,*Correspondence: Yulan Lin
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Different Data Mining Approaches Based Medical Text Data. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1285167. [PMID: 34912530 PMCID: PMC8668297 DOI: 10.1155/2021/1285167] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/18/2021] [Indexed: 12/15/2022]
Abstract
The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.
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Li HQ, Xue H, Yuan H, Wan GY, Zhang XY. Preferences of first-degree relatives of gastric cancer patients for gastric cancer screening: a discrete choice experiment. BMC Cancer 2021; 21:959. [PMID: 34445987 PMCID: PMC8393792 DOI: 10.1186/s12885-021-08677-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 08/12/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND It is very necessary to implement gastric cancer screening in China to reduce the mortality of gastric cancer, but there are no national screening guidelines and programs. Understanding of individual preferences is conducive to formulating more acceptable screening strategies, and discrete choice experiments can quantify individual preferences. In addition, the first-degree relatives of gastric cancer patients are at high risk for gastric cancer. Compared with those without a family history of gastric cancer, the risk of gastric cancer in first-degree relatives of gastric cancer patients is increased by 60%. Therefore, a discrete choice experiment was carried out to quantitatively analyse the preferences of first-degree relatives of gastric cancer patients for gastric cancer screening to serve as a reference for the development of gastric cancer screening strategies. METHODS A questionnaire was designed based on a discrete choice experiment, and 342 first-degree relatives of gastric cancer patients were investigated. In STATA 15.0 software, the data were statistically analysed using a mixed logit model. RESULTS The five attributes included in our study had a significant influence on the preferences of first-degree relatives of gastric cancer patients for gastric cancer screening (P < 0.05). Participants most preferred the sensitivity of the screening program to be 95% (coefficient = 1.424, P < 0.01) with a willingness to pay 2501.902 Yuan (95% CI, 738.074-4265.729). In addition, the participants' sex and screening experiences affected their preferences. An increase in sensitivity 35 to 95% had the greatest impact on the participants' willingness to choose a gastric cancer screening program. CONCLUSION The formulation of gastric cancer screening strategies should be rooted in people's preferences. The influence of sex differences and screening experiences on the preferences of people undergoing screening should be considered, and screening strategies should be formulated according to local conditions to help them play a greater role.
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Affiliation(s)
- Hui-Qin Li
- Department of Fundamental Nursing, School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130021, Jilin Province, P. R. China
| | - Hui Xue
- Department of Histology & Embryology, College of Basic Medical Sciences, Jilin University, 126 Xinmin Street, Changchun, 130021, Jilin Province, P. R. China
| | - Hua Yuan
- Department of Fundamental Nursing, School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130021, Jilin Province, P. R. China
| | - Guang-Ying Wan
- Department of Fundamental Nursing, School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130021, Jilin Province, P. R. China
| | - Xiu-Ying Zhang
- Department of Fundamental Nursing, School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130021, Jilin Province, P. R. China.
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13
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Kuo CC, Wang HH, Tseng LP. Using data mining technology to predict medication-taking behaviour in women with breast cancer: A retrospective study. Nurs Open 2021; 9:2646-2656. [PMID: 34156764 PMCID: PMC9584494 DOI: 10.1002/nop2.963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 05/10/2021] [Accepted: 05/27/2021] [Indexed: 11/19/2022] Open
Abstract
Aims Medication‐taking behaviours of breast cancer survivors undergoing adjuvant hormone therapy have received considerable attention. This study aimed to determine factors affecting medication‐taking behaviours in people with breast cancer using data mining. Design A longitudinal observational retrospective cohort study with a hospital‐based survey. Methods A total of 385 subjects were surveyed, analysing existing data from January 2010 to December 2017 in Taiwan. Three data mining approaches—multiple logistic regression, decision tree and artificial neural network—were used to build the prediction models and rank the importance of influencing factors. Accuracy, specificity and sensitivity were used as assessment indicators for the prediction models. Results Multiple logistic regression was the most effective approach, achieving an accuracy of 96.37%, specificity of 96.75% and sensitivity of 96.12%. The duration of adjuvant hormone therapy discontinuation, duration of adjuvant hormone therapy use and age at diagnosis by data mining were the three most critical factors influencing the medication‐taking behaviours of people with breast cancer.
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Affiliation(s)
- Chen-Chen Kuo
- The Cancer Prevention and Treatment Center, St. Martin De Porres Hospital, Chiayi, Taiwan.,School of Nursing, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Hsiu-Hung Wang
- School of Nursing, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Li-Ping Tseng
- Management Center, St. Martin De Porres Hospital, Chiayi, Taiwan.,Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliu, Taiwan
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14
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Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/21/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN's clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
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Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
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15
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Zhan M, Chen ZB, Ding CC, Qu Q, Wang GQ, Liu S, Wen FQ. Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute lymphoblastic leukemia. Leuk Lymphoma 2021; 62:2502-2513. [PMID: 33899650 DOI: 10.1080/10428194.2021.1913140] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Methotrexate (MTX), an antimetabolite for the treatment of leukemia, could cause neutropenia and subsequently fever, which might lead to treatment delay and affect prognosis. Here, we aimed to predict neutropenia and fever related to high-dose MTX using artificial intelligence. This study included 139 pediatric patients newly diagnosed with standard- or intermediate risk B-cell acute lymphoblastic leukemia. Fifty-seven SNPs of 16 genes were genotyped. Univariate and multivariate analysis were used to select SNPs and clinical covariates for model developing. Five machine learning algorithms combined with four resampling techniques were used to build optimal predictive model. The combination of random forest with adaptive synthetic appeared to be the best model for neutropenia (sensitivity = 0.935, specificity = 0.920, AUC = 0.927) and performed best for fever (sensitivity = 0.818, specificity = 0.924, AUC = 0.870). By machine learning, we have developed and validated comprehensive models to predict the risk of neutropenia and fever. Such models may be helpful for medical oncologists in quick decision-making.
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Affiliation(s)
- Min Zhan
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Ze-Bin Chen
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Chang-Cai Ding
- Department of Research and Development, Shenzhen Advanced precision medical CO., LTD, Shenzhen, People's Republic of China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Guo-Qiang Wang
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Sixi Liu
- Department of Hematology/Oncology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Fei-Qiu Wen
- Department of Hematology/Oncology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
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16
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Yang K, Lu L, Liu H, Wang X, Gao Y, Yang L, Li Y, Su M, Jin M, Khan S. A comprehensive update on early gastric cancer: defining terms, etiology, and alarming risk factors. Expert Rev Gastroenterol Hepatol 2021; 15:255-273. [PMID: 33121300 DOI: 10.1080/17474124.2021.1845140] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Early gastric cancer (EGC) is a well-defined gastric malignancy that is limited to the mucosa or submucosa, irrespective of lymph node metastasis. At an early stage, gastric cancer often does not cause symptoms until it becomes advanced, and it is a heterogeneous disease and usually encountered in its late stages. AREA COVERED This comprehensive review will provide a novel insight into the evaluation of EGC epidemiology, defining terms, extensive etiology and risk factors, and timely diagnosis since prevention is an essential approach for controlling this cancer and reducing its morbidity and mortality. EXPERT OPINION The causative manner of EGC is complex and multifactorial. In recent years, researchers have made significant contributions to understanding the etiology and pathogenesis of EGC, and standardization in the evaluation of disease activity. Though the incidence of this cancer is steadily declining in some advanced societies owing to appropriate interventions, there remains a serious threat to health in developing nations. Early detection of resectable gastric cancer is crucial for better patient outcomes.
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Affiliation(s)
- Kuo Yang
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Lijie Lu
- Department of Digestive Diseases, Dongfang Hospital of Beijing University of Chinese Medicine , Beijing, PR, China
| | - Huayi Liu
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Xiujuan Wang
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Ying Gao
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Liu Yang
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Yupeng Li
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Meiling Su
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Ming Jin
- Department of Digestive Diseases, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital , Tianjin, PR, China
| | - Samiullah Khan
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital , Tianjin, PR, China
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17
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Fu L, Yang Q, Liu X, Wang Z. Three-stage model based evaluation of local residents' acceptance towards waste-to-energy incineration project under construction: A Chinese perspective. WASTE MANAGEMENT (NEW YORK, N.Y.) 2021; 121:105-116. [PMID: 33360810 DOI: 10.1016/j.wasman.2020.11.039] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 10/31/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
Incineration is an efficient and economical means of solid waste disposal. And local residents' acceptance has to be acquired for the smooth going of waste-to-energy incineration project (WTEIP). From a Chinese perspective, this paper finds what influence local residents' acceptance towards WTEIP under construction, based on which we rank WTEIPs and figure out the project with the least local residents' acceptance. To achieve this, a three-stage model is developed. Stage 1 involves identifying the criteria based on the expert judgement for local residents' acceptance towards WTEIP under construction. Stage 2 involves the criteria weights determination employing Best Worst-Decision Making Trial and Evaluation Laboratory (BWD). BWD incorporates Best Worst Method and Decision Making Trial and Evaluation Laboratory which is intended to take the interrelationships among the criteria into account. Stage 3 involves project ranking according to the criteria weights determined by BWD. Sensitivity analysis is conducted to check the effectiveness and robustness of the three-stage model. Results show that perceived risk-free is the most influential criterion of local residents' acceptance towards WTEIP under construction and the three-stage model is reliable and robust. The study is helpful to enhance local residents' acceptance towards WTEIP under construction and provide important reference for decision-makers and policymakers in waste management.
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Affiliation(s)
- Lingmei Fu
- School of Management, Wuhan University of Technology, Wuhan 430070, China.
| | - Qing Yang
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, China.
| | - Xingxing Liu
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, China.
| | - Zhan Wang
- School of Management, Wuhan University of Technology, Wuhan 430070, China.
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18
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Yu C, Helwig EJ. Artificial intelligence in gastric cancer: a translational narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:269. [PMID: 33708896 PMCID: PMC7940908 DOI: 10.21037/atm-20-6337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application. There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies. It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.
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Affiliation(s)
- Chaoran Yu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
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19
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Zhu SL, Dong J, Zhang C, Huang YB, Pan W. Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics. PLoS One 2020; 15:e0244869. [PMID: 33382829 PMCID: PMC7775073 DOI: 10.1371/journal.pone.0244869] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 12/17/2020] [Indexed: 12/24/2022] Open
Abstract
Background The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics. Aims To construct a predictive model for the diagnosis of gastric cancer with high accuracy based on noninvasive characteristics. Methods A retrospective study of 709 patients at Zhejiang Provincial People's Hospital was conducted. Variables of age, gender, blood cell count, liver function, kidney function, blood lipids, tumor markers and pathological results were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the accuracy of the model. Results Of the 709 patients, 398 were diagnosed with gastric cancer; 311 were health people or diagnosed with benign gastric disease. Multivariate analysis showed that gender, age, neutrophil lymphocyte ratio, hemoglobin, albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) were independent characteristics associated with gastric cancer. We constructed a predictive model using GBDT, and the area under the receiver operating characteristic curve (AUC) of the model was 91%. For the test dataset, sensitivity was 87.0% and specificity 84.1% at the optimal threshold value of 0.56. The overall accuracy was 83.0%. Positive and negative predictive values were 83.0% and 87.8%, respectively. Conclusion We construct a predictive model to diagnose gastric cancer with high sensitivity and specificity. The model is noninvasive and may reduce the medical cost.
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Affiliation(s)
- Shuang-Li Zhu
- Department of Geriatric VIP NO.1, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China
| | - Jie Dong
- Department of Gastroenterology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China
| | - Chenjing Zhang
- Department of Gastroenterology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China
| | - Yao-Bo Huang
- Department of Financial Security, Alibaba Group, Hangzhou, Zhejiang Province, China
| | - Wensheng Pan
- Department of Gastroenterology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China
- * E-mail:
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20
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Wu J, Qin S, Wang J, Li J, Wang H, Li H, Chen Z, Li C, Wang J, Yuan J. Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers. Front Bioeng Biotechnol 2020; 8:839. [PMID: 33014993 PMCID: PMC7513671 DOI: 10.3389/fbioe.2020.00839] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 06/30/2020] [Indexed: 11/13/2022] Open
Abstract
The convolutional neural network (CNN) has made certain progress in image processing, language processing, medical information processing and other aspects, and there are few relevant researches on its application in disease risk prediction. Dyslipidemia is a major and modifiable risk factor for cardiovascular disease, early detection of dyslipidemia and early intervention can effectively reduce the occurrence of cardiovascular diseases. Risk prediction model can effectively identify high-risk groups and is widely used in public health and clinical medicine. Steel workers are a special occupational group. Their particular occupational hazards, such as high temperatures, noise and shift work, make them more susceptible to disease than the general population, which makes the risk prediction model for the general population no longer applicable to steel workers. Therefore, it is necessary to establish a new model dedicated to the prediction of dyslipidemia of steel workers. In this study, the physical examination information of thousands of steel workers was collected, and the risk factors of dyslipidemia in steel workers were screened out. Then, based on the data characteristics, the corresponding parameters were set for the convolutional neural network model, and the risk of dyslipidemia in steel workers was predicted by using convolutional neural network. Finally, the predictive performance of the convolutional neural network model is compared with the existing predictive models of dyslipidemia, logistics regression model and BP neural network model. The results show that the convolutional neural network has a good predictive performance in the risk prediction of dyslipidemia of steel workers, and is superior to the Logistic regression model and BP neural network model.
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Affiliation(s)
- Jianhui Wu
- School of Public Health, North China University of Science and Technology, Tangshan, China.,Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, Tangshan, China
| | - Sheng Qin
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Jie Wang
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Jing Li
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Han Wang
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Huiyuan Li
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Zhe Chen
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Chao Li
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Jiaojiao Wang
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Juxiang Yuan
- School of Public Health, North China University of Science and Technology, Tangshan, China.,Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, Tangshan, China
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21
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Application of artificial intelligence in the diagnosis and prediction of gastric cancer. Artif Intell Gastroenterol 2020. [DOI: 10.35712/wjg.v1.i1.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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22
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Qie YY, Xue XF, Wang XG, Dang SC. Application of artificial intelligence in the diagnosis and prediction of gastric cancer. Artif Intell Gastroenterol 2020; 1:12-18. [DOI: 10.35712/aig.v1.i1.12] [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: 06/12/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer is the second leading cause of cancer deaths worldwide. Despite the great progress in the diagnosis and treatment of gastric cancer, the incidence and mortality rate of the disease in China are still relatively high. The high mortality rate of gastric cancer may be related to its low early diagnosis rate and poor prognosis. Much research has been focused on improving the sensitivity and specificity of diagnostic tools for gastric cancer, in order to more accurately predict the survival times of gastric cancer patients. Taking appropriate treatment measures is the key to reducing the mortality rate of gastric cancer. In the past decade, artificial intelligence technology has been applied to various fields of medicine as a branch of computer science. This article discusses the application and research status of artificial intelligence in gastric cancer diagnosis and survival prediction.
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Affiliation(s)
- Yin-Yin Qie
- Department of General Surgery, The Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
| | - Xiao-Fei Xue
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Xiao-Gang Wang
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Sheng-Chun Dang
- Department of General Surgery, the Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
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Zhang ZG, Xu L, Zhang PJ, Han L. Evaluation of the value of multiparameter combined analysis of serum markers in the early diagnosis of gastric cancer. World J Gastrointest Oncol 2020; 12:483-491. [PMID: 32368325 PMCID: PMC7191329 DOI: 10.4251/wjgo.v12.i4.483] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 02/05/2020] [Accepted: 03/22/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND In early gastric cancer (GC), tumor markers are increased in the blood. The levels of these markers have been used as important indexes for GC screening, early diagnosis and prognostic evaluation. However, specific tumor markers have not yet been discovered. Diagnosis based on a single tumor marker has limited significance. The detection rate of GC is still very low.
AIM To improve the diagnostic value of blood markers for GC.
METHODS We used a multiparameter joint analysis of 77 indexes of malignant GC and gastric polyp (GP), 64 indexes of GC and healthy controls (Ctrls).
RESULTS By analyzing the data, there are 27 indexes in the final Ctrls vs GC with P values < 0.01, the area under the curve (AUC) of albumin is the largest in Ctrls vs GC, and the AUC was 0.907. 30 indexes in GP vs GC have P values < 0.01. Among them, the D-dimer showed an AUC of 0.729. The 27 indexes in Ctrls vs GC and 30 indexes in GP vs GC were used for binary logistic regression, discriminant analysis, classification tree analysis and artificial neural network analysis model. For the ability to distinguish between Ctrls vs GC, GP vs GC, artificial neural networks had better diagnostic value when compared with classification tree, binary logistic regression, and discriminant analysis. When compared Ctrl and GC, the overall prediction accuracy was 92.9%, and the AUC was 0.992 (0.980, 1.000). When compared GP and GC, the overall prediction accuracy was 77.9%, and the AUC was 0.969 (0.948, 0.990).
CONCLUSION The diagnostic effect of multi-parameter joint artificial neural networks analysis is significantly better than the single-index test diagnosis, and it may provide an assistant method for the detection of GC.
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Affiliation(s)
- Zhi-Guo Zhang
- Department of Oncology, Beijing Daxing District People’s Hospital, Beijing 102600, China
| | - Liang Xu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Interventional Therapy Department, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Peng-Jun Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Interventional Therapy Department, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Lei Han
- Department of Oncology, Beijing Daxing District People’s Hospital, Beijing 102600, China
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Preoperative imaging evaluation of the absolute indication criteria for endoscopic submucosal dissection in early gastric cancer patients. Wideochir Inne Tech Maloinwazyjne 2020; 16:45-53. [PMID: 33786116 PMCID: PMC7991940 DOI: 10.5114/wiitm.2020.94270] [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: 12/23/2019] [Accepted: 02/24/2020] [Indexed: 11/18/2022] Open
Abstract
Introduction Gastric cancer (GC) is a common malignant tumor with a high mortality rate. Aim To determine the accuracy of preoperative imaging information obtained from the combined use of general gastroscopy (GS), endoscopic ultrasonography (EUS), and multi-detector computed tomography (MDCT) regarding absolute indication of endoscopic submucosal dissection (ESD) in early gastric cancer (EGC). Material and methods The relationship between clinical features of 794 EGC patients and lymph node metastasis (LNM) was analyzed. Multivariate logistic regression analysis was used to investigate the risk factors for LNM. Additionally, the accuracy of diagnosis of imaging techniques for ESD indications was determined by receiver operating characteristic (ROC) analysis. Results Data showed that tumor size > 2 cm (p = 0.0071), T1b stage (p < 0.0001), undifferentiated histology (p < 0.0001), and vascular invasion (p = 0.0007) were independent risk factors for LNM in patients with EGC. Indications for ESD have a specificity of 100% for the diagnosis of patients with LNM. Additionally, the diagnostic efficacy of the use of GS, EUS, and MDCT in identifying node positive status, T1a disease, tumor size ≤ 2 cm, and ulceration was found to be moderate with area under the curve (AUC) of receiver operating characteristic curve (ROC) of 0.71, 0.64, 0.72, and 0.68, respectively. Furthermore, the use of imaging techniques for overall indication criteria for ESD had a moderate utility value with an AUC of 0.71. Conclusions Our data suggested that, based on the combined use of GS, EUS, and MDCT, a high specificity of patient selection for ESD treatment can be achieved.
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