Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.

IF 4.5 2区医学 Q1 ONCOLOGY

Cancers Pub Date : 2025-02-06 DOI:10.3390/cancers17030558

Amin Naemi, Ashkan Tashk, Amir Sorayaie Azar, Tahereh Samimi, Ghanbar Tavassoli, Anita Bagherzadeh Mohasefi, Elaheh Nasiri Khanshan, Mehrdad Heshmat Najafabad, Vafa Tarighi, Uffe Kock Wiil, Jamshid Bagherzadeh Mohasefi, Habibollah Pirnejad, Zahra Niazkhani

{"title":"Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.","authors":"Amin Naemi, Ashkan Tashk, Amir Sorayaie Azar, Tahereh Samimi, Ghanbar Tavassoli, Anita Bagherzadeh Mohasefi, Elaheh Nasiri Khanshan, Mehrdad Heshmat Najafabad, Vafa Tarighi, Uffe Kock Wiil, Jamshid Bagherzadeh Mohasefi, Habibollah Pirnejad, Zahra Niazkhani","doi":"10.3390/cancers17030558","DOIUrl":null,"url":null,"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.","PeriodicalId":9681,"journal":{"name":"Cancers","volume":"17 3","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11817159/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancers","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/cancers17030558","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Cancers Medicine-Oncology

CiteScore

8.00

自引率

9.60%

发文量

5371

审稿时长

18.07 days

期刊介绍： Cancers (ISSN 2072-6694) is an international, peer-reviewed open access journal on oncology. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.