[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126167-en":3,"doc-seo-126167-105":31,"detail-sidebar-cat-0-en-105":97},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126167,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Methodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis","Evaluates the quality and transparency of reporting in machine learning (ML) oncology studies, emphasizing adherence to CREMLS, TRIPOD-AI, and bias assessment with PROBAST. Reviews primary studies developing or testing ML models for cancer diagnosis, treatment, or prognosis published from Feb 1, 2024 to Jan 31, 2025, selecting the fifteen most recent articles per category. Two reviewers extract reporting quality, risk of bias, and ML performance metrics. Finds reporting deficiencies related to sample size calculation, data quality, outlier handling, predictor documentation, access to training/validation data, and performance heterogeneity.","TYPE Review  \nPUBLISHED 14 April 2025  \nDOI 10.3389/fonc.2025.1555247  \nOPEN ACCESS  \nEDITED BY  \nSharon R. Pine,  \nUniversity of Colorado Anschutz Medical Campus, United States  \nREVIEWED BY  \nDevesh U. Kapoor,  \nGujarat Technological University, India Miaomiao Yang,  \nYantai Yuhuangding Hospital, China  \n*CORRESPONDENCE  \nJoseph Finkelstein  \njoseph.fi[nkelstin@utah.edu](nkelstin@utah.edu)  \nRECEIVED 03 January 2025  \nACCEPTED 18 March 2025  \nPUBLISHED 14 April 2025  \nCITATION  \nSmiley A, Villarreal-Zegarra D, Reategui-Rivera CM, Escobar-Agreda Sand Finkelstein J (2025) Methodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis.  \nFront. Oncol. 15:1555247 .  \ndoi: 10.3389/fonc.2025.1555247  \nCOPYRIGHT  \n© 2025 Smiley, Villarreal-Zegarra, Reategui-Rivera, Escobar-Agreda and Finkelstein. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMethodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis  \nAref Smiley1, David Villarreal-Zegarra 1,  \nC. Mahony Reategui-Rivera 1, Stefan Escobar-Agreda 2 and Joseph Finkelstein 1*  \n1 Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States, 2Telehealth Unit, Universidad Nacional Mayor de San Marcos, Lima, Peru  \nThis study aimed to evaluate the quality and transparency of reporting in studies using machine learning (ML) in oncology, focusing on adherence to the Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS), TRIPOD-AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis), and PROBAST (Prediction Model Risk of Bias Assessment Tool) . The literature search included primary studies published between February 1, 2024, and January 31, 2025, that developed or tested ML models for cancer diagnosis, treatment, or prognosis. To reflect the current state of the rapidly evolving landscape of ML applications in oncology, fifteen most recent articles in each category were selected for evaluation. Two independent reviewers screened studies and extracted data on study characteristics, reporting quality (CREMLSand TRIPOD+AI), risk of bias (PROBAST), and ML performance metrics. The most frequently studied cancer types were breast cancer (n=7/45; 15.6%), lung cancer (n=7/45; 15 . 6%), and liver cancer (n=5/45; 11 . 1%) . The findings indicate several deficiencies in reporting quality, as assessed by CREMLS and TRIPOD+AI. These deficiencies primarily relate to sample size calculation, reporting on data quality, strategies for handling outliers, documentation of ML model predictors, access to training or validation data, and reporting on model performance heterogeneity. The methodological quality assessment using PROBAST revealed that 89% of the included studies exhibited a low overall risk of bias, and all studies have shown a low risk of bias in terms of applicability. Regarding the specific AI models identified as the best-performing, Random Forest (RF) and XGBoost were the most frequently reported, each used in 17.8% of the studies (n = 8) . Additionally, our study outlines the specific areas where reporting is deficient, providing researchers with guidance to improve reporting quality in these sections and, consequently, reduce the risk of bias in their studies.  \nKEYWORDS  \ncancer, artificial intelligence, diagnosis, prognosis, therapy  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nCancer is one of the leading causes of disease burden and mortality worldwide","cbCaibQdq4Py6T3X","https://ap.wps.com/l/cbCaibQdq4Py6T3X","pdf",2778244,6,1,16,"English","en",105,"# Introduction\n## Purpose and rationale\n## Reporting transparency and reproducibility concerns\n# Methods\n## Search strategy and study selection\n## Screening and data extraction\n# Findings\n## Cancer types studied\n## Reporting quality gaps (CREMLS, TRIPOD-AI)\n## Risk of bias assessment (PROBAST)\n## Frequently used AI models\n# Conclusions\n## Guidance to improve reporting and reduce bias risk","[{\"question\":\"Which reporting and bias frameworks are assessed in this review?\",\"answer\":\"The review evaluates adherence to CREMLS and TRIPOD-AI for reporting quality and uses PROBAST to assess risk of bias across study domains.\"},{\"question\":\"What time window and study types are included?\",\"answer\":\"Primary studies published from February 1, 2024 to January 31, 2025 that developed or tested machine learning models for cancer diagnosis, treatment, or prognosis are included.\"},{\"question\":\"What kinds of deficiencies are most common in ML study reporting?\",\"answer\":\"Deficiencies concentrate on sample size calculation, reporting data quality, strategies for handling outliers, documenting ML model predictors, access to training or validation data, and reporting performance heterogeneity.\"},{\"question\":\"How is methodological quality reflected in the PROBAST results?\",\"answer\":\"PROBAST indicates that 89% of included studies have a low overall risk of bias, and all studies show low risk regarding applicability.\"}]","Methodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis | 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