Ruby Kemna, J Michiel Zeeuw, Kirsten A Ziesemer, Mahsoem Ali, Jacqueline I Bereska, Henk Marquering, Jaap Stoker, Inez M Verpalen, Rutger-Jan Swijnenburg, Joost Huiskens, Geert Kazemier
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Therefore, this systematic review aimed to provide a contemporary overview of the current maturity status of AI models for patients with CRLM.</p><p><strong>Methods: </strong>A systematic search of the literature until November 2, 2023, was conducted in PubMed, <ext-link ext-link-type=\"uri\" xlink:href=\"http://Embase.com\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Embase.com</ext-link>, and Clarivate Analytics/Web of Science Core Collection to identify eligible studies. Studies using AI and/or radiomics for patients with CRLM were considered eligible. Data on the study aim, study design, size of dataset, country, type of AI application, level of validation and clinical implementation status (NASA technology readiness levels) were collected. Risk of bias and applicability of the individual studies were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).</p><p><strong>Results: </strong>A total of 117 studies were included. 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None of the included studies performed real-time testing, workflow integration, clinical testing, or clinical integration.</p><p><strong>Conclusion: </strong>Although a rapid increase in research describing the development of AI models for patients with CRLM has been observed in recent years, not a single AI model has been translated into clinical practice.</p>","PeriodicalId":19497,"journal":{"name":"Oncology","volume":" ","pages":"1-10"},"PeriodicalIF":1.8000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12215172/pdf/","citationCount":"0","resultStr":"{\"title\":\"From Development to Implementation: A Systematic Review on the Current Maturity Status of Artificial Intelligence Models for Patients with Colorectal Cancer Liver Metastases.\",\"authors\":\"Ruby Kemna, J Michiel Zeeuw, Kirsten A Ziesemer, Mahsoem Ali, Jacqueline I Bereska, Henk Marquering, Jaap Stoker, Inez M Verpalen, Rutger-Jan Swijnenburg, Joost Huiskens, Geert Kazemier\",\"doi\":\"10.1159/000546572\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Artificial intelligence (AI) is increasingly being researched and developed in the medical field and holds the potential to transform healthcare after successful implementation. 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Risk of bias and applicability of the individual studies were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).</p><p><strong>Results: </strong>A total of 117 studies were included. Ninety-seven studies (83%) have been published in the last 5 years. The most common study design was retrospective (96%). Thirty-five studies (30%) utilized a dataset of fewer than 50 patients with CRLM. Internal validation was performed in 63% of the studies and external validation in 17%. The remaining studies did not report validation. Half of the studies were classified as high risk of bias. 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引用次数: 0
摘要
人工智能（AI）在医疗领域的研究和开发越来越多，成功实施后具有改变医疗保健的潜力。对于结直肠癌肝转移（CRLM）患者，已经开发了许多AI模型，但缺乏这些模型在临床工作流程中的翻译知识。因此，本系统综述旨在对CRLM患者AI模型的成熟度现状进行当代概述。方法系统检索PubMed、Embase.com和Clarivate Analytics/Web of Science Core Collection中截至2023年11月2日的文献，确定符合条件的研究。使用AI和/或放射组学对CRLM患者的研究被认为是合格的。收集了有关研究目的、研究设计、数据集规模、国家、人工智能应用类型、验证水平和临床实施状态（NASA技术就绪水平）的数据。使用预测模型偏倚风险评估工具（PROBAST）评估单个研究的偏倚风险和适用性。结果共纳入117项研究。在过去五年中发表了97项研究（83%）。最常见的研究设计是回顾性的（96%）。35项研究（30%）使用了少于50例CRLM患者的数据集。63%的研究进行了内部验证，17%的研究进行了外部验证。其余的研究没有证实。一半的研究被归类为高偏倚风险。纳入的研究中没有进行实时测试、工作流程整合、临床测试或临床整合。尽管近年来描述CRLM患者AI模型发展的研究迅速增加，但没有一个AI模型被转化为临床实践。
From Development to Implementation: A Systematic Review on the Current Maturity Status of Artificial Intelligence Models for Patients with Colorectal Cancer Liver Metastases.
Introduction: Artificial intelligence (AI) is increasingly being researched and developed in the medical field and holds the potential to transform healthcare after successful implementation. For patients with colorectal cancer liver metastases (CRLM), many AI models have been developed, but knowledge about translation of these models in the clinical workflow is lacking. Therefore, this systematic review aimed to provide a contemporary overview of the current maturity status of AI models for patients with CRLM.
Methods: A systematic search of the literature until November 2, 2023, was conducted in PubMed, Embase.com, and Clarivate Analytics/Web of Science Core Collection to identify eligible studies. Studies using AI and/or radiomics for patients with CRLM were considered eligible. Data on the study aim, study design, size of dataset, country, type of AI application, level of validation and clinical implementation status (NASA technology readiness levels) were collected. Risk of bias and applicability of the individual studies were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).
Results: A total of 117 studies were included. Ninety-seven studies (83%) have been published in the last 5 years. The most common study design was retrospective (96%). Thirty-five studies (30%) utilized a dataset of fewer than 50 patients with CRLM. Internal validation was performed in 63% of the studies and external validation in 17%. The remaining studies did not report validation. Half of the studies were classified as high risk of bias. None of the included studies performed real-time testing, workflow integration, clinical testing, or clinical integration.
Conclusion: Although a rapid increase in research describing the development of AI models for patients with CRLM has been observed in recent years, not a single AI model has been translated into clinical practice.
期刊介绍：
Although laboratory and clinical cancer research need to be closely linked, observations at the basic level often remain removed from medical applications. This journal works to accelerate the translation of experimental results into the clinic, and back again into the laboratory for further investigation. The fundamental purpose of this effort is to advance clinically-relevant knowledge of cancer, and improve the outcome of prevention, diagnosis and treatment of malignant disease. The journal publishes significant clinical studies from cancer programs around the world, along with important translational laboratory findings, mini-reviews (invited and submitted) and in-depth discussions of evolving and controversial topics in the oncology arena. A unique feature of the journal is a new section which focuses on rapid peer-review and subsequent publication of short reports of phase 1 and phase 2 clinical cancer trials, with a goal of insuring that high-quality clinical cancer research quickly enters the public domain, regardless of the trial’s ultimate conclusions regarding efficacy or toxicity.
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