Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.
Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.
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DOI：
10.21037/tcr-22-1626
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发表时间：
2022-10
影响因子：
0.9
通讯作者：
中科院分区：
文献类型：
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机器学习（ML）模型越来越多地用于肿瘤研究的临床应用。然而，虽然更复杂的模型可能提供预测或预测能力的改进，但采用它们的障碍是模型可解释性的限制，其中内部工作可以被视为“黑盒子”。可解释的人工智能（XAI）框架，包括局部可解释模型不可知论解释（LIME）和SHapley加性解释（SHAP）是新颖的模型不可知论方法，旨在通过产生模型预测计算方式的定量可视化，深入了解“黑匣子”的内部工作原理。在此过程中，XAI可以将复杂的ML模型转换为易于理解的图表和可解释的规则集，从而使提供者能够直观地理解所生成的知识，从而促进这些模型在日常临床工作流程中的部署。我们对最新文献进行了全面的、非系统的回顾，以定义与模型无关的XAI框架在肿瘤学研究中的用例。检查的数据库为PubMed/MEDLINE。最后一次搜索是在2022年5月1日。在这篇综述中，我们确定了肿瘤研究中使用ML模型和XAI来提高可解释性的几个领域，包括预测、诊断、放射组学、病理学、治疗选择、放射治疗工作流程和流行病学。在这些领域中，XAI有助于确定整体模型中的特征重要性、关系和/或交互的可视化、个体预测如何产生的评估、特征选择、预测和/或预测阈值的识别以及模型的总体信心等。这些例子为将来的扩展工作提供了基础，当这种建模的复杂性被禁止时，它可以促进在临床中的采用。与模型无关的XAI框架提供了一种直观有效的描述肿瘤ML模型的方法，其应用包括预测和确定最佳治疗方案。使用这样的框架为提高对ML模型的理解提供了机会，这是在临床中采用ML模型的关键一步。
Machine learning (ML) models are increasingly being utilized in oncology research for use in the clinic. However, while more complicated models may provide improvements in predictive or prognostic power, a hurdle to their adoption are limits of model interpretability, wherein the inner workings can be perceived as a “black box”. Explainable artificial intelligence (XAI) frameworks including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are novel, model-agnostic approaches that aim to provide insight into the inner workings of the “black box” by producing quantitative visualizations of how model predictions are calculated. In doing so, XAI can transform complicated ML models into easily understandable charts and interpretable sets of rules, which can give providers with an intuitive understanding of the knowledge generated, thus facilitating the deployment of such models in routine clinical workflows. We performed a comprehensive, non-systematic review of the latest literature to define use cases of model-agnostic XAI frameworks in oncologic research. The examined database was PubMed/MEDLINE. The last search was run on May 1, 2022. In this review, we identified several fields in oncology research where ML models and XAI were utilized to improve interpretability, including prognostication, diagnosis, radiomics, pathology, treatment selection, radiation treatment workflows, and epidemiology. Within these fields, XAI facilitates determination of feature importance in the overall model, visualization of relationships and/or interactions, evaluation of how individual predictions are produced, feature selection, identification of prognostic and/or predictive thresholds, and overall confidence in the models, among other benefits. These examples provide a basis for future work to expand on, which can facilitate adoption in the clinic when the complexity of such modeling would otherwise be prohibitive. Model-agnostic XAI frameworks offer an intuitive and effective means of describing oncology ML models, with applications including prognostication and determination of optimal treatment regimens. Using such frameworks presents an opportunity to improve understanding of ML models, which is a critical step to their adoption in the clinic.
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影响因子：
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作者：
通讯作者：
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DOI：
10.1016/s1470-2045(18)30079-2
发表时间：
2018-03
期刊：
The Lancet. Oncology
影响因子：
--
作者：
de Boer SM;Powell ME;Mileshkin L;Katsaros D;Bessette P;Haie-Meder C;Ottevanger PB;Ledermann JA;Khaw P;Colombo A;Fyles A;Baron MH;Jürgenliemk-Schulz IM;Kitchener HC;Nijman HW;Wilson G;Brooks S;Carinelli S;Provencher D;Hanzen C;Lutgens LCHW;Smit VTHBM;Singh N;Do V;D'Amico R;Nout RA;Feeney A;Verhoeven-Adema KW;Putter H;Creutzberg CL;PORTEC study group
通讯作者：
PORTEC study group
影响因子：
3.5
作者：
Chen, Yunsheng;Aleman, Dionne M.;McIntosh, Chris
通讯作者：
McIntosh, Chris
影响因子：
2.4
作者：
Djulbegovic, Benjamin;Hozo, Iztok;Dale, William
通讯作者：
Dale, William
影响因子：
4.6
作者：
Alsinglawi B;Alshari O;Alorjani M;Mubin O;Alnajjar F;Novoa M;Darwish O
通讯作者：
Darwish O