加拿大健康网络大学Thomas G. Purdie和Alejandro Berlin课题组合作取得最新进展。他们将机器学习 (ML)的临床整合用于前列腺癌患者的治愈性放射治疗。相关论文发表在2021年6月3日出版的《自然-医学》杂志上。

他们前瞻性地部署并评估了随机森林算法，用于在完全整合到临床工作流程的盲法、头对头研究中对前列腺癌进行治疗性放疗 (RT) 治疗计划。ML 和人工生成的 RT 治疗计划在回顾性模拟与重新测试 (n = 50) 和前瞻性临床部署 (n = 50) 阶段进行了直接比较。在整个研究阶段，治疗医师始终按照先验定义的标准化标准和同行评审过程，以盲法评估 ML 和人工生成的 RT 治疗计划，并在前瞻性阶段为患者治疗提供选定的 RT 计划。

总体而言，89% 的 ML 生成的 RT 计划被认为在临床上是可接受的，72% 的选择在头对头比较中优于人工生成的 RT 计划。使用 ML 的 RT 计划将整个 RT 计划过程所需的中位时间减少了 60.1%（118 到 47 小时）。虽然 ML RT 计划的可接受性在模拟和部署阶段之间保持稳定（92% 对 86%），但选择用于治疗的 ML RT 计划的数量显著减少（分别为 83% 和 61%）。

这些发现强调，即使在专家盲审下，对 ML 方法的回顾性或模拟评估也可能无法代表在危及患者护理的真实临床环境中的算法接受度。

研究人员表示，ML有望影响医疗保健服务；然而，迄今为止，大多数方法都是在“模拟”环境中进行测试的，这些环境无法概括影响现实世界临床实践的因素。

附：英文原文

Title: Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer

Author: Chris McIntosh, Leigh Conroy, Michael C. Tjong, Tim Craig, Andrew Bayley, Charles Catton, Mary Gospodarowicz, Joelle Helou, Naghmeh Isfahanian, Vickie Kong, Tony Lam, Srinivas Raman, Padraig Warde, Peter Chung, Alejandro Berlin, Thomas G. Purdie

Issue&Volume: 2021-06-03

Abstract: Machine learning (ML) holds great promise for impacting healthcare delivery; however, to date most methods are tested in ‘simulated’ environments that cannot recapitulate factors influencing real-world clinical practice. We prospectively deployed and evaluated a random forest algorithm for therapeutic curative-intent radiation therapy (RT) treatment planning for prostate cancer in a blinded, head-to-head study with full integration into the clinical workflow. ML- and human-generated RT treatment plans were directly compared in a retrospective simulation with retesting (n=50) and a prospective clinical deployment (n=50) phase. Consistently throughout the study phases, treating physicians assessed ML- and human-generated RT treatment plans in a blinded manner following a priori defined standardized criteria and peer review processes, with the selected RT plan in the prospective phase delivered for patient treatment. Overall, 89% of ML-generated RT plans were considered clinically acceptable and 72% were selected over human-generated RT plans in head-to-head comparisons. RT planning using ML reduced the median time required for the entire RT planning process by 60.1% (118 to 47h). While ML RT plan acceptability remained stable between the simulation and deployment phases (92 versus 86%), the number of ML RT plans selected for treatment was significantly reduced (83 versus 61%, respectively). These findings highlight that retrospective or simulated evaluation of ML methods, even under expert blinded review, may not be representative of algorithm acceptance in a real-world clinical setting when patient care is at stake.

DOI: 10.1038/s41591-021-01359-w

Source: https://www.nature.com/articles/s41591-021-01359-w