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

医学 前列腺癌 放射治疗 放射治疗计划 前瞻性队列研究 工作流程 内科学 临床试验 癌症 医学物理学 外科 计算机科学 数据库
作者
Chris McIntosh,Leigh Conroy,Michael C. Tjong,Tim Craig,Andrew Bayley,Charles Catton,Mary Gospodarowicz,Joelle Helou,Naghmeh Isfahanian,Victor Kong,Tony K.T. Lam,Srinivas Raman,Padraig Warde,Peter Chung,Alejandro Berlín,Thomas G. Purdie
出处
期刊:Nature Medicine [Nature Portfolio]
卷期号:27 (6): 999-1005 被引量:107
标识
DOI:10.1038/s41591-021-01359-w
摘要

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 47 h). 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. An artificial intelligence system prospectively deployed to design radiation therapy plans for patients with prostate cancer illustrates the real-world impact of machine learning in clinical practice and identifies factors influencing human–algorithm interaction
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