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

, Leigh Conroy, Michael C. Tjong, Tim Craig, Andrew Bayley, Charles Catton, Mary Gospodarowicz, Joelle Helou, Naghmeh Isfahanian, Vickie Kong,
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Published: 3 June 2021
Nature Medicine , Volume 27, pp 999-1005; doi:10.1038/s41591-021-01359-w

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 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
Keywords: Machine learning / Radiotherapy / Software / Biomedicine / general / Cancer Research / Metabolic Diseases / Infectious Diseases / Molecular Medicine / Neurosciences

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