Journal Article

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

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

2021

Overview

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

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/114/1305 / 631/114/794 / 631/67/1059/485 / 692/700/565/485 / Acceptability / Algorithms / Artificial intelligence / Biomedical and Life Sciences / Biomedicine / Cancer Research / Cancer therapies / Care and treatment / Clinical medicine / Evaluation / Health aspects / Health services / Infectious Diseases / Innovations / Integration / Learning algorithms / Letter / Machine learning / Metabolic Diseases / Molecular Medicine / Neurosciences / Patients / Physicians / Planning / Prostate cancer / Radiation therapy / Radiotherapy / Simulation / Workflow