{"title":"Clinical Outcomes for Standard of Care Machine Learning Prostate Radiotherapy Treatment Planning","authors":"","doi":"10.1016/j.ijrobp.2024.07.017","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose/Objective(s)</h3><div>Machine learning (ML) radiotherapy (RT) treatment planning has shown improved efficiency while maintaining quality. However, there has been no prospective evaluation of patient outcomes when using ML as standard-of-care for RT planning, thereby limiting the assessment of its value proposition. We hypothesized that minimal clinical differences in genitourinary (GU) and gastrointestinal (GI) toxicities exist between ML- and human-generated RT plans during prospective application.</div></div><div><h3>Materials/Methods</h3><div>We prospectively evaluated ML- and human-generated plans for curative-intent prostate RT (60 Gy in 20 fractions) in a cohort of 113 consecutive patients treated between November 2019 and June 2022. We employed a previously institutionally developed, validated, and clinically implemented dose prediction ML model functioning within a commercial RT planning system. ML planning, without any manual adjustments, was the default planning method used in all cases. Radiation oncologists either approved the ML plan or requested an alternative human-generated plan for direct comparison, and then selected the preferred plan for treatment. GU and GI toxicities with minimum follow-up of 180 days were collected for all patients. We performed a toxicity-free survival Kaplan-Meier analysis for grade 2+ GU and grade 2+ GI toxicities between ML- and human-generated plans, and comparisons were based on log-rank tests.</div></div><div><h3>Results</h3><div>In the prospective standard of care ML deployment study, radiation oncologists selected ML plans for clinical treatment in 86 cases (76%) and selected human plans in 27 cases (24%). For cases in which a human-generated plan was requested, the ML plan was selected for treatment in only one case. In terms of treatment outcomes, there were no treatment-related grade 2+ GI toxicities observed and no significant differences in toxicity-free survival were observed for GU grade 2+ toxicities between ML- and human-generated plans (<em>P</em> = 0.39).</div></div><div><h3>Conclusion</h3><div>This is the first study demonstrating that dose prediction ML planning maintains low levels of toxicity in curative-intent prostate cancer and encourages the clinical translation of this technology into practice. When appropriately validated and deployed, ML planning can retain good clinical outcomes while improving efficiencies and can be safely used as standard of care applicable to the majority of patients, with a human-in-loop strategy.</div></div>","PeriodicalId":14215,"journal":{"name":"International Journal of Radiation Oncology Biology Physics","volume":null,"pages":null},"PeriodicalIF":6.4000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Radiation Oncology Biology Physics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S036030162400779X","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose/Objective(s)
Machine learning (ML) radiotherapy (RT) treatment planning has shown improved efficiency while maintaining quality. However, there has been no prospective evaluation of patient outcomes when using ML as standard-of-care for RT planning, thereby limiting the assessment of its value proposition. We hypothesized that minimal clinical differences in genitourinary (GU) and gastrointestinal (GI) toxicities exist between ML- and human-generated RT plans during prospective application.
Materials/Methods
We prospectively evaluated ML- and human-generated plans for curative-intent prostate RT (60 Gy in 20 fractions) in a cohort of 113 consecutive patients treated between November 2019 and June 2022. We employed a previously institutionally developed, validated, and clinically implemented dose prediction ML model functioning within a commercial RT planning system. ML planning, without any manual adjustments, was the default planning method used in all cases. Radiation oncologists either approved the ML plan or requested an alternative human-generated plan for direct comparison, and then selected the preferred plan for treatment. GU and GI toxicities with minimum follow-up of 180 days were collected for all patients. We performed a toxicity-free survival Kaplan-Meier analysis for grade 2+ GU and grade 2+ GI toxicities between ML- and human-generated plans, and comparisons were based on log-rank tests.
Results
In the prospective standard of care ML deployment study, radiation oncologists selected ML plans for clinical treatment in 86 cases (76%) and selected human plans in 27 cases (24%). For cases in which a human-generated plan was requested, the ML plan was selected for treatment in only one case. In terms of treatment outcomes, there were no treatment-related grade 2+ GI toxicities observed and no significant differences in toxicity-free survival were observed for GU grade 2+ toxicities between ML- and human-generated plans (P = 0.39).
Conclusion
This is the first study demonstrating that dose prediction ML planning maintains low levels of toxicity in curative-intent prostate cancer and encourages the clinical translation of this technology into practice. When appropriately validated and deployed, ML planning can retain good clinical outcomes while improving efficiencies and can be safely used as standard of care applicable to the majority of patients, with a human-in-loop strategy.
期刊介绍:
International Journal of Radiation Oncology • Biology • Physics (IJROBP), known in the field as the Red Journal, publishes original laboratory and clinical investigations related to radiation oncology, radiation biology, medical physics, and both education and health policy as it relates to the field.
This journal has a particular interest in original contributions of the following types: prospective clinical trials, outcomes research, and large database interrogation. In addition, it seeks reports of high-impact innovations in single or combined modality treatment, tumor sensitization, normal tissue protection (including both precision avoidance and pharmacologic means), brachytherapy, particle irradiation, and cancer imaging. Technical advances related to dosimetry and conformal radiation treatment planning are of interest, as are basic science studies investigating tumor physiology and the molecular biology underlying cancer and normal tissue radiation response.