A Prospective Study of Machine Learning-Assisted Radiotherapy Planning for Patients Receiving 54 Gy to the Brain

Abstract

Radiotherapy (RT) planning is presently a semi-manual, iterative, labor-intensive process which may result in unnecessary variation in plan quality. To improve treatment plan quality and decrease RT planning time, we conducted a prospective, blinded study to compare machine learning-assisted planning with conventional manual planning for patients receiving 54 Gy in 30 fractions for a primary brain tumor. From January 31, 2022 to January 10, 2023, 40 patients receiving 54 Gy for primary CNS tumors were prospectively enrolled (median age 50 years, range 4-78 years). Patients underwent standard CT/MR simulation and target/OAR delineation by the treating radiation oncologist. Each patient had one ML plan and 1-2 manual RT plans created by different planners. The reviewing oncologist was blinded to planning method by removing optimization and IMRT/VMAT beam arrangement details from all plans, which were then rated based on clinical acceptability, target coverage, OAR sparing, conformity, and dose-fall off. One preferred plan was chosen and used for clinical treatment. A total of 115 plans for 40 patients were evaluated: 40 ML plans (35% of all plans), and 75 manual plans (65% of all plans; 5 and 35 patients had 1 and 2 manual plans created, respectively). ML plans required a mean planning time of 65 min as compared to 107 min for manual plans, with a mean time savings of 41 min per patient (paired t-test p = 0.002). 97% of ML plans (95% confidence interval [CI] 85-100) and 96% of manual plans (95% CI 87-99) were designated clinically acceptable by the treating radiation oncologist. While ML-assisted plans represented 35% of plans evaluated, they were chosen as preferred for clinical treatment in 43% of cases (17/40, 95% CI 29-58, p = 0.32). Median doses to the brain (10.8 Gy vs. 11.3 Gy, Wilcoxon rank-sum p = 0.012) and brain minus PTV (9.2 Gy vs 10.0 Gy, Wilcoxon rank-sum p = 0.009) were lower with ML planning versus manual planning, respectively. Doses to other structures, including hippocampi, cochlea, pituitary and hypothalamus were not statistically different. In this prospective study with blinded oncologist evaluation, ML-assisted RT planning for primary CNS tumors was faster than manual planning, and produced a very high rate of acceptable plans with similar or superior OAR sparing. Future work will be undertaken to iteratively refine the ML model using the preferred cases from this study.