Predicting patient specific Pareto fronts from patient anatomy only. (September 2020)

• Record Type: Journal Article
• Authors: van der Bijl, Erik; Wang, Yibing; Janssen, Tomas; Petit, Steven
• Is Part Of: Radiotherapy and oncology. Volume 150(2020)
• Page Start: 46
• Page End: 50
• Publication Date: 2020-09
• Subjects: Treatment planning -- Pareto front -- Knowledge based planning (KBP) -- Prostate cancer; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.05.050
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Prediction of the patient specific treatment planning Pareto front is feasible. Prediction accuracy of Pareto fronts was below 2 Gy for 90% of cases. Patient specific trade-offs could be estimated in seconds after completing delineations. Abstract: Purpose: To demonstrate the feasibility of predicting the patient-specific treatment planning Pareto front (PF) for prostate cancer patients based only on delineations of PTV, rectum and body. Material/methods: Our methodology consists of four steps. First, using Erasmus-iCycle, the Pareto fronts of 112 prostate cancer patients were constructed by generating per patient 42 Pareto optimal treatment plans with different priorities. Dose parameters associated to homogeneity, conformity and dose to rectum were extracted. Second, a 3D convex function representing the PF spanned by the 42 plans was fitted for each patient using three patient-specific parameters. Third, ten features were extracted from the, aforementioned, structures to train a linear-regressor prediction algorithm to predict these three patient-specific parameters. Fourth, the quality of the predictions was assessed by calculating the average and maximum distances of the predicted PF to the 42 plans for patients in the validation cohort. Results: The prediction model was able to predict the clinically relevant PF within 2 Gy for 90% of the patients with a median average distance of 0.6 Gy. Conclusions: We demonstrate the feasibility of fast, accurate predictions of the patient-specific PF for prostate cancer patients based only on delineations of PTV, rectum and body.