A Predictive Tool for Optimizing Treatment System Allocation in Hypofractionated Whole-Breast Radiotherapy
Recommended Citation
Hall RD, Dai Z, Luo B, Snyder KC, Doemer AJ, Walker E, Levin K, Movsas B, Thind KS. A Predictive Tool for Optimizing Treatment System Allocation in Hypofractionated Whole-Breast Radiotherapy. Med Phys 2025; 52(10):388-389.
Document Type
Conference Proceeding
Publication Date
9-30-2025
Publication Title
Med Phys
Keywords
breast cancer, breast radiotherapy, cancer patient, clinical article, cohort analysis, conference abstract, controlled study, flushing, human, linear accelerator, machine learning, predictive model, radiotherapy, therapy
Abstract
Purpose: To investigate the feasibility of a predictive tool for efficient allocation of hypofractionated whole-breast irradiation patients between Varian Truebeam and Ethos systems. Methods: A fully automated, knowledge-based automated planning tool integrated via the Eclipse (Varian, Palo Alto) scripting interface was developed in C# to simultaneously generate intensity-modulated tangential plans for Truebeam and Ethos. The tool calculates posterior breast separation to inform Truebeam energy selection. For larger separations, Truebeam plans use two 6 MV intensitymodulated tangent fields and two 15/18 MV static tangents, with the ratio determined empirically Ethos plans are limited to 6 MV-FFF. Fluences are then generated via a machine-learning DVH predictive model trained on an independent cohort, followed by post-processing for skin flash and fluence smoothing. A cohort of 28 breast cancer patients treated with 26 Gy in 5 fractions (Fast Forward trial) were replanned with the tool, producing automated Truebeam (autoTB) and Ethos (autoEthos) plans. Plans were renormalized to identical coverage (D95% = 95% Rx), and clinical goal compliance was assessed per Fast Forward constraints. Results: autoTB plans met clinical goals in 86% of patients (24/28), while autoEthos achieved 25% compliance (7/28). Analysis revealed posterior breast separation as a key factor influencing success. autoEthos plans failing dose homogeneity criteria (n=17) had an average separation of 24.7 cm, exceeding the 23.4 cm observed in successful plans. Notably, 76% (17/21) of failing autoEthos plans met all criteria with autoTB. These findings suggest that incorporating separation as a criterion for treatment system selection can enhance automated planning effectiveness. Conclusion: This study demonstrates the feasibility of a novel auto-planning tool for predictive patient allocation in hypofractionated whole-breast radiotherapy, using posterior breast separation as a key criterion. Further validation is warranted in larger cohorts to refine these criteria and identify additional easily measurable patient factors.
Volume
52
Issue
10
First Page
388
Last Page
389
