Dosimetric Impact of Hidden Input Parameters in Inverse Optimization Algorithms for GYN HDR Brachytherapy
YeongHyeon Park, Shiqin Su, Sarath Vijayan, Zhiqian Henry Yu, Mandy Cunningham, Yusung Kim

TL;DR
This study evaluates how hidden input parameters in inverse optimization algorithms affect dosimetric outcomes in gynecological HDR brachytherapy, revealing significant sensitivities and potential for plan improvement through parameter tuning.
Contribution
It systematically analyzes the dosimetric impact of hidden parameters in three inverse optimization algorithms across multiple applicator types in GYN HDR brachytherapy.
Findings
MCO shows highest sensitivity to hidden parameters.
Target coverage differences up to 28.4 Gy observed.
Hidden parameters significantly influence dosimetric outcomes.
Abstract
Inverse optimization (IO) algorithms are used in GYN HDR brachytherapy planning, with user parameter settings embedded in commercial TPS. To examine the dosimetric influence of hidden input parameters in three IO algorithms-IPSA, HIPO, and MCO-for GYN HDR brachytherapy across two applicator types. In-house implementations of IPSA, HIPO, and MCO were implemented and evaluated against retrospectively generated commercial TPS plans (Oncentra Brachy) using identical clinical input parameters across 24 cervical cancer cases (18 T&O; 6 T&O+Needles (T&O+N)). Each IO algorithm was assessed using 1k combinations of hidden parameters (e.g., dwell-time modulation constraints, convergence thresholds). Cumulative DVH curves and dosimetric indices (HR-CTV D98/D90, OAR D2cc) were compared with commercial plans. Standard deviations (SD) of DVH differences were used to characterize sensitivity to hidden…
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Taxonomy
TopicsEndometrial and Cervical Cancer Treatments · Advanced Radiotherapy Techniques · Head and Neck Cancer Studies
