Abstract
Review of radiotherapy (RT) treatment plans often relies on subjective analysis by physicists and physicians. Qualitative step-wise metrics are non-differentiable as optimization objectives, posing hurdles to automated RT planning. While continuous plan quality scorecards are used for retrospective evaluation, their use as optimization objectives remains limited. This paper proposes a data-driven, differentiable scorecard based on a Gaussian Mixture Model cumulative distribution function (GMM-CDF) for cohort-aware radiotherapy plan evaluation. Historical clinical prostate plans were categorized by prescription and target complexity, including 70 Gy hypofractionation and 40 Gy SBRT cohorts. A 3-component GMM mapped dosimetric parameters to continuous percentile-like scores. The naturally differentiable GMM-CDF yields probability density functions as exact derivatives, enabling fast gradient-based optimization. The GMM-CDF scorecard was evaluated via four experiments: an 80/20 train/test split, a target-complexity cohort assessment, a cross-prescription mismatch test, and scalar dose-scaling optimization using an L-BFGS-B solver. Results demonstrated similar grade distributions between train and testing set. Cohort-specific scorecards produced distinct organ-at-risk (OAR) score distributions versus generalized scorecards, particularly across single-, dual-, and tri-target groups. In cross-prescription testing, OAR scores shifted substantially with mismatched prescription scorecards, whereas prescription-normalized target scores remained relatively stable. In optimization testing, average plan scores improved by 22.8 points. The proposed GMM-CDF scorecard provides a continuous, differentiable approach of selected DVH-based plan quality metrics, supporting differentiable statistical scorecards as optimization-aware plan quality tools.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_019.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{ChaHo_ADifferentiable_MICCAISAT2026,
author = { Chang, Ho-Hsin AND Cardan, Rex AND Popple, Richard A. AND Stanley, Dennis N. AND Fiveash, John B. AND Bodduluri, Sandeep AND Harms, Joseph AND Cardenas, Carlos E.},
title = { { A Differentiable Gaussian Mixture Model-based Scorecard for Cohort-Aware Radiotherapy Plan Evaluation and Optimization } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}