Predicting patient-specific quality assurance outcomes in helical tomotherapy using plan complexity and 3D dose-distribution radiomics.

Pretreatment patient-specific quality assurance (PSQA) for helical tomotherapy (HT) is time-consuming and resource-intensive. Machine-learning-based screening tools may help prioritize measurement-based QA resources. This study investigated whether combining plan complexity descriptors with 3D dose-distribution radiomic features could improve the prediction of HT plan deliverability. A total of 498 clinical HT plans from two institutions were retrospectively analyzed, including 286 plans from Institution 1 and 212 from Institution 2. For each plan, 72 plan complexity features and 851 dose-distribution radiomic features were extracted. Support vector machine classifiers were developed separately for each institution using three feature sets: plan complexity features alone, dose-distribution radiomic features alone, and their combination. Model performance was evaluated on independent test sets using receiver operating characteristic analysis, with ground-truth PSQA outcomes defined according to preset gamma passing-rate thresholds. Across both institutions and both gamma criteria, the hybrid model achieved the best discrimination. Its AUCs were 0.774 and 0.938 for γ 3%/2 mm in Institutions 1 and 2, respectively, and 0.820 and 0.825 for γ 2%/2 mm. The most informative predictors included variables from both feature domains, and the hybrid model achieved higher AUCs than models using either domain alone. Additional analyses, including an exact permutation test and a bidirectional cross-institution evaluation, showed that the high AUCs were unlikely to result from chance but highlighted limited transferability across centers. These findings suggest that integrating plan complexity and 3D dose-distribution radiomic features may support risk-adapted pretreatment PSQA screening for HT. However, external multi-institution validation and prospective workflow assessment are required before considering clinical implementation.