Risk stratification and cross-center prediction of thoracoabdominal 6D setup errors

Thoracoabdominal CBCT-derived six-degree-of-freedom (6DoF) setup errors vary substantially across centers. Using record-level setup events as the unit of analysis, we examined whether pre-treatment workflow variables could stratify the risk of large translational events, how they performed for infrequent rotational events, and whether model performance held up across centers.

This retrospective two-center cohort study included CBCT-derived 6DoF setup records collected during 2022-2025 at Center 1 and 2020-2022 at Center 2. After exact matching and deduplication, 6,033 unique records were retained from the original 6,584 records. The primary endpoint was three-dimensional composite translational displacement ≥ 5 mm (Y5mm), calculated using tvec. Secondary endpoints were tvec ≥ 10 mm (Y10mm), maximum rotational magnitude (rmax) ≥ 3° (R3°), and the composite endpoint Yaction, defined as either Y5mm or R3°. Center, anatomic site, tumor type, and immobilization method were used as predictors. CatBoost and XGBoost were evaluated with three-fold stratified cross-validation and bidirectional cross-center external validation.

Event rates for Y5mm, Y10mm, R3°, and Yaction were 39.3%, 6.1%, 5.4%, and 40.9%, respectively. Key endpoint rates differed significantly between centers, suggesting marked domain shift. In internal validation, CatBoost and XGBoost performed similarly for Y5mm (AUPRC, 0.620 and 0.622, respectively), whereas prediction of R3° was limited. In bidirectional cross-center validation, performance for the primary and composite endpoints declined compared with internal validation and showed asymmetric transfer patterns. Calibration also worsened externally. SHAP analysis identified immobilization method as one of the most important features for Y5mm.

Thoracoabdominal CBCT-derived 6DoF setup errors show clear risk stratification patterns and substantial cross-center heterogeneity. Models based on pre-treatment workflow variables provided modest stratification for large translational events, but were less useful for rare large rotational events. For workflow deployment, local recalibration at the target center may be preferable to direct transfer of raw predicted probabilities.