Dosimetric equivalence of deep learning prostate contours after LDR brachytherapy: pre-declared margins, patient-level acceptance thresholds and the incremental predictive value of DVH indices

Abstract

Background and purpose: Dose Volume Histogram (DVH) indices remain the main dose-effect metrics for toxicity prediction, but they depend on how contours were made. Inter-observer variability (IOV) is unavoidable and clinically accepted, so the question for automatic segmentation addresses equivalence: do automatic contours produce DVH errors comparable to human IOV, and can these indices predict patient-reported toxicity? Materials and methods: In 429 patients treated with iodine-125 LDR-BT monotherapy, indices from expert manual delineation were compared with a deterministic and a Bayesian nnUNet on a fixed dose distribution, by two-one-sided tests against margins set from CT contouring IOV. Logistic regression gave patient-level thresholds at 90\,\% probability of equivalence. In 380 patients, eleven DHV indices were added to a clinical baseline predicting change in International Prostate Symptom Score (IPSS) at six horizons over 5 years, with nested cross-validation, bootstrap intervals and corrected $t$-tests. Results: All cohort-level comparisons of DVH indices were declared equivalent, with no interval consuming more than 44\,\% of its equivalence margin. Individual agreement was weaker with equivalence rates from 55.9\,\% to 89.7\,\%, depending on the DVH index and automatic segmentation model. Thresholds ranged from 0.864 to 0.958 Dice. No DVH block improved IPSS prediction at any horizon. The largest improvement declared by bootstrap intervals was 0.12 IPSS points, well below the minimal clinically important difference, across all learners. Conclusion: Automatic contours matched expert dosimetry within human IOV on average, but individual equivalence requires a volume dependent quality metric threshold definition. Our DVH indices panel provides no significant predictive power for IPSS, irrespective of segmentation source and horizon.