Bladder spatial-dose descriptors correlate with acute urinary toxicity after radiation therapy for prostate cancer

Purpose: To assess bladder spatial-dose parameters predicting acute urinary toxicity after radiotherapy for prostate cancer (PCa) through a pixel-wise method for analysis of bladder dose-surface maps (DSMs). Materials & methods: The final cohort of a multi-institutional study, consisting of 539 patients with PCa treated with conventionally (CONV:1.8–2 Gy/fr) or moderately hypo-fractionated radiotherapy (HYPO:2.2–2.7 Gy/fr) was considered. Urinary toxicity was evaluated through the International Prostate Symptoms Score (IPSS) administered before and after radiotherapy. IPSS increases P10 and 15 points at the end of radiotherapy (DIPSSP10 and DIPSSP15) were chosen as endpoints. Average DSMs (corrected into 2 Gy-equivalent doses) of patients with/without toxicity were compared through a pixel-wise method. This allowed the extraction of selected spatial descriptors discriminating between patients with/without toxicity. Previously logistic models based on dose-surface histograms (DSH) were considered and replaced with DSM descriptors. Discrimination power, calibration and log-likelihood were considered to evaluate the impact of the inclusion of spatial descriptors. Results: Data of 375/539 patients were available. DIPSSP10 was recorded in 76/375 (20%) patients, while 30/375 (8%) experienced DIPSSP15. The posterior dose at 12 mm from the bladder base (roughly corresponding to the trigone region) resulted significantly associated to toxicity in the whole/HYPO populations. The cranial extension of the 75 Gy isodose along the bladder central axis was the best DSM-based predictor in CONV patients. Multi-variable models including DSM descriptors showed better discrimination (AUC = 0.66–0.77) when compared to DSH-based models (AUC = 0.58–0.71) and higher log-likelihoods. Conclusion: DSMs are correlated with the risk of acute GU toxicity. The incorporation of spatial descriptors improves discrimination and log-likelihood of multi-variable models including dosimetric and clinical parameters.