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 ⩾10 and 15 points at the end of radiotherapy (ΔIPSS ⩾ 10 and ΔIPSS ⩾ 15) 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. ΔIPSS ⩾ 10 was recorded in 76/375 (20%) patients, while 30/375 (8%) experienced ΔIPSS ⩾ 15. 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.