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To perform bladder dose-surface map (DSM) analysis for (1) identifying symptomrelated sub-surfaces (Ssurf) and evaluating their prediction capability of urinary toxicity, (2) comparing DSM with dose-volume map (DVM) (method effect), and (3) assessing the reproducibility of DSM (cohort effect).Methods and materials: Urinary toxicities were prospectively analyzed for 254 prostate cancer patients treated with IMRT/IGRT at 78/80Gy. DSMs were generated by unfolding bladder surfaces in a 2D plane. Pixel-by-pixel analysis was performed to identify symptom-related Ssurf. Likewise, DVM analysis was performed to identify sub-volumes (Svol). The prediction capability of Ssurf and Svol DVHs was assessed by logistic/Cox regression using the area under the ROC curve (AUC). The Ssurf localization and prediction capability were compared to (1) the Svol obtained by DVM analysis in the same cohort and (2) the Ssurf obtained from other DSM studies.Results: Three Ssurf were identified in the bladder: posterior for acute retention (AUC =0.64), posterior-superior for late retention (AUC =0.68), and inferior-anterior-lateral for late dysuria (AUC=0.73). Five Svol were identified: one in the urethra for acute incontinence and four in the posterior bladder part for acute and late retention, late dysuria, and hematuria. The overlap between Ssurf and Svol was moderate for acute retention, good for late retention, and bad for late dysuria, and AUCs ranged from 0.62 to 0.81. The prediction capabilities of Ssurf and Svol models were not significantly different. Among five symptoms comparable between cohorts, common Ssurf was found only for late dysuria, with a good spatial agreement. Conclusion:Spatial agreement between methods is relatively good although DVM identified more sub-regions. Reproducibility of identified Ssurf between cohorts is low.

For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.