Purpose: To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer radiotherapy. Materials and Methods: In all, 74 patients with peripheral zone localized prostate adenocarcinoma underwent pretreatment 3.0T MRI before external beam radiotherapy. Median follow-up of 47 months revealed 11 patients with biochemical recurrence. Prostate tumors were segmented on T 2 -weighted sequences (T 2 -w) and contours were propagated onto the coregistered apparent diffusion coefficient (ADC) images. We extracted 140 image features from normalized T 2 -w and ADC images corresponding to first-order (n 5 6), gradient-based (n 5 4), and second-order Haralick textural features (n 5 130). Four geometrical features (tumor diameter, perimeter, area, and volume) were also computed. Correlations between Gleason score and MRI features were assessed. Cox regression analysis and random survival forests (RSF) were performed to assess the association between MRI features and biochemical recurrence. Results: Three T 2 -w and one ADC Haralick textural features were significantly correlated with Gleason score (P < 0.05). Twenty-eight T 2 -w Haralick features and all four geometrical features were significantly associated with biochemical recurrence (P < 0.05). The most relevant features were Haralick features T 2 -w contrast, T 2 -w difference variance, ADC median, along with tumor volume and tumor area (C-index from 0.76 to 0.82; P < 0.05). By combining these most powerful features in an RSF model, the obtained C-index was 0.90. Conclusion: T 2 -w Haralick features appear to be strongly associated with biochemical recurrence following prostate cancer radiotherapy. Level of Evidence: 3

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In external beam radiotherapy for prostate cancer limiting toxicities for dose escalation are bladder and rectum toxicities. Normal tissue complication probability models aim at quantifying the risk of developping adverse events following radiotherapy. These models, originally proposed in the context of uniform irradiation, have evolved to implementations based on the state-of-the-art classification methods which are trained using empirical data. Recently, the use of image processing techniques combined with population analysis methods has led to a new generation of models to understand the risk of normal tissue complications following radiotherapy. This paper overviews those methods in the case of prostate cancer radiation therapy and propose some lines of future research.