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Introduction: Prostate cancer (PCa) is a prevalent malignancy in European men, often treated with radiotherapy (RT) for localized disease. While modern RT achieves high success rates, concerns about late gastrointestinal (GI) toxicities persist. This retrospective study aims to identify predictors for late GI toxicities following definitive conventionally fractionated external beam RT (EBRT) for PCa, specifically exploring the dose to the rectal wall. Materials and methods: A cohort of 96 intermediate- to high-risk PCa patients underwent EBRT between 2008 and 2016. Rectum and rectum wall contours were delineated, and 3D dose matrices were extracted. Volumetric and dosimetric indices were computed, and statistical analyses were performed to identify predictors using the Mann-Whitney U-rank test, logistic regression, and recursive feature elimination. Results: In our cohort, 15 out of 96 patients experienced grade II late proctitis. Our analysis reveals distinct optimal predictors for rectum and rectum wall (RW) structures varying with α/ß values (3.0 and 2.3 Gy) across prescribed doses of 68 to 76 Gy. Despite variability, RW predictors demonstrate greater consistency, notably V68Gy[%] to V74Gy[%] for α/ß 3.0 Gy, and V68Gy[%] to V70Gy[%] for α/ß 2.3 Gy. The model with α/ß 2.3 Gy, featuring RW volume receiving 70 Gy (V70Gy[%]), stands out with a BIC value of 62.92, indicating its superior predictive effectiveness. Finally, focusing solely on the rectum structure, the V74Gy[%] emerges the best predictor for α/ß 3.0 Gy, with a BIC value of 66.73. Conclusion: This investigation highlights the critical role of V70Gy[%] in the rectum wall as a robust predictor for grade II late gastrointestinal (GI) toxicity following external beam radiation therapy (EBRT) for prostate cancer (PCa). Furthermore, our findings suggest that focusing on the rectum wall specifically, rather than the entire rectum, may offer improved accuracy in assessing proctitis development. A V70Gy (in EQD2 with α/ß 2.3 Gy) of ≤5% and if possible ≤1% for the rectal wall should be achieved to minimize the risk of late grade II proctitis.
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OBJECTIVES: In lung cancer, one of the main limitations for the optimal integration of the biological and anatomical information derived from Positron Emission Tomography (PET) and Computed Tomography (CT) is the time and expertise required for the evaluation of the different respiratory phases. In this study, we present two open-source models able to automatically segment lung tumors on PET and CT, with and without motion compensation. MATERIALS AND METHODS: This study involved time-bin gated (4D) and non-gated (3D) PET/CT images from two prospective lung cancer cohorts (Trials 108237 and 108472) and one retrospective. For model construction, the ground truth (GT) was defined by consensus of two experts, and the nnU-Net with 5-fold cross-validation was applied to 560 4D-images for PET and 100 3D-images for CT. The test sets included 270 4D- images and 19 3D-images for PET and 80 4D-images and 27 3D-images for CT, recruited at 10 different centres. RESULTS: In the performance evaluation with the multicentre test sets, the Dice Similarity Coefficients (DSC) obtained for our PET model were DSC(4D-PET) = 0.74 ± 0.06, improving 19% relative to the DSC between experts and DSC(3D-PET) = 0.82 ± 0.11. The performance for CT was DSC(4D-CT) = 0.61 ± 0.28 and DSC(3D-CT) = 0.63 ± 0.34, improving 4% and 15% relative to DSC between experts. CONCLUSIONS: Performance evaluation demonstrated that the automatic segmentation models have the potential to achieve accuracy comparable to manual segmentation and thus hold promise for clinical application. The resulting models can be freely downloaded and employed to support the integration of 3D- or 4D- PET/CT and to facilitate the evaluation of its impact on lung cancer clinical practice. CLINICAL RELEVANCE STATEMENT: We provide two open-source nnU-Net models for the automatic segmentation of lung tumors on PET/CT to facilitate the optimal integration of biological and anatomical information in clinical practice. The models have superior performance compared to the variability observed in manual segmentations by the different experts for images with and without motion compensation, allowing to take advantage in the clinical practice of the more accurate and robust 4D-quantification. KEY POINTS: Lung tumor segmentation on PET/CT imaging is limited by respiratory motion and manual delineation is time consuming and suffer from inter- and intra-variability. Our segmentation models had superior performance compared to the manual segmentations by different experts. Automating PET image segmentation allows for easier clinical implementation of biological information.