RN-D

Purpose: Radiation necrosis (RN) is an important complication of radiation therapy (RT) and is challenging to assess radiographically because lesions are prone to inconsistent delineation, often necessitating intracranial surgery to obtain a diagnosis. This study evaluated how established deep learning segmentation models can be retrained on an institutional RN cohort, assessed external generalization, and integrated the best components into RN-D3, an end-to-end pipeline for RN detection, differentiation, and delineation. Methods and materials: We trained models on a cohort of 52 patients with RN after proton beam RT treated at Massachusetts General Hospital (2004-2016) and tested these models on an external cohort of 29 patients with 39 RN lesions from the MOLAB brain metastasis dataset (photon-based RT, five Spanish institutions, 2005-2021). Six architectures were evaluated: four convolutional neural networks (nnU-Net, STU-Net, TRIAD PlainConv, and Spark3D) and two transformers (SwinUNETR and TRIAD SwinB). Training strategies included training from scratch, supervised pretraining, self-supervised pretraining, and foundation model initialization. Matched-encoder pairs enabled direct comparison of pretraining effects. Performance was evaluated by Dice similarity coefficient (DSC), surface Dice at 2 mm, and lesion detection rate. A post-processing pipeline combining encoder features and shape radiomics was assessed for reducing false positives. The best segmentation model and this classifier were combined into RN-D Results: Spark3D and STU-Net achieved the highest external DSC (median 0.69 [IQR 0.50-0.84] and 0.65 [0.29-0.88]), followed by baseline nnU-Net (0.61 [0.39-0.83]). For Spark3D and STU-Net, large lesions (≥1 mL, Conclusion: RN-D