Abstract
Purpose To develop and validate a deep learning model for automatic segmentation of primary central nervous system lymphoma (PCNSL) at postcontrast T1-weighted MRI. Materials and Methods Data were retrospectively collected from patients with pathologically proven immunocompetent PCNSL between September 2010 and February 2022. Postcontrast T1-weighted MRI scans were used to train and validate a deep learning model based on the nnU-Net framework. Manual segmentation by neuroradiologists served as the reference standard. The model was trained using an internal dataset from a single center and tested on both internal and external test sets from seven additional centers. Performance was assessed using Dice score, mean average surface distance, and F1 score. Statistical comparisons were performed using Mann-Whitney U test and bootstrap resampling for CIs. Results The study included 135 patients (68 female, 66 male, and one of unspecified sex; internal dataset: mean age ±SD, 67.0 years ± 12.0; external dataset: mean age, 75.5 years ± 13.6). The model achieved a mean Dice score of 0.84 (95% CI: 0.79, 0.88) on the internal test set (n = 44) and 0.88 (95% CI: 0.84, 0.91) on the external test set (n = 48), with no evidence of a difference between test sets (P = .59). Performance varied by lesion type; accuracy was highest in homogeneous discrete lesions, and performance was slightly decreased when numerous poorly defined infracentimetric lesions occurred. Strong volumetric correlation was observed between automatic and manual segmentations (internal: r = 0.99, P < .001; external: r = 0.98, P < .001). Conclusion A deep learning model achieved accurate and robust automatic segmentation of PCNSL across multiple clinical centers with different MRI acquisition parameters. Keywords: Brain Lymphoma, Brain Tumor, Automatic Segmentation, Artificial Intelligence, Deep Learning Supplemental material is available for this article. © RSNA, 2025.