Deep Learning Pipeline for Automatic Segmentation, Classification, and Molecular Subtyping of Three Pediatric Posterior Fossa Tumors Using T2-Weighted MRI.

BACKGROUND: Pediatric posterior fossa tumors vary in malignancy, treatment, and prognosis across tumor types and molecular subtypes, yet noninvasive preoperative differentiation remains challenging. PURPOSE: To develop a deep learning (DL) pipeline using T2-weighted (T2w) MR images to automatically segment pediatric posterior fossa tumors, differentiate tumor types (medulloblastoma [MB], ependymoma [EP], pilocytic astrocytoma [PA]), and classify molecular subtypes of MB and EP. STUDY TYPE: Retrospective and prospective. POPULATION: 1305 patients (M/F: 828/477; 490 MB, 327 EP, and 488 PA) from three centers. For tumor segmentation and classification, PF-nnU-Net was developed on the training set (n = 880) and validated on a validation set (n = 220), an internal prospective test set (n = 90), and two external independent test sets (n = 68, n = 47). MB-nnU-Net was trained on 338 patients and tested on 63 patients for MB subtyping; a prior developed EP-nnU-Net was tested on 38 patients for EP subtyping. FIELD STRENGTH/SEQUENCE: 1.5 T or 3 T MRI, axial T2w images (turbo spin echo). ASSESSMENT: Three nnU-Net-based models: PF-nnU-Net and MB-nnU-Net for development, EP-nnU-Net for validation. Five-fold cross-validation was performed on training sets, followed by testing on independent test sets. STATISTICAL TESTS: Dice similarity coefficient for segmentation. Accuracy, sensitivity, specificity, the area under the receiver operating characteristic curve (AUC), and Cohen's kappa for classification. A two-sided p < 0.05 was considered significant. RESULTS: PF-nnU-Net achieved Dice scores of 0.94-0.96 and overall classification accuracy of 0.824-0.918 (multiclass Cohen's kappa: 0.722-0.873). MB-nnU-Net attained an overall accuracy of 0.794 (multiclass Cohen's kappa: 0.605), and EP-nnU-Net achieved an accuracy of 0.789 (Cohen's kappa: 0.538). DATA CONCLUSION: A fully automated DL pipeline was developed and validated to accurately segment pediatric posterior fossa tumors, differentiate tumor types (MB, EP, PA), and classify MB and EP molecular subtypes. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.