Habitat Radiomics and Machine Learning for Preoperative Three-class Classification of Pediatric Acute Appendicitis: A...

Accurate preoperative differentiation of pediatric acute appendicitis subtypes is relevant to treatment planning. We developed and externally validated a machine-learning framework combining habitat-based computed tomography (CT) radiomics with clinical variables for three-class classification (simple, complex, and appendiceal abscess). This retrospective dual-center study included 317 children undergoing appendectomy at Institution A (training n = 222, internal validation n = 95) and 174 children at Institution B (external validation). K-means + + habitat segmentation (k = 3) of preoperative noncontrast CT volumes of interest yielded 1218 radiomic features; ten features selected on the training cohort were combined with 20 preoperative clinical variables. Eight classifiers were compared using macro-averaged one-versus-rest area under the receiver operating characteristic curve (AUC), accuracy, and macro-F1, with bootstrap confidence intervals (CIs), DeLong tests, calibration statistics, and decision curve analysis. In external validation, naive Bayes achieved the highest macro-AUC (0.946, 95% CI 0.910-0.974), accuracy 0.776 (0.713-0.833), and macro-F1 0.766 (0.674-0.837), significantly exceeding all seven comparators among the configurations evaluated (p < 0.001); the internal validation macro-AUC was 0.940 (0.893-0.977). Per-class external AUCs were 0.920 (simple), 0.948 (complex), and 0.971 (abscess). Calibration slopes (0.23-0.30) indicated that probability outputs require recalibration before probabilistic use. In a five-arm ablation of the input feature blocks, the fusion model combining clinical variables with habitat radiomics performed best (external macro-AUC 0.971), ahead of habitat radiomics alone (0.952), clinical variables plus conventional whole-VOI radiomics (0.776), clinical variables alone (0.763) and conventional whole-VOI radiomics alone (0.582). Habitat radiomics combined with machine learning provided accurate, externally validated three-class classification of pediatric acute appendicitis in children undergoing appendectomy. Naive Bayes offered the most balanced subtype discrimination. The model is a subtype-stratification aid rather than a stand-alone diagnostic or treatment-selection tool.