aceHabitat Radiomics and Machine Learning for Preoperative Three-class Classification of Pediatric Acute Appendicitis: A Dual-center Study with External Validation
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…
A machine-learning model combining habitat-based CT radiomics and clinical variables distinguished simple, complex, and abscess appendicitis in children at a second hospital with a macro-AUC of 0.946.
The model's probability outputs were poorly calibrated (slopes 0.23-0.30) and need recalibration before probabilistic use, and it is only a stratification aid, not a stand-alone diagnostic tool.
Probability outputs were poorly calibrated and require recalibration, and the model is described only as a subtype-stratification aid, not a stand-alone diagnostic or treatment-selection tool.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-14
How should this claim be treated?
Truvace Impact Record TRV-2026-1913, v1: “Habitat Radiomics and Machine Learning for Preoperative Three-class Classification of Pediatric Acute Appendicitis: A Dual-center Study with External Validation.” Truvace, 2026-10-19. /record/TRV-2026-1913 (accessed at citation time). sha256 615a6f1e9d50a4f4…
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