Preoperative CT-based three-class classification of pediatric acute appendicitis

Source article: Habitat 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…

By the Truvace deskOctober 19, 20262 min read1 sourcePeer-reviewedTRV-2026-1913· cite & verify

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In brief

Researchers built a classifier that uses preoperative noncontrast CT scans and clinical variables to sort pediatric appendicitis into simple, complex, and abscess subtypes. The CT volumes were divided into three habitat sub-regions, from which 1218 radiomic features were extracted, and ten selected features were combined with 20 clinical variables. Eight classifiers were compared, with naive Bayes performing best.

Earlier knowledge of the subtype could help treatment planning, but the tool is positioned only as a stratification aid. Its probability estimates were not well calibrated, the data were retrospective, and all children had undergone appendectomy, so performance in other settings or clinical workflows remains untested.

Main points

The study was retrospective and dual-center: 317 children at Institution A for training and internal validation, and 174 children at Institution B for external validation.
Naive Bayes reached external accuracy 0.776 and macro-F1 0.766, significantly exceeding all seven comparator classifiers.
Per-class external AUCs were 0.920 for simple, 0.948 for complex, and 0.971 for abscess appendicitis.
In the ablation, fusing clinical variables with habitat radiomics gave the best external macro-AUC at 0.971, while conventional whole-VOI radiomics alone scored 0.582.

The gain

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 problem

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.

The rundown

The cohort came from two institutions. Institution A supplied 317 children split into 222 for training and 95 for internal validation, and Institution B supplied 174 children as a fully external test set. All had undergone appendectomy and preoperative noncontrast CT.

Habitat segmentation used K-means++ with k = 3. Feature selection was done on the training cohort only, and the model comparison used bootstrap confidence intervals, DeLong tests, calibration statistics, and decision curve analysis.

A five-arm ablation showed habitat radiomics carried most of the signal. Habitat radiomics alone reached 0.952 external macro-AUC, compared with 0.763 for clinical variables alone and 0.582 for conventional whole-VOI radiomics alone. The fusion model reached 0.971.

What this doesn’t fix

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.