An interpretable computed tomography radiomics-based machine learning model for preoperative prediction of complicated appendicitis in children.
5 affiliations
- 1Department of General Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, Hubei, 432000, China.
- 2Medical College, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China.
- 3Department of Pediatric Surgery, Yichang Central People's Hospital Affiliated to China Three Gorges University, Yichang, Hubei, 443003, China.
- 4Department of Pediatric Surgery, Yichang Central People's Hospital Affiliated to China Three Gorges University, Yichang, Hubei, 443003, China. [email protected].
- 5Department of General Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, Hubei, 432000, China. [email protected].
Abstract
Background
Accurate preoperative discrimination between uncomplicated and complicated acute appendicitis in children is critical for optimal surgical decision-making but remains a clinical challenge. Current reliance on conventional imaging and laboratory findings lacks sufficient precision, necessitating the development of more reliable predictive tools.Methods
We conducted a multicenter retrospective study to develop and validate a machine-learning model integrating CT radiomics and clinical variables. Data from 287 pediatric patients (aged 2-14 years) at Xiaogan Central Hospital (Jan 2017-May 2025) were randomly divided into a training (n = 201) and an internal-validation cohort (n = 86). An independent external-validation cohort comprised 164 patients from Yichang Central People's Hospital (Nov 2023-May 2025). Preoperative CT images and clinical data were processed; features with < 30% missing values were imputed. An automated modeling pipeline constructed 100 candidate models by combining 10 feature-selection algorithms with 10 machine-learning frameworks. Model performance was assessed using receiver operating characteristic curve analysis, with interpretability provided by SHapley Additive exPlanations (SHAP).Results
The optimal model, chi square feature selection combined with AdaBoost, demonstrated robust discriminative ability. It achieved AUCs of 0.900 (95% CI: 0.832-0.958), 0.889 (95% CI: 0.841-0.937), and 0.893 (95% CI: 0.855-0.931) in the training, internal-validation, and external-validation cohorts, respectively. The model achieved a sensitivity of 87% and specificity of 85% in the external-validation cohort. SHAP analysis identified radiomic texture features (RootMeanSquared, Strength, Skewness), appendicolith presence, and maximum body temperature as the most important predictors.Conclusion
A CT radiomics-based machine learning model integrating clinical features can accurately differentiate complicated from uncomplicated appendicitis in children, providing an interpretable framework to support preoperative surgical decision-making.Trial registration
Not applicable (retrospective diagnostic study).
Related
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BibTeX
@article{li2026interpretable,
title = {{An interpretable computed tomography radiomics-based machine learning model for preoperative prediction of complicated appendicitis in children.}},
author = {Zhaopu Li and Yang Li and Zhongjing Zhang and Jiaqing Lin and Tao Xu and Wei Zhu and Zhaokun Guo and Kun Yang},
journal = {BMC medical imaging},
year = {2026},
month = aug,
doi = {10.1186/s12880-026-02628-1},
url = {https://europepmc.org/article/MED/42958065},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - An interpretable computed tomography radiomics-based machine learning model for preoperative prediction of complicated appendicitis in children.
AU - Zhaopu Li
AU - Yang Li
AU - Zhongjing Zhang
AU - Jiaqing Lin
AU - Tao Xu
AU - Wei Zhu
AU - Zhaokun Guo
AU - Kun Yang
PY - 2026
DA - 2026/08/01
JO - BMC medical imaging
DO - 10.1186/s12880-026-02628-1
UR - https://europepmc.org/article/MED/42958065
AB - Background Accurate preoperative discrimination between uncomplicated and complicated acute appendicitis in children is critical for optimal surgical decision-making but remains a clinical challenge. Current reliance on conventional imaging and laboratory findings lacks sufficient precision, necessitating the development of more reliable predictive tools.Methods We conducted a multicenter retrospective study to develop and validate a machine-learning model integrating CT radiomics and clinical variables. Data from 287 pediatric patients (aged 2-14 years) at Xiaogan Central Hospital (Jan 2017-May 2025) were randomly divided into a training (n = 201) and an internal-validation cohort (n = 86). An independent external-validation cohort comprised 164 patients from Yichang Central People's Hospital (Nov 2023-May 2025). Preoperative CT images and clinical data were processed; features with < 30% missing values were imputed. An automated modeling pipeline constructed 100 candidate models by combining 10 feature-selection algorithms with 10 machine-learning frameworks. Model performance was assessed using receiver operating characteristic curve analysis, with interpretability provided by SHapley Additive exPlanations (SHAP).Results The optimal model, chi square feature selection combined with AdaBoost, demonstrated robust discriminative ability. It achieved AUCs of 0.900 (95% CI: 0.832-0.958), 0.889 (95% CI: 0.841-0.937), and 0.893 (95% CI: 0.855-0.931) in the training, internal-validation, and external-validation cohorts, respectively. The model achieved a sensitivity of 87% and specificity of 85% in the external-validation cohort. SHAP analysis identified radiomic texture features (RootMeanSquared, Strength, Skewness), appendicolith presence, and maximum body temperature as the most important predictors.Conclusion A CT radiomics-based machine learning model integrating clinical features can accurately differentiate complicated from uncomplicated appendicitis in children, providing an interpretable framework to support preoperative surgical decision-making.Trial registration Not applicable (retrospective diagnostic study).
SN - 1471-2342
ER -