Li Li, Sun Yangyang, Sun Yang, Gao Yunhe, Zhang Benlong, Qi Ruizhao, Sheng Fugeng, Yang Xiaodong, Liu Xu, Liu Lin, Lu Canrong, Chen Lin, Zhang Kecheng
Chinese PLA Medical School, Beijing, P. R. China.
Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, P. R. China.
Gastroenterol Rep (Oxf). 2025 Jun 3;13:goaf039. doi: 10.1093/gastro/goaf039. eCollection 2025.
Increasing evidence suggests that non-operative management (NOM) with antibiotics could serve as a safe alternative to surgery for the treatment of uncomplicated acute appendicitis (AA). However, accurately differentiating between uncomplicated and complicated AA remains challenging. Our aim was to develop and validate machine-learning-based diagnostic models to differentiate uncomplicated from complicated AA. This was a multicenter cohort trial conducted from January 2021 and December 2022 across five tertiary hospitals. Three distinct diagnostic models were created, namely, the clinical-parameter-based model, the CT-radiomics-based model, and the clinical-radiomics-fused model. These models were developed using a comprehensive set of eight machine-learning algorithms, which included logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), gradient boosting (GB), K-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), and multi-layer perceptron (MLP). The performance and accuracy of these diverse models were compared. All models exhibited excellent diagnostic performance in the training cohort, achieving a maximal AUC of 1.00. For the clinical-parameter model, the GB classifier yielded the optimal AUC of 0.77 (95% confidence interval [CI]: 0.64-0.90) in the testing cohort, while the LR classifier yielded the optimal AUC of 0.76 (95% CI: 0.66-0.86) in the validation cohort. For the CT-radiomics-based model, GB classifier achieved the best AUC of 0.74 (95% CI: 0.60-0.88) in the testing cohort, and SVM yielded an optimal AUC of 0.63 (95% CI: 0.51-0.75) in the validation cohort. For the clinical-radiomics-fused model, RF classifier yielded an optimal AUC of 0.84 (95% CI: 0.74-0.95) in the testing cohort and 0.76 (95% CI: 0.67-0.86) in the validation cohort. An open-access, user-friendly online tool was developed for clinical application. This multicenter study suggests that the clinical-radiomics-fused model, constructed using RF algorithm, effectively differentiated between complicated and uncomplicated AA.
越来越多的证据表明，对于单纯性急性阑尾炎（AA）的治疗，使用抗生素的非手术治疗（NOM）可作为手术的安全替代方案。然而，准确区分单纯性和复杂性AA仍然具有挑战性。我们的目标是开发并验证基于机器学习的诊断模型，以区分单纯性和复杂性AA。这是一项于2021年1月至2022年12月在五家三级医院开展的多中心队列研究。创建了三种不同的诊断模型，即基于临床参数的模型、基于CT影像组学的模型和临床-影像组学融合模型。这些模型是使用一套全面的八种机器学习算法开发的，其中包括逻辑回归（LR）、支持向量机（SVM）、随机森林（RF）、决策树（DT）、梯度提升（GB）、K近邻（KNN）、高斯朴素贝叶斯（GNB）和多层感知器（MLP）。比较了这些不同模型的性能和准确性。所有模型在训练队列中均表现出优异的诊断性能，最大AUC达到1.00。对于临床参数模型，GB分类器在测试队列中产生的最佳AUC为0.77（95%置信区间[CI]：0.64-0.90），而LR分类器在验证队列中产生的最佳AUC为0.76（95%CI：0.66-0.86）。对于基于CT影像组学的模型，GB分类器在测试队列中获得的最佳AUC为0.74（95%CI：0.60-0.88），SVM在验证队列中产生的最佳AUC为0.63（95%CI：0.51-0.75）。为临床应用开发了一个开放获取、用户友好的在线工具。这项多中心研究表明，使用RF算法构建的临床-影像组学融合模型能够有效区分复杂性和单纯性AA。
