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.

Currently, there are inconsistencies among different studies on preoperative prediction of Cytokeratin 19 (CK19) expression in HCC using traditional imaging, radiomics, and deep learning. We aimed to systematically analyze and compare the performance of non-invasive methods for predicting CK19-positive HCC, thereby providing insights for the stratified management of HCC patients. A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library from inception to February 2025. Two investigators independently screened and extracted data based on inclusion and exclusion criteria. Eligible studies were included, and key findings were summarized in tables to provide a clear overview. Ultimately, 22 studies involving 3395 HCC patients were included. 72.7% (16/22) focused on traditional imaging, 36.4% (8/22) on radiomics, 9.1% (2/22) on deep learning, and 54.5% (12/22) on combined models. The magnetic resonance imaging was the most commonly used imaging modality (19/22), and over half of the studies (12/22) were published between 2022 and 2025. Moreover, 27.3% (6/22) were multicenter studies, 36.4% (8/22) included a validation set, and only 13.6% (3/22) were prospective. The area under the curve (AUC) range of using clinical and traditional imaging was 0.560 to 0.917. The AUC ranges of radiomics were 0.648 to 0.951, and the AUC ranges of deep learning were 0.718 to 0.820. Notably, the AUC ranges of combined models of clinical, imaging, radiomics and deep learning were 0.614 to 0.995. Nevertheless, the multicenter external data were limited, with only 13.6% (3/22) incorporating validation. The combined model integrating traditional imaging, radiomics and deep learning achieves excellent potential and performance for predicting CK19 in HCC. Based on current limitations, future research should focus on building an easy-to-use dynamic online tool, combining multicenter-multimodal imaging and advanced deep learning approaches to enhance the accuracy and robustness of model predictions.