Self-supervised learning model integrates plain and contrast-enhanced CT for preoperatively identifying gangrenous...

Gangrenous cholecystitis (GC) is a severe complication of acute cholecystitis that requires prompt surgical intervention. However, its preoperative diagnosis remains challenging due to the limitations of traditional imaging techniques. This study aims to develop a self-supervised learning (SSL) model to preoperatively identify GC using both plain and contrast-enhanced CT images.

This was a retrospective, multicenter cohort study conducted from January 2021 to September 2024. A total of 7368 CT images from 1228 patients (training set: 921 patients; independent validation set: 307 patients) were retrospectively analyzed. We developed an SSL model using seResNet-50 framework, trained on unenhanced and contrast-enhanced CT images, to predict the presence of GC preoperatively. The model leverages unlabeled data to pretrain the network, and is then fine-tuned on a limited set of annotated images. After feature extraction and selection, we developed three models for predicting GC, including a fusion model, an enhanced CT (ECT) model, and a non-enhanced CT (NECT) model. Performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

The fusion model demonstrated robust performance in preoperative GC prediction. In the training set, the fusion model achieved an AUC of 0.965, a sensitivity of 88%, and a specificity of 95% in detecting GC. In Validation Set I, the fusion model had an AUC of 0.879, surpassing the enhanced and non-enhanced models (AUCs: 0.791, 0.756, respectively). Similarly, in Validation Set II, the fusion model achieved an AUC of 0.887, significantly better than the ECT and NECT models (AUCs: 0.810, 0.730). The model also provided interpretable analyses by detecting subtle features of gangrenous changes in the CT images, facilitating clinical decision-making.

We developed a fusion SSL model for preoperative prediction of gangrenous cholecystitis using both unenhanced and contrast-enhanced CT scans. The model's high diagnostic performance suggests its clinical applicability in improving early diagnosis and timely risk stratification of GC.