International journal of surgery (London, England) · 2025
Self-supervised learning model integrates plain and contrast-enhanced CT for preoperatively identifying gangrenous cholecystitis: a multicenter retrospective cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Guo Qingping, Li Yue, Huang Yuhong, Xie Shengjie, Li Siyang, Zheng Jinpeng, Yang Haiqing, Yuan Guandou, He Songqing
Аннотация
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.