APEX-NET: automated pancreatic evaluation network using early non-contrast CT.
Authors
Affiliations (10)
Affiliations (10)
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Department of Radiology, West China Tianfu Hospital of Sichuan University, Chengdu, China.
- Department of Radiology, Chongqing University, Qianjiang Hospital, Chongqing, China.
- Department of Radiology, Jiangjin Central Hospital of Chongqing, Chongqing, China.
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China. [email protected].
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China. [email protected].
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China. [email protected].
- Shanghai Clinical Research and Trial Center, Shanghai, China. [email protected].
Abstract
To develop and validate APEX-NET for early diagnosis and severity stratification of acute pancreatitis (AP) using non-contrast CT (NCCT), by leveraging contrast-enhanced CT (CECT) feature learning. This five-center retrospective and prospective study included 3383 patients, comprising AP and Non-AP (abdominal pain patients and healthy individuals) patients. APEX-NET was trained and evaluated to perform pancreas segmentation, AP diagnosis (AP vs Non-AP), and severity prediction (mild, moderately severe, or severe per the revised Atlanta classification) using 3 internal and 2 external cohorts. A feature mapping module was employed to derive simulated CECT features from NCCT based on paired NCCT-CECT feature learning. The model was further evaluated with subgroup analyses, and a reader study was conducted by comparing its performance with six radiologists of varying experiences. Evaluation metrics included the Dice similarity coefficient, area under the receiver operating characteristic curve (AUC), and accuracy. For AP diagnosis, APEX-NET achieved AUCs of 0.949, 0.958, 0.981, and 0.955 in the validation, internal, and two external testing cohorts, respectively. For severity prediction, APEX-NET significantly outperformed the NCCT model (p < 0.05), with macro-average AUCs of 0.873 (validation) and 0.872 (internal testing). The advantage of APEX-NET had been demonstrated in almost all the age, gender, and etiology subgroups. In the reader study, APEX-NET performed comparably to senior radiologists and superior to junior radiologists (p < 0.05). APEX-NET enables accurate NCCT-based diagnosis and early severity stratification of AP, demonstrating strong potential for clinical integration to overcome the inherent delay of CECT-based assessment. Question The absence of an accurate method for predicting AP severity from early NCCT, the initial diagnostic scan, thus forgoing the critical intervention window. Findings Achieve accurate severity prediction for AP by incorporating contrast-enhanced feature learning. Demonstrate robust performance across diverse demographic groups, etiologies, and imaging parameters. Clinical relevance The APEX-NET, an integrated deep learning framework using NCCT, accelerated the diagnosis and severity stratification of AP, demonstrating performance comparable to senior radiologists and direct potential for clinical workflow integration by reducing reliance on delayed contrast-enhanced scans.