Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.
Authors
Affiliations (7)
Affiliations (7)
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China. [email protected].
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. [email protected].
- Department of Gastroenterology, The First Affiliated Hospital of Shandong, First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China.
- Gastroenterology Department, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. [email protected].
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China. [email protected].
Abstract
This study aims to develop and validate an interpretable deep learning (DL) model and a nomogram based on endoscopic ultrasound (EUS) images for the prediction of pathological grading in pancreatic neuroendocrine tumors (PNETs). This multicenter retrospective study included 108 patients with PNETs, who were divided into train (<i>n</i> = 81, internal center) and test cohorts (<i>n</i> = 27, external centers). Univariate and multivariate logistic regression were used for screening demographic characteristics and EUS semantic features. Deep transfer learning was employed using a pre-trained ResNet18 model to extract features from EUS images. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO), and various machine learning algorithms were utilized to construct DL models. The optimal model was then integrated with clinical features to develop a nomogram. The performance of the model was assessed using the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). The nomogram, which integrates the optimal DL model (Naive Bayes) with clinical features, achieved AUC values of 0.928 (95% CI 0.849–0.981) in the train cohort and 0.882 (95% CI 0.778–0.954) in the test cohort. Calibration curves revealed minimal discrepancies between predicted and actual probabilities, with mean absolute errors of 4.5% and 6.6% in the train and test cohorts, respectively. DCA and CIC demonstrated substantial net benefit and clinical utility. The SHapley Additive exPlanations (SHAP) method provided insights into the contribution of each DL feature to the model’s predictions. This study developed and validated a novel interpretable DL model and nomogram using EUS images and machine learning, which holds promise for enhancing the clinical application of EUS in identifying PNETs’ pathological grading. The online version contains supplementary material available at 10.1186/s12911-025-03193-3.