Development of a radiomics-3D deep learning fusion model for prognostic prediction in pancreatic cancer.

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Abstract

With pancreatic cancer’s dismal prognosis, developing accurate predictive tools is crucial for personalized treatment. This study aims to develop and evaluate a radiomics-3D deep learning fusion model to enhance survival prediction accuracy and explore its potential for clinical risk stratification in pancreatic cancer patients. This study retrospectively analyzed from pancreatic cancer patients treated at two hospitals between 2013 and 2023. Patients were split into training and test cohorts (7:3). Baseline clinical data and portal venous phase contrast-enhanced CT images were collected. Two physicians independently delineated tumor regions of interest (ROIs), and 1,037 radiomic features were extracted. After dimensionality reduction via Principal component analysis (PCA) and feature selection with LASSO regression, a radiomics model was developed using the random survival forest (RSF) algorithm to predict overall survival, accounting for censored data. A separate 3D-DenseNet model was trained using ROI-based image inputs to extract deep features. For fusion models, we adopted a binary classification approach to predict survival status at 1-, 2-, and 3-year time points. Radiomics features, 3D-DenseNet outputs, and clinical variables were integrated using logistic regression, random forest, support vector machine, and decision tree classifiers. Model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and accuracy. The best-performing fusion model was selected for clinical risk stratification. Kaplan-Meier curves and Log-rank tests were used to assess survival differences between risk groups. A total of 880 eligible patients were included in this study. In the test cohort, the performance of each model in predicting 1-year, 2-year, and 3-year survival was evaluated. The radiomics model achieved AUC values of 0.78, 0.85, and 0.91, with corresponding accuracies of 0.75, 0.77, and 0.77. The 3D-DenseNet model demonstrated AUC values of 0.81, 0.79, and 0.75, with accuracies of 0.72, 0.76, and 0.77. The fusion model, developed using logistic regression, exhibited superior predictive performance with AUC values of 0.87, 0.92, and 0.94, and accuracies of 0.84, 0.86, and 0.89, outperforming the individual unimodal models. Risk stratification based on the fusion model categorized patients into high-risk and low-risk groups, revealing a statistically significant difference in OS between the two groups (<i>P</i> < 0.001). Feature contribution analysis indicated that the 3D-DenseNet model had the greatest influence on the predictions of the fusion model, followed by the radiomics model. This study developed a fusion model incorporating radiomics features, deep learning-derived features, and clinical data, which outperformed unimodal models in predicting survival outcomes in pancreatic cancer and demonstrated potential utility in patient risk stratification. The online version contains supplementary material available at 10.1186/s12885-025-14889-0.

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