Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis due to late diagnosis, limitations of computed tomography (CT) imaging, and low accuracy of clinical biomarkers. This study aimed to develop and validate a multimodal artificial intelligence (AI) approach integrating imaging-based deep learning (DL) and clinical data-driven machine learning (ML) to improve PDAC diagnosis. A retrospective cohort of 158 patients (123 PDAC, 35 benign) undergoing pancreatic surgery was analyzed. A YOLOv8-based DL model was trained on contrast-enhanced CT scans to detect pancreatic lesions, while clinical data (age, sex, serum CA19-9) were analyzed with a Random Forest ML classifier. Predictions from both models were combined into a multimodal fusion model, optimized to maximize diagnostic accuracy. Performance metrics included precision, recall, accuracy, F1-score, and ROC-AUC. The imaging-based DL model achieved strong tumor detection performance (mAP: 87.0%, precision: 86.5%, recall: 81.2%). The clinical ML model showed excellent specificity (precision: 100%, ROC-AUC: 0.931) but limited sensitivity (60%). The multimodal AI fusion model outperformed both individual models, significantly improving sensitivity, specificity, and overall diagnostic accuracy. A multimodal AI strategy integrating DL imaging analysis with ML-based clinical predictions markedly enhances diagnostic performance in pancreatic cancer. This approach offers potential as an effective decision-support tool, facilitating earlier diagnosis and optimized clinical decision-making.
Şahin et al. (Wed,) studied this question.