Differentiation Benign and Malignant Pancreatic Lesions

Accurate differentiation between benign and malignant pancreatic lesions is critical for patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography images to predict benign and malignant pancreatic lesions. This retrospective study across three medical centers constituted a training cohort, an internal testing cohort, and an external validation cohorts. A novel hybrid model, Multi-Scale Large Kernel Attention with Mobile Vision Transformer (MVIT-MLKA), integrating CNN and Transformer architectures, was developed to classify pancreatic lesions. We compared the model's performance with traditional machine learning and deep learning methods. Moreover, we evaluated radiologists' diagnostic accuracy with and without the optimal model assistance.The MVIT-MLKA model demonstrated superior performance for predicting pancreatic lesions, outperforming traditional models and standard CNNs and Transformers. Radiologists assisted by the MVIT-MLKA model showed significant improvements in diagnostic performance compared to those without model assistance, with notable increases in both accuracy and sensitivity. Model interpretability was enhanced through Grad-CAM visualization, effectively highlighting key lesion areas.The MVIT-MLKA model effectively differentiates between benign and malignant pancreatic lesions, surpassing traditional methods and enhancing radiologist performance. This suggests that integrating advanced deep learning model into clinical practice has the potential to reduce diagnostic errors and optimize treatment strategies in clinical practices.

Inclusion criteria

All patients with malignant pancreatic lesions had confirmed histopathology according to the 8th edition of the American Joint Committee on Cancer TNM staging system [25]; Lesions were classified as benign if they had either histopathologic confirmation or demonstrated benign characteristics with stability over at least one year of follow-up on CT or MRI imaging; (2) Patients underwent preoperative abdominal contrast-enhanced CT scans; (3) No anti-tumor treatment was conducted before the CT scan

Exclusion criteria

(1) Patients with significant motion artifacts or other imaging issues; (2) A time gap of one month or more between the CT scan and subsequent surgery; (3) Tumors less than 10 mm in maximum diameter.