Deep Learning–based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT

Background: Deep learning (DL) may facilitate the diagnosis of various pancreatic lesions at imaging. Purpose: To develop and validate a DL-based approach for automatic identification of patients with various solid and cystic pancreatic neoplasms at abdominal CT and compare its diagnostic performance with that of radiologists. Materials and Methods: In this retrospective study, a three-dimensional nnU-Net–based DL model was trained using the CT data of patients who underwent resection for pancreatic lesions between January 2014 and March 2015 and a subset of patients without pancreatic abnormality who underwent CT in 2014. Performance of the DL-based approach to identify patients with pancreatic lesions was evaluated in a temporally independent cohort (test set 1) and a temporally and spatially independent cohort (test set 2) and was compared with that of two board-certified radiologists. Performance was assessed using receiver operating characteristic analysis. Results: The study included 852 patients in the training set (median age, 60 years [range, 19–85 years]; 462 men), 603 patients in test set 1 (median age, 58 years [range, 18–82 years]; 376 men), and 589 patients in test set 2 (median age, 63 years [range, 18–99 years]; 343 men). In test set 1, the DL-based approach had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.94) and showed slightly worse performance in test set 2 (AUC, 0.87 [95% CI: 0.84, 0.89]). The DL-based approach showed high sensitivity in identifying patients with solid lesions of any size (98%–100%) or cystic lesions measuring 1.0 cm or larger (92%–93%), which was comparable with the radiologists (95%–100% for solid lesions [P = .51 to P > .99]; 93%–98% for cystic lesions ≥1.0 cm [P = .38 to P > .99]). Conclusion: The deep learning–based approach demonstrated high performance in identifying patients with various solid and cystic pancreatic lesions at CT.