Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography examinations: A multicenter retrospective case-control study.

PURPOSE: The purpose of this study was to develop and validate a computer-aided detection (CAD) tool for the detection of pancreatic cancer (PC) on diagnostic and prediagnostic computed tomography (CT) examinations. MATERIALS AND METHODS: A CAD tool was developed using 2496 contrast-enhanced CT images (596 PCs, 1335 normal pancreas, 565 other pancreatic diseases) from a referral center (October 2004-December 2019) and underwent external validation at two independent institutions (January 2018-December 2020) in a retrospective case-control design. Prediagnostic CT images obtained one to 12 months before the clinical diagnosis of PC, representing clinically challenging or missed images, were collected (November 2004-August 2022) from three referral centers to further evaluate the performance of the CAD tool. Classification performance of the CAD tool was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), RESULTS: From internal and external datasets, the diagnostic test sets included 200 PCs and 4998 controls of 4744 patients with normal pancreas and 254 patients with other pancreatic diseases (2448 women and 2750 men; median age, 63 years; age range: 18-101). The CAD tool achievedan AUC of 0.950 (95 % confidence interval [CI]: 0.932-0.968), 90.0 % sensitivity (180 out of 200; 95 % CI: 85.0-93.8), and 87.8 % specificity (4389 out of 4998; 95 % CI: 86.9-88.7) in the diagnosis of PC. For prediagnostic test sets, which included 54 PCs and 118 controls of 89 patients with normal pancreas and 19 patients with other pancreatic diseases (63 women and 99 men; median age, 61 years; age range: 18-99), the sensitivity was 66.7 % (36 out of 54; 95 % CI: 52.5-78.9). Sensitivities for PCs ≤ 2 cm were 77.1 % (27 out of 35; 95 % CI: 59.9-89.6) and 66.7 % (14 out of 21; 95 % CI: 43.0-85.4) in diagnostic and prediagnostic test sets, respectively. CONCLUSION: This CAD tool demonstrates high diagnostic performance for the detection of PC, including for small PC or clinically unrecognized patients.