Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography examinations: A multicenter retrospective case-control study.
Authors
Affiliations (10)
Affiliations (10)
- Department of Education, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei 100225, Taiwan.
- Data Science Degree Program, National Taiwan University and Academia Sinica, Taipei 106319, Taiwan.
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106319, Taiwan.
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100225, Taiwan.
- NVIDIA, Santa Clara, CA 95051, USA.
- Division of Gastroenterology, Changhua Christian Hospital, Changhua 500209, Taiwan; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan.
- Department of Medical Image, Shuang Ho Hospital, Taipei Medical University, New Taipei City, 235041, Taiwan.
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100225, Taiwan; Department of Medical Imaging, National Taiwan University Hospital Hsinchu Branch, Hsinchu 300001, Taiwan. Electronic address: [email protected].
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei 100225, Taiwan; Internal Medicine, National Taiwan University College of Medicine, Taipei 100225, Taiwan.
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei 106319, Taiwan.
Abstract
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. 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. This CAD tool demonstrates high diagnostic performance for the detection of PC, including for small PC or clinically unrecognized patients.