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Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. 2023

Po-Ting Chen, and Tinghui Wu, and Pochuan Wang, and Dawei Chang, and Kao-Lang Liu, and Ming-Shiang Wu, and Holger R Roth, and Po-Chang Lee, and Wei-Chih Liao, and Weichung Wang
From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.).

Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic cancer at CT. Materials and Methods Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test. Results A total of 546 patients with pancreatic cancer (mean age, 65 years ± 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference (P = .11) in sensitivity compared with the original radiologist report (96.1% [98 of 102]; 95% CI: 90.3, 98.9). In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm. Conclusion The deep learning-based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Aisen and Rodrigues in this issue.

UI MeSH Term Description Entries
D008297 Male Males
D010179 Pancreas A nodular organ in the ABDOMEN that contains a mixture of ENDOCRINE GLANDS and EXOCRINE GLANDS. The small endocrine portion consists of the ISLETS OF LANGERHANS secreting a number of hormones into the blood stream. The large exocrine portion (EXOCRINE PANCREAS) is a compound acinar gland that secretes several digestive enzymes into the pancreatic ductal system that empties into the DUODENUM.
D010190 Pancreatic Neoplasms Tumors or cancer of the PANCREAS. Depending on the types of ISLET CELLS present in the tumors, various hormones can be secreted: GLUCAGON from PANCREATIC ALPHA CELLS; INSULIN from PANCREATIC BETA CELLS; and SOMATOSTATIN from the SOMATOSTATIN-SECRETING CELLS. Most are malignant except the insulin-producing tumors (INSULINOMA). Cancer of Pancreas,Pancreatic Cancer,Cancer of the Pancreas,Neoplasms, Pancreatic,Pancreas Cancer,Pancreas Neoplasms,Pancreatic Acinar Carcinoma,Pancreatic Carcinoma,Acinar Carcinoma, Pancreatic,Acinar Carcinomas, Pancreatic,Cancer, Pancreas,Cancer, Pancreatic,Cancers, Pancreas,Cancers, Pancreatic,Carcinoma, Pancreatic,Carcinoma, Pancreatic Acinar,Carcinomas, Pancreatic,Carcinomas, Pancreatic Acinar,Neoplasm, Pancreas,Neoplasm, Pancreatic,Neoplasms, Pancreas,Pancreas Cancers,Pancreas Neoplasm,Pancreatic Acinar Carcinomas,Pancreatic Cancers,Pancreatic Carcinomas,Pancreatic Neoplasm
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000077321 Deep Learning Supervised or unsupervised machine learning methods that use multiple layers of data representations generated by nonlinear transformations, instead of individual task-specific ALGORITHMS, to build and train neural network models. Hierarchical Learning,Learning, Deep,Learning, Hierarchical
D000368 Aged A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available. Elderly
D012189 Retrospective Studies Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons. Retrospective Study,Studies, Retrospective,Study, Retrospective
D012680 Sensitivity and Specificity Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed) Specificity,Sensitivity,Specificity and Sensitivity
D014057 Tomography, X-Ray Computed Tomography using x-ray transmission and a computer algorithm to reconstruct the image. CAT Scan, X-Ray,CT Scan, X-Ray,Cine-CT,Computerized Tomography, X-Ray,Electron Beam Computed Tomography,Tomodensitometry,Tomography, Transmission Computed,X-Ray Tomography, Computed,CAT Scan, X Ray,CT X Ray,Computed Tomography, X-Ray,Computed X Ray Tomography,Computerized Tomography, X Ray,Electron Beam Tomography,Tomography, X Ray Computed,Tomography, X-Ray Computer Assisted,Tomography, X-Ray Computerized,Tomography, X-Ray Computerized Axial,Tomography, Xray Computed,X Ray Computerized Tomography,X Ray Tomography, Computed,X-Ray Computer Assisted Tomography,X-Ray Computerized Axial Tomography,Beam Tomography, Electron,CAT Scans, X-Ray,CT Scan, X Ray,CT Scans, X-Ray,CT X Rays,Cine CT,Computed Tomography, Transmission,Computed Tomography, X Ray,Computed Tomography, Xray,Computed X-Ray Tomography,Scan, X-Ray CAT,Scan, X-Ray CT,Scans, X-Ray CAT,Scans, X-Ray CT,Tomographies, Computed X-Ray,Tomography, Computed X-Ray,Tomography, Electron Beam,Tomography, X Ray Computer Assisted,Tomography, X Ray Computerized,Tomography, X Ray Computerized Axial,Transmission Computed Tomography,X Ray Computer Assisted Tomography,X Ray Computerized Axial Tomography,X Ray, CT,X Rays, CT,X-Ray CAT Scan,X-Ray CAT Scans,X-Ray CT Scan,X-Ray CT Scans,X-Ray Computed Tomography,X-Ray Computerized Tomography,Xray Computed Tomography

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