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Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
- Source :
- Radiology. 306(1)
- Publication Year :
- 2022
-
Abstract
- 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 (
- Subjects :
- Radiology, Nuclear Medicine and imaging
Subjects
Details
- ISSN :
- 15271315
- Volume :
- 306
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Radiology
- Accession number :
- edsair.doi.dedup.....320a40cdf3b406953f973ed851cc6fa3