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

Published Online:Sep 13 2022https://doi.org/10.1148/radiol.220152

See editorial byAlex M. Aisen

Abstract

An automatic end-to-end deep learning–based detection tool could detect pancreatic cancer on CT scans in a nationwide real-world test data set with 91% accuracy, without requiring manual image labeling or preprocessing.

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.

Online supplemental material is available for this article.

See also the editorial by Aisen and Rodrigues in this issue.

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Article History

Received: Mar 5 2022
Revision requested: Apr 22 2022
Revision received: July 15 2022
Accepted: Aug 1 2022
Published online: Sept 13 2022
Published in print: Jan 2023

Vol. 306, No. 1

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Abbreviations

Abbreviations:
AUC	area under the receiver operating characteristic curve
CAD	computer-aided detection
CNN	convolutional neural network
DL	deep learning
LR	likelihood ratio
NHI	National Health Insurance
PC	pancreatic cancer

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