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Cited by 222AI in cancer detectionRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation

Open access
Liu, Kao-Lang, Wu, Tinghui, Chen, Po-Ting, Tsai, Yuhsiang M, Roth, Holger, Wu, Ming-Shiang, Liao, Wei-Chih, Wang, Weichung·2020·The Lancet Digital Health
DOI 10.1016/s2589-7500(20)30078-9
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Abstract

BACKGROUND: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks' potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation. METHODS: In this retrospective, diagnostic study, contrast-enhanced CT images of 370 patients with pancreatic cancer and 320 controls from a Taiwanese centre were manually labelled and randomly divided for training and validation (295 patients with pancreatic cancer and 256 controls) and testing (75 patients with pancreatic cancer and 64 controls; local test set 1). Images were preprocessed into patches, and a CNN was trained to classify patches as cancerous or non-cancerous. Individuals were classified as with or without pancreatic cancer on the basis of the proportion of patches diagnosed as cancerous by the CNN, using a cutoff determined using the training and validation set. The CNN was further tested with another local test set (101 patients with pancreatic cancers and 88 controls; local test set 2) and a US dataset (281 pancreatic cancers and 82 controls). Radiologist reports of pancreatic cancer images in the local test sets were retrieved for comparison. FINDINGS: Between Jan 1, 2006, and Dec 31, 2018, we obtained CT images. In local test set 1, CNN-based analysis had a sensitivity of 0·973, specificity of 1·000, and accuracy of 0·986 (area under the curve [AUC] 0·997 (95% CI 0·992-1·000). In local test set 2, CNN-based analysis had a sensitivity of 0·990, specificity of 0·989, and accuracy of 0·989 (AUC 0·999 [0·998-1·000]). In the US test set, CNN-based analysis had a sensitivity of 0·790, specificity of 0·976, and accuracy of 0·832 (AUC 0·920 [0·891-0·948)]. CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011-0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1-1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0-3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set. INTERPRETATION: CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation. FUNDING: Taiwan Ministry of Science and Technology.

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Metadata

Authors
Liu, K.-L., Wu, T., Chen, P.-T., Tsai, Y. M., Roth, H., Wu, M.-S., Liao, W.-C., & Wang, W.
Affiliations
  • National Taiwan University (TW)
  • Nvidia (United States) (US)
Corresponding author
  • Liao, Wei-Chih
  • Wang, Weichung
ORCID
Venue
The Lancet Digital Health
Publisher
Elsevier BV
Published
2020-06
Volume
2
Issue
6
Pages
e303-e313
ISSN
2589-7500 (ISSN-L)
DOI
10.1016/s2589-7500(20)30078-9
OpenAlex
W3030432638
Cited by
222
References
42
Citations by year
2026: 28 · 2025: 38 · 2024: 33 · 2023: 46 · 2022: 39 · 2021: 32 · 2020: 6
FWCI
12.09
Type
journal article
Language
en
Open access
Licence
Locations
  • The Lancet Digital Health (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Ministry of Science and Technology (107-2634-F-002-014-, 107-2634-F-002-016-, 108-2634-F-002-011-, 108-2634-F-002-013-, 109-2634-F-002-028-)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
pancreatic cancer, computer-aided diagnosis, convolutional neural networks, contrast-enhanced CT, image classification, diagnostic accuracy, early cancer detection, image patches, United States, artificial intelligence in radiology, radiology workforce shortage, radiology residency
MeSH terms
Deep Learning, Aged, Contrast Media, Diagnosis, Differential, Female, Humans, Male, Middle Aged, Pancreas, Pancreatic Neoplasms, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, Sensitivity and Specificity, Taiwan, Tomography, X-Ray Computed, Reproducibility of Results, Racial Groups

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