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Cited by 358AI in cancer detectionRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

Large-scale pancreatic cancer detection via non-contrast CT and deep learning

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Cao, Kai, Xia, Yingda, Yao, Jiawen, Han, Xu, Lambert, Lukas, Zhang, Tingting, Tang, Wei, Jin, Gang +28 more·2023·Nature Medicine
DOI 10.1038/s41591-023-02640-wPMC PMC10719100
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Abstract

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

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  • Recent advances include the application of deep learning techniques like CNNs, transformer-based models, and new architectures that focus on handling various types of pancreatic lesions, segmenting multiple organs and tumors, and integrating auxiliary information.

    “Recent advances include the application of deep learning techniques like convolutional neural networks (CNNs), transformer-based models, and new architectures that focus on handling various types of pancreatic lesions, segmenting multiple organs and tumors, and integrating auxiliary information [7,8,9,24,25,26].”

    From Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning (2025) · cites this paper as [25]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Few reviews discuss the latest developments in AI for pancreatic cancer diagnosis using cross-sectional imaging techniques, with most focusing on CT.

    “Few reviews discuss the latest developments in AI for pancreatic cancer diagnosis using cross-sectional imaging techniques, with most focusing on CT [7,8,9,25,26,27].”

    From Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning (2025) · cites this paper as [25]

    The Main section discusses AI-based pancreatic cancer detection using CT, but does not assess how many reviews cover cross-sectional imaging or whether most focus on CT. Searches for reviews of AI in pancreatic imaging and for a CT focus found no passage addressing that comparison; page numbers were not available in our copy. (LLM, conf=0.72)

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Authors
Cao, K., Xia, Y., Yao, J., Han, X., Lambert, L., Zhang, T., Tang, W., Jin, G., Jiang, H., Fang, X., Nogues, I., Li, X., Guo, W., Wang, Y., Fang, W., Qiu, M., Hou, Y., Kovarnik, T., Vocka, M., Lu, Y., Chen, Y., Chen, X., Liu, Z., Zhou, J., Xie, C., Zhang, R., Lu, H., Hager, G. D., Yuille, A. L., Lu, L., Shao, C., Shi, Y., Zhang, Q., Liang, T., Zhang, L., & Lu, J.
Affiliations
  • Alibaba DAMO Academy (China) (CN)
  • First Affiliated Hospital Zhejiang University (CN)
  • Shanghai Jiao Tong University (CN)
  • Fudan University Shanghai Cancer Center (CN)
  • Harvard University (US)
  • Sheng Jing Hospital (CN)
  • Guangdong Provincial People's Hospital (CN)
  • Sun Yat-sen University Cancer Center (CN)
  • Tianjin Medical University Cancer Institute and Hospital (CN)
  • Johns Hopkins University (US)
  • Alibaba Group (United States) (US)
Corresponding author
  • Shao, Chengwei
  • Shi, Yu
  • Zhang, Qi
  • Liang, Tingbo
  • Zhang, Ling
  • Lu, Jianping
ORCID
Venue
Nature Medicine
Publisher
Springer Science and Business Media LLC
Published
2023-11-20
Volume
29
Issue
12
Pages
3033-3043
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
DOI
10.1038/s41591-023-02640-w
PMC
PMC10719100
OpenAlex
W4388834713
Cited by
358
References
57
Citations by year
2026: 119 · 2025: 162 · 2024: 74 · 2023: 2 · 2021: 1
FWCI
33.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (82372045)
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 ductal adenocarcinoma, pancreatic cancer detection, non-contrast CT, deep learning, computer-aided detection, cancer screening, radiologist performance comparison, PANDAS
MeSH terms
Deep Learning, Artificial Intelligence, Humans, Pancreas, Pancreatic Neoplasms, Retrospective Studies, Tomography, X-Ray Computed, Carcinoma, Pancreatic Ductal

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