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.

Damo Academy Alibaba Group Hangzhou China

DAMO Academy Alibaba Group New York NY USA

Department of Biostatistics Harvard University T H Chan School of Public Health Cambridge MA USA

Department of Computer Science Johns Hopkins University Baltimore MD USA

Department of Hepatobiliary and Pancreatic Surgery 1st Affiliated Hospital of Zhejiang University Hangzhou China

Department of Invasive Cardiology 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Department of Oncology 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Department of Pathology Shanghai Institution of Pancreatic Disease Shanghai China

Department of Radiology 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Department of Radiology Fudan University Shanghai Cancer Center Shanghai China

Department of Radiology Guangdong Provincial People's Hospital Guangzhou China

Department of Radiology Shanghai Institution of Pancreatic Disease Shanghai China

Department of Radiology Shengjing Hospital of China Medical University Shenyang China

Department of Radiology Sun Yat Sen University Cancer Center Guangzhou China

Department of Radiology Tianjin Medical University Cancer Institute and Hospital Tianjin China

Department of Radiology Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

Department of Surgery Shanghai Institution of Pancreatic Disease Shanghai China

Hupan Laboratory Hangzhou China

See more in PubMed

Sung H, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021;71:209–249. PubMed

Vasen H, et al. Benefit of surveillance for pancreatic cancer in high-risk individuals: outcome of long-term prospective follow-up studies from three European expert centers. J. Clin. Oncol. 2016;34:2010–2019. PubMed

Singhi AD, Koay EJ, Chari ST, Maitra A. Early detection of pancreatic cancer: opportunities and challenges. Gastroenterology. 2019;156:2024–2040. PubMed PMC

Pereira SP, et al. Early detection of pancreatic cancer. Lancet Gastroenterol. Hepatol. 2020;5:698–710. PubMed PMC

Klatte DCF, et al. Pancreatic cancer surveillance in carriers of a germline CDKN2A pathogenic variant: yield and outcomes of a 20-year prospective follow-up. J. Clin. Oncol. 2022;40:3267–3277. PubMed

Dbouk M, et al. The multicenter Cancer of Pancreas Screening study: impact on stage and survival. J. Clin. Oncol. 2022;40:3257–3266. PubMed PMC

Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J. Clin. 2021;71:7–33. PubMed

Gonda TA, et al. Recommendations for a more organized and effective approach to the early detection of pancreatic cancer from the PRECEDE (Pancreatic Cancer Early Detection) Consortium. Gastroenterology. 2021;161:1751–1757. PubMed

Grossberg AJ, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J. Clin. 2020;70:375–403. PubMed PMC

Lucas AL, Kastrinos F. Screening for pancreatic cancer. JAMA. 2019;322:407–408. PubMed

Sodickson A, et al. Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults. Radiology. 2009;251:175–184. PubMed

Esteva A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–118. PubMed PMC

De Fauw J, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 2018;24:1342–1350. PubMed

Ardila D, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 2019;25:954–961. PubMed

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019;25:44–56. PubMed

McKinney SM, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577:89–94. PubMed

Lotter W, et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat. Med. 2021;27:244–249. PubMed PMC

Preetha CJ, et al. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit. Health. 2021;3:784–794. PubMed

Zhang Q, et al. Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue characterization in hypertrophic cardiomyopathy. Circulation. 2021;144:589–599. PubMed PMC

Ounkomol C, Seshamani S, Maleckar MM, Collman F, Johnson GR. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods. 2018;15:917–920. PubMed PMC

Rivenson Y, et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 2019;3:466–477. PubMed

Pickhardt PJ. Value-added opportunistic CT screening: state of the art. Radiology. 2022;303:241–254. PubMed PMC

Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 2021;18:203–211. PubMed

Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. In 9th International Conference on Learning Representations (2021).

Wang, H., Zhu, Y., Adam, H., Yuille, A. & Chen, L.-C. Max-deeplab: end-to-end panoptic segmentation with mask transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5463–5474 (2021).

Cohen JD, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science. 2018;359:926–930. PubMed PMC

Rajpurkar P, Lungren MP. The current and future state of AI interpretation of medical images. N. Engl. J. Med. 2023;388:1981–1990. PubMed

To’o KJ, et al. Pancreatic and peripancreatic diseases mimicking primary pancreatic neoplasia. Radiographics. 2005;25:949–965. PubMed

Balaur E, et al. Colorimetric histology using plasmonically active microscope slides. Nature. 2021;598:65–71. PubMed

Park HJ, et al. Deep learning-based detection of solid and cystic pancreatic neoplasms at contrast-enhanced CT. Radiology. 2023;306:140–149. PubMed

Liu K-L, et al. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit. Health. 2020;2:303–313. PubMed

LeBlanc, M., Kang, J. & Costa, A. F. Can we rely on contrast-enhanced CT to identify pancreatic ductal adenocarcinoma? A population-based study in sensitivity and factors associated with false negatives. Eur. Radiol.10.1007/s00330-023-09758-y (2023). PubMed

Marya NB, et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis. Gut. 2021;70:1335–1344. PubMed

Xia, Y. et al. The FELIX project: deep networks to detect pancreatic neoplasms. Preprint at bioRxiv10.1101/2022.09.24.22280071 (2022).

Klein E, et al. Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann. Oncol. 2021;32:1167–1177. PubMed

Fahrmann JF, et al. Lead-time trajectory of CA19-9 as an anchor marker for pancreatic cancer early detection. Gastroenterology. 2021;160:1373–1383. PubMed PMC

Lehman CD, et al. National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology. 2017;283:49–58. PubMed PMC

Imperiale TF, et al. Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 2014;370:1287–1297. PubMed

Pinsky PF, et al. Performance of Lung-RADS in the National Lung Screening Trial: a retrospective assessment. Ann. Intern. Med. 2015;162:485–491. PubMed PMC

Goggins M, et al. Management of patients with increased risk for familial pancreatic cancer: updated recommendations from the International Cancer of the Pancreas Screening (CAPS) Consortium. Gut. 2020;69:7–17. PubMed PMC

Springer S, et al. A multimodality test to guide the management of patients with a pancreatic cyst. Sci. Transl. Med. 2019;11:eaav4772. PubMed PMC

Yao, J. et al. Effective opportunistic esophageal cancer screening using noncontrast CT imaging. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2022 (eds Wang, L. et al.), Lecture Notes in Computer Science, Vol. 13433, pp. 344–354 (Springer, 2022).

Yan, K. et al. Liver tumor screening and diagnosis in CT with pixel-lesion-patient network. In International Conference on Medical Image Computing and Computer-Assisted Intervention in press (2023).

Yuan, M. et al. Cluster-induced mask transformers for effective opportunistic gastric cancer screening on non-contrast CT scans. In International Conference on Medical Image Computing and Computer-Assisted Intervention in press (2023).

Chu LC, et al. Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists. Abdom. Radiol. 2022;47:4139–4150. PubMed PMC

Yao J, et al. Deep learning for fully automated prediction of overall survival in patients undergoing resection for pancreatic cancer: a retrospective multicenter study. Ann. Surg. 2023;278:68–79. PubMed

Xia, Y. et al. Effective pancreatic cancer screening on non-contrast CT scans via anatomy-aware transformers. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2021 (eds de Bruijne, M et al.), Lecture Notes in Computer Science, Vol. 12905, pp. 259–269 (Springer 2021).

Luo H, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20:1645–1654. PubMed

Jin C, et al. Predicting treatment response from longitudinal images using multi-task deep learning. Nat. Commun. 2021;12:1851. PubMed PMC

Yushkevich PA, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31:1116–1128. PubMed

Qian X, et al. Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat. Biomed. Eng. 2021;5:522–532. PubMed

Lu MY, et al. AI-based pathology predicts origins for cancers of unknown primary. Nature. 2021;594:106–110. PubMed

Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV) 618–626 (IEEE, 2017).

Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?