The DOI matches its Crossref record; 41 of the 43 references it cites were found in a registry or an index.
A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging
Open accessAbstract
Purpose of review: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Recent findings: This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. Summary: Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.
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“To the best of our knowledge, until now there has been a lack of studies in the literature on using MRI radiomics data for the differentiation of pancreatic adenocarcinoma from normal pancreatic tissue [7,8,9].”
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 [8]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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 [8]
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In the citing paper
“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].”
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1 retracted
Althobaiti, M. M., Almulihi, A. H., Ashour, A. A., Mansour, R. F., & Gupta, D. K. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/2872461 · Cited by 27
RetractedPredicting cancer outcomes with radiomics and artificial intelligence in radiology
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00560-7 · Cited by 819
Hussein, S., Kandel, P., Bolan, C. W., Wallace, M. B., & Bagci, U. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2894349 · Cited by 285
Screening for Pancreatic Cancer: Why, How, and Who?
Poruk, K. E., Firpo, M. A., Adler, D. G., & Mulvihill, S. J. (2013) · Annals of Surgery · doi:10.1097/sla.0b013e31825ffbfb · Cited by 249
Liu, K.-L., Wu, T., Chen, P.-T., Tsai, Y. M., Roth, H., Wu, M.-S., Liao, W.-C., & Wang, W. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30078-9 · Cited by 222
Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
Chen, P., Wu, T., Wang, P., Chang, D., Liu, K., Wu, M., Roth, H. R., Lee, W., Liao, W., & Wang, W. (2022) · Radiology · doi:10.1148/radiol.220152 · Cited by 203
Mukherjee, S., Patra, A., Khasawneh, H., Korfiatis, P., Rajamohan, N., Suman, G., Majumder, S., Panda, A., Johnson, M. P., Larson, N. B., et al. (2022) · Gastroenterology · doi:10.1053/j.gastro.2022.06.066 · Cited by 187
Intraductal papillary mucinous neoplasm of pancreas
Machado, N., al Qadhi, H., & al Wahibi, K. (2015) · North American Journal of Medical Sciences · doi:10.4103/1947-2714.157477 · Cited by 149 · cited 1×
“DL can be used for tumor segmentation, crucial for diagnosis and prognostic purposes because the size, shape, and location of the tumor are critical in such tasks.”
MethodTonozuka, R., Itoi, T., Nagata, N., Kojima, H., Sofuni, A., Tsuchiya, T., Ishii, K., Tanaka, R., Nagakawa, Y., & Mukai, S. (2021) · Journal of Hepato-Biliary-Pancreatic Sciences · doi:10.1002/jhbp.825 · Cited by 129
Fully end-to-end deep-learning-based diagnosis of pancreatic tumors
Si, K., Xue, Y., Yu, X., Zhu, X., Li, Q., Gong, W., Liang, T., & Duan, S. (2021) · Theranostics · doi:10.7150/thno.52508 · Cited by 120
Flagged first, then by citations
2 of the 43 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Schulz, 2022, Deep learning can accurately distinguish between low grade dysplasia and high grade dysplasia/invasive carcinoma in IPMN by unilizing endosonographic images, Endoscopy, 54, e421
- Mukherjee, 2022, Radiomics-based machine learning models can detect pancreatic cancer on prediagnostic CTs at a substantial lead time prior to clinical diagnosis, Gastroenterology, 163, 1435, 10.1053/j.gastro.2022.06.066 · DOI · Paper page
Cited by
Sorted by citations
From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer
Tripathi, S., Tabari, A., Mansur, A., Dabbara, H., Bridge, C. P., & Daye, D. (2024) · Diagnostics · doi:10.3390/diagnostics14020174 · Cited by 70
Artificial Intelligence in Pancreatic Imaging: A Systematic Review
Podină, N., Gheorghe, E. C., Constantin, A., Cazacu, I., Croitoru, V., Gheorghe, C., Balaban, D. V., Jinga, M., Țieranu, C. G., & Săftoiu, A. (2025) · United European Gastroenterology Journal · doi:10.1002/ueg2.12723 · Cited by 48
Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations
Seyithanoğlu, D., Durak, G., Keleş, E., Medetalibeyoğlu, A., Hong, Z., Zhang, Z., Taktak, Y. B., Cebeci, T., Tiwari, P., Velichko, Y., et al. (2024) · Cancers · doi:10.3390/cancers16244268 · Cited by 23
Self-citationArtificial intelligence techniques in liver cancer
Wang, L., Fatemi, M., & Alizad, A. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1415859 · Cited by 22
Hu, Z., Ma, L., Ding, Y., Zhao, X.-X., Shi, X., Lu, H., & Liu, K. (2023) · Cancers · doi:10.3390/cancers15215281 · Cited by 14
Metadata
- Authors
- Yao, L., Zhang, Z., Keles, E., Yazici, C., Tirkes, T., & Bagci, U.
- Affiliations
- Northwestern University (US)
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
- Division of Gastroentrrology and Hepatology, University of Illinois Chicago, Chicago, Illinois
- Indiana University Indianapolis (US)
- Venue
- Current Opinion in Gastroenterology
- Published
- 2023-07-18
- Volume
- 39
- Issue
- 5
- Pages
- 436-447
- ISSN
- 0267-1379 (ISSN-L)
- 1531-7056
- PMC
- PMC10403281
- OpenAlex
- W4385407083
- Cited by
- 53
- References
- 43
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 12 · 2023: 2
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Gastroenterology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health
- National Cancer Institute (R01CA246704, R01CA240639)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- pancreatic cancer, computer-aided diagnosis, deep learning, radiomics, convolutional neural networks, transformer models, organ segmentation, clinical decision support systems, CT, magnetic resonance imaging, prognostic factors, translational medicine
- MeSH terms
- Deep Learning, Artificial Intelligence, Humans, Pancreas, Pancreatic Neoplasms, Tomography, X-Ray Computed
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