Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications
Open accessAbstract
As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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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.
“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 [7]
Section 3.2 (page unavailable) describes CT radiomics studies distinguishing pancreatic cancer from healthy tissue, while the MRI radiomics studies it discusses distinguish cancer from other lesions or chronic pancreatitis. This is consistent with a gap for the specific MRI comparison, but the review does not explicitly establish a literature-wide lack of such studies. (LLM, conf=0.80)
Experimental — accuracy not yet measuredRecent 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 [7]
Section 3.2 (page unavailable) describes CNNs for different pancreatic lesions and a deep-learning approach that segments abdominal organs before detecting pancreatic cancer. Section 3.1 describes combining radiomic features with deep-learning models, supporting the integration of auxiliary information. The reviewed passages do not establish the claim about transformer-based models, and the cited multi-organ approach detects rather than segments tumors. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFew 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 [7]
The Introduction (page unavailable) identifies a gap in comprehensive reviews of pancreatic-cancer radiomics and deep learning, and Section 3.2 reviews imaging-based detection and diagnosis. Section 2.1 says CT is the most widely used imaging modality, but the paper does not say that most *reviews* focus on CT; that part of the citing sentence is unsupported. (LLM, conf=0.82)
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Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy
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- Authors
- Preuss, K., Thach, N., Liang, X., Baine, M., Chen, J., Zhang, C., Du, H., Yu, H., Lin, C., Hollingsworth, M. A., & Zheng, D.
- Affiliations
- University of Nebraska–Lincoln (US)
- Mayo Clinic (US)
- University of Nebraska Medical Center (US)
- University of Rochester Medicine (US)
- Corresponding author
- Zheng, Dandan
- ORCID
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2022-03-24
- Volume
- 14
- Issue
- 7
- Pages
- 1654
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC8997008
- OpenAlex
- W4220797967
- Cited by
- 69
- References
- 132
- Citations by year
- 2026: 5 · 2025: 25 · 2024: 12 · 2023: 20 · 2022: 7
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lincoln (University of Nebraska) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (2P50CA127297-06A1)
- 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 › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- pancreatic cancer, radiomics, deep learning, quantitative imaging, personalized medicine, radiogenomics, precancerous lesions, early detection, outcome prediction, treatment stratification, imaging phenotypes, multi-omics
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