Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

Authors

Kiersten Preuss, University of Nebraska Medical Center
Nate Thach, University of Nebraska Medical Center
Xiaoying Liang, Mayo Clinic
Michael Baine, University of Nebraska Medical Center
Justin Chen, University of Nebraska Medical Center
Chi Zhang, University of Nebraska Lincoln
Huijing Du, University of Nebraska Lincoln
Hongfeng Yu, University of Nebraska Lincoln
Chi Lin, University of Nebraska Medical Center
Michael A. Hollingsworth, University of Nebraska Medical Center
Dandan Zheng, University of Nebraska Medical Center, University of Rochester

Document Type

Article

Date of this Version

3-24-2022

Citation

Preuss, K.; Thach, N.; Liang, X.; Baine, M.; Chen, J.; Zhang, C.; Du, H.; Yu, H.; Lin, C.; Hollingsworth, M.A.; et al. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers 2022, 14, 1654. https://doi.org/10.3390/ cancers14071654

Comments

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses

Abstract

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.