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

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.

Role of CA 19.9 in the Management of Resectable Pancreatic Cancer: State of the Art and Future Perspectives

“…Nonetheless, it has to be considered that interesting perspectives are emerging, due to recent advances in multiomics, that may lead to the discovery of other biomarkers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. In this scenario, panels of markers combining CA 19.9 with other novel biomarkers from different “omics” levels are showing promising results in pancreatic cancer early detection [ 46 ] and in advancing its precision management [ 47 ].…”

Artificial intelligence assists precision medicine in cancer treatment

“…It can predict patient viability based on imaging features and determine the level of treatment needed to achieve optimal survival. The prediction of recurrence, metastasis, surgical margins and therapeutic responses can be used to formulate an optimal therapeutic strategy for individual patients ( 21 , 26 ).…”

“…Quantitative image analysis is a suitable candidate for PM and can assist PM for cancer. ML and DL have been used for quantitatively extracting image features to establish models for diagnosis, monitoring, and predicting recurrence and metastasis, biomarkers and prognosis (17)(18)(19)(20)(21). AI can integrate the above data for comprehensive analysis of tumors for the development of a clinical decision support system (DSS) (22).…”

Biological functions, mechanisms, and clinical significance of circular RNA in pancreatic cancer: a promising rising star

“…Currently, noninvasive enhanced computed tomography (CT) is recommended as the preferred diagnostic method, which can not only determine the size of the tumor but can also aid in the evaluation of resectability. Nevertheless, the sensitivity for tumors ≤ 2 cm is only 77%, and the diagnostic efficacy for patients with distant metastases is not as good as that of more expensive PET-CT imaging [ 116 ]. In humoral testing, CA19-9 is the only biomarker approved by the Food and Drug Administration (FDA) for diagnostic use [ 117 ].…”