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.

Keywords: deep learning; machine learning; pancreatic cancer; quantitative imaging; radiomics.

Figure 1

A schematic drawing comparing the radiomics approach and the deep learning approach, using an example case of tumor detection.

Figure 2

Quantitative imaging studies using radiomics and deep learning methods in pancreatic cancer-related research by year of publication.

Figure 3

In a typical radiomics workflow, medical images are acquired and curated and volumes of interest (VOIs) such as pancreatic tumors are segmented (A). From the segmented VOI images, hundreds to thousands of radiomic features are then be extracted (B). After conducting preliminary radiomics analysis such as feature selection (C) and possibly adding clinical and biological information (C1), all features can be integrated through advanced statistical and/or machine learning methods to develop predictive models (D). The model accuracy and robustness can then be evaluated on validation and testing datasets (E).