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

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

Radiogenomics: Combining SNP Studies with Pancreatic Ductal Adenocarcinoma Medical Images

Radiogenomics is a fusion of two methods, radiomics, and genomics. Radiomics is the process in which features are extracted from medical images such as MRIs, CT scans, and PET scans. Genomics on the other hand is studying an organism\u27s genome and, in this study, their respective gene mutations. By combining both methods, radiogenomics can help find biomarkers for various cancers to help diagnose and treat patients more efficiently. In this study, Radiogenomics was used to find an association between genomic profiles and imaging features of patients with pancreatic ductal adenocarcinoma (PDAC). Out of a total of 117 patients available, only 29 patients were selected for the study. The study required that the patients must have a complete genetic profile available, a preoperative CT scan, PDAC, and are participating in the Rapid Autopsy Program (RAP).https://digitalcommons.unmc.edu/surp2021/1001/thumbnail.jp

Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions

Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers

The integration of artificial intelligence models to augment imaging modalities in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a limited number of effective treatments. Using emerging technologies such as artificial intelligence (AI) to facilitate the earlier diagnosis and decision-making process represents one of the most promising areas for investigation. The integration of AI models to augment imaging modalities in PDAC has made great progression in the past 5 years, especially in organ segmentation, AI-aided diagnosis, and radiomics based individualized medicine. In this article, we review the developments of AI in the field of PDAC and the present clinical position. We also examine the barriers to future development and more widespread application which will require increased familiarity of the underlying technology among clinicians to promote the necessary enthusiasm and collaboration with computer professionals

Novel Detection and Treatment Strategies for Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with an estimated 5-year survival rate of less than 9%. The high lethality of PDAC is due to two primary reasons: the discovery of PDAC at later stages, with locally invasive or metastatic disease present at the time of initial diagnosis as well as the lack of efficacious therapeutic interventions that significantly impact survival. In this dissertation, we sought to discover and test novel detection and treatment strategies for PDAC. Firstly, serum EVs were investigated as potential non-invasive liquid biopsy biomarkers, to serve as a means of early cancer detection. Secondly, a recently discovered form of cell death, ferroptosis, was investigated as a means of potentiating radiation therapy. The investigation into the potential of extracellular vesicles (EVs) as circulating biomarkers began with a label-free analysis of EVs via surface-enhanced Raman Spectroscopy (SERS) and principal component discriminant function analysis (PC-DFA), to identify tumor-specific spectral signatures. This method differentiated EVs originating from PDAC or normal pancreatic epithelial cell lines with 90% overall accuracy. The proof-of-concept application of this method to EVs purified from patient serum exhibited up to 87% and 90% predictive accuracy for healthy control and early PDAC individual samples, respectively. The specific EV surface proteins that may contribute to the observed SERS differences were investigated via surface shaving LC-MS/MS. This analysis provided protein targets that were selected and validated with a combination of bioinformatics, western blot, and immunogold labeling techniques. The first target protein selected for assessment via ELISA, EPHA2, showed elevated expression in complete cancer patient serum as compared to benign controls. Further, EV specific EPHA2 expression was capable of predicting cancer status in 25% (5/20) of the patient samples with 100% specificity. These data suggest a potential role of EV surface profiling for the early detection of PDAC. However, further work is required to increase the overall accuracy. Additionally, we sought to investigate the involvement of ferroptosis, in radiation-induced cell death. Ferroptosis is a non-apoptotic form of cell death that requires labile ferrous iron (Fe2+) and is caused by the reactive oxygen species (ROS) mediated build-up of lipid hydroperoxides. Further, we tested if the pharmaceutical induction of ferroptosis via the small molecule Erastin can potentiate the lethal effects of radiation in vitro and in vivo. We observed that radiation produces an increase in ROS and free Fe2+ leading to lipid hydroperoxidation, which was enhanced with the addition of Erastin culminating in the likely induction of ferroptosis. The combination of radiation and Erastin synergistically increased cell death in monocultures and patient-derived organoids as well as significantly reduced tumor size in xenograft mouse models. These findings suggest the potential of ferroptosis induction to improve radiation therapy, though specific mechanistic components require further evaluation. Therefore, further studies must be conducted to elucidate the specific role of ferroptosis in radiation-induced cell death. The combination of early detection and novel therapeutic intervention strategies offers a means of improving the survival of those with this dreaded disease