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Prospective of Pancreatic Cancer Diagnosis Using Cardiac Sensing

J Imaging. 2023 Jul 25;9(8):149. doi: 10.3390/jimaging9080149.

Abstract

Pancreatic carcinoma (Ca Pancreas) is the third leading cause of cancer-related deaths in the world. The malignancies of the pancreas can be diagnosed with the help of various imaging modalities. An endoscopic ultrasound with a tissue biopsy is so far considered to be the gold standard in terms of the detection of Ca Pancreas, especially for lesions <2 mm. However, other methods, like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), are also conventionally used. Moreover, newer techniques, like proteomics, radiomics, metabolomics, and artificial intelligence (AI), are slowly being introduced for diagnosing pancreatic cancer. Regardless, it is still a challenge to diagnose pancreatic carcinoma non-invasively at an early stage due to its delayed presentation. Similarly, this also makes it difficult to demonstrate an association between Ca Pancreas and other vital organs of the body, such as the heart. A number of studies have proven a correlation between the heart and pancreatic cancer. The tumor of the pancreas affects the heart at the physiological, as well as the molecular, level. An overexpression of the SMAD4 gene; a disruption in biomolecules, such as IGF, MAPK, and ApoE; and increased CA19-9 markers are a few of the many factors that are noted to affect cardiovascular systems with pancreatic malignancies. A comprehensive review of this correlation will aid researchers in conducting studies to help establish a definite relation between the two organs and discover ways to use it for the early detection of Ca Pancreas.

Keywords: CT; MRI; cardiac hypertrophy; cardiac sensors; cardiomyopathy; early detection; heart failure; heart remodeling; pancreatic cancer; ultrasound imaging.

Publication types

  • Review

Grants and funding

This work was supported by the Advanced Analytics and Practice Innovation unit for Artificial Intelligence and Informatics research within the Department of Medicine, Mayo Clinic, Rochester, MN USA. This work was also supported by the GIH Division for the GIH Artificial Intelligence Laboratory (GAIL) and Microwave Engineering and Imaging Laboratory (MEIL), Department of Medicine, Mayo Clinic, Rochester, MN USA.