Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer typesfor its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents,thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinaryapproach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis,and treatment. To this end, the application of quantitative tools can help improve the understanding ofdisease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategiesto improve therapeutic outcomes. However, such approaches have only been minimally applied towardsthe investigation of PDAC, and we review the current status of mathematical modeling works inthis field.
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Biomarkers in Cancer Detection, Diagnosis, and Prognosis
Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).
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- Award ID(s):
- 2045640
- PAR ID:
- 10507421
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 24
- Issue:
- 1
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 37
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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