Mathematical Modeling to Address Challenges in Pancreatic Cancer

Curr Top Med Chem. 2020;20(5):367-376. doi: 10.2174/1568026620666200101095641.

Abstract

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for 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 multidisciplinary approach 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 of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.

Keywords: Cancer; Carcinoembryonic Antigen (CEA); Desmoplasia; Mathematical modeling; Numerical simulation; Pancreatic ductal adenocarcinoma..

MeSH terms

  • Antineoplastic Agents / pharmacology
  • Apoptosis / drug effects
  • Carcinoma, Pancreatic Ductal / diagnosis*
  • Carcinoma, Pancreatic Ductal / drug therapy
  • Cell Proliferation / drug effects
  • Humans
  • Models, Statistical*
  • Pancreatic Neoplasms / diagnosis*
  • Pancreatic Neoplasms / drug therapy

Substances

  • Antineoplastic Agents