Biomarkers for Early Detection of Pancreatic Cancer: A Review

Abstract

Early detection of cancer improves survival chances. Some cancers, such as pancreatic cancer, are hard to identify or detect earlier, and the stages progress aggressively. This review discusses the recent advancements of biomarkers for the early detection of pancreatic cancer. Genomic, protein, blood, and urine biomarkers of pancreatic cancer, as well as corresponding biosensors for diagnosis of pancreatic cancer, have been evaluated, each of these instances show that new biosensors are emerging as an incredibly prominent substitute to defined processes. In order to predict the overall survival of patients with pancreatic ductal adenocarcinoma cancer (PDAC) this review discusses the state-of-the-art machine learning (ML) techniques utilized and a panel of biomarkers for early cancer diagnosis. Recent studies emphasize the significance of machine learning algorithms like support vector machines (SVM), decision tree (DT), naive bayes like algorithms confusing and enormous volumes of data. The phases of the disease and the chance of survival do not significantly correlate. In clinical practice, ML techniques need to undergo the proper level of validation. Pathologists can better manage patients when they have knowledge of the patient’s condition, the surgical procedure to be performed, individualized therapy, the best use of available resources and medications to prescribe due to accurate predictions.