Machine Learning-Based Identification of Blood Biomarkers that Distinguish Precachectic and Cachectic Patients with Pancreatic Ductal Adenocarcinoma.

ABSTRACT:

Background

Identification of minimally invasive biomarkers of different stages of cachexia (Ca), and precachexia (PCa) in particular, might help clinicians in treating patients with pancreatic ductal adenocarcinoma (PDAC) at high risk of progressing to a more severe cachectic stage. In this work, we developed a machine-learning (ML) model optimized to blood biomarkers data that identifies precachectic and cachectic patients.

Methods

Blood and clinical data was collected from treatment-naïve patients with PDAC through the Florida Pancreas Collaborative (FPC), a multi-institutional cohort study and biobanking initiative. Blood was processed into serum and assayed for a total of 35 candidate biomarkers. Participants were classified as having noncachexia (NCa), precachexia, or c

SUBMITTER: Olumoyin KD 

PROVIDER: S-EPMC12755264 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature