Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning.

ABSTRACT:

Background

Millions of people die from cancer every year. Early cancer detection is crucial for ensuring higher survival rates, as it provides an opportunity for timely medical interventions. This paper proposes a multi-level cancer classification system that uses plasma cfDNA/ctDNA mutations and protein biomarkers to identify seven distinct cancer types: colorectal, breast, upper gastrointestinal, lung, pancreas, ovarian, and liver.

Results

The proposed system employs a multi-stage binary classification framework where each stage is customized for a specific cancer type. A majority vote feature selection process is employed by combining six feature selectors: Information Value, Chi-Square, Random Forest Feature Importance, Extra Tree Feature Importance, Recursive Feature El

SUBMITTER: Eledkawy A 

PROVIDER: S-EPMC11987386 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature