Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma.

ABSTRACT: Chloride channel accessory 1 (CLCA1) is considered a potential prognostic biomarker for colon adenocarcinoma (COAD). The objective of this research was to develop two pathomics models to predict CLCA1 expression from hematoxylin-eosin (H&E) stained pathological images and to investigate the biological mechanisms linked to pathomics features by associating the pathomics model with transcriptomic data. The prognostic value of CLCA1 in COAD was assessed based on gene transcriptome expression data. The two pathomics models were constructed to predict CLCA1 expression in COAD based on pathological image features using the random forest (RF) and XGBoost machine learning algorithms. The RF pathomics model demonstrated superior predictive performance, achieving area under the curve (AUC) values of

SUBMITTER: Yao C 

PROVIDER: S-EPMC12279125 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature