What does this research mean for the field?

Quantitative histologic features extracted from routine H&E whole slide images using the CHAI platform provide significant, independent prognostic value for survival across multiple solid tumor types. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This work aims to assess the prognostic value of quantitative histologic features for oncology biomarkers using computational histology.

May 30, 2026

Histomics as an emerging avenue for oncology biomarker development: Computational Histology Artificial Intelligence (CHAI) analysis of The Cancer Genome Atlas (TCGA).

Key Points

This work aims to assess the prognostic value of quantitative histologic features for oncology biomarkers using computational histology.
Analyzed >30,000 histomic features from H&E stained whole slide images of nonmetastatic solid tumors from TCGA.
Cohorts divided into development (30%) and validation (70%) sets, stratified by cancer type and stage.
Cox multivariable analysis assessed the relationship between CHAI biomarkers and clinical outcomes.
Among 6117 TCGA cases, 35% were identified as biomarker (+), associated with worse survival rates.
Hazard ratios for biomarker (+) ranged between 1.66 and 3.25 across multiple disease sites.
Findings affirm histomics as a promising approach for biomarker development in various solid tumors (p≤0.02).

Abstract

e15008 Background: Biomarkers are critical to precision oncology. The emerging modality of “histomics” is predicated upon the analysis of quantitative histologic features from routine hematoxylin a cut point dichotomized each case into biomarker (+) or (-), with (+) indicating higher risk. The models then the 20 cancer types were grouped into 9 disease sites to simplify reporting. Cox multivariable analysis (MVA) was used to assess associations between the CHAI biomarkers & clinical outcomes. Results: 6117 nonmetastatic TCGA cases had available H&E WSI: 1795 dev and 4322 val. CHAI processed >7000 H&E WSIs, classifying ~2 billion cells and >200 billion μm2 tissue to calculate biomarker scores. A median of 35% of cases were biomarker (+). CHAI biomarker (+) remained significantly associated with worse survival for each disease site after controlling for available clinicopathologic variables including age, sex, and stage with hazard ratios ranging from 1.66-3.25 (Table). Conclusions: The CHAI histomics platform quantified histologic features of prognostic value across cancer types (p≤0.02) from TCGA via cancer-specific histologic signatures. These findings support histomics as a modality for oncology biomarker development in solid tumors. CHAI validation results by disease site in TCGA. Biomarker(+): higher risk. Disease site TCGA cancer type N Biomarker(+) (%) MVA HR 95% CI p GI Luminal COAD, READ, STAD, ESCA 695 262 (38) 1.66 1.22, 2.26 <0.01 Hepatobiliary PAAD, LIHC, CHOL 399 140 (35) 1.72 1.29, 2.30 <0.01 GU PRAD, BLCA, KIRP, KIRC, KICH 989 272 (27) 2.00 1.45, 2.75 <0.01 Lung LUAD, LUSC 621 230 (37) 1.73 1.31, 2.29 <0.01 GYN CESC, OV 235 49 (17) 2.57 1.43, 4.62 <0.01 Endocrine THCA 317 64 (20) 2.79 1.40, 5.57 <0.01 Breast BRCA 643 59 (9) 2.42 1.19, 4.90 0.01 H&N HNSC 140 52 (37) 3.25 1.52, 6.94 0.02 Skin SKCM 280 114 (40) 2.25 1.56, 3.24 <0.01

Ask AI

Helpful

Bookmark