Evidence map›Paper›PMID 42898111›Full record

ArticleFrontiers in oncology2026

Genetic risk stratification reveals distinct first-order radiomic phenotypes in hepatocellular carcinoma.

Mohamed Eltaher, Zaina Boriek, Rohan Amare, Awj Twam, Prasun Jalal, James Long, Eugene Koay, Khaled M Elsayes, Rikita Hatia, Manal Hassan and 1 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Mohamed EltaherThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Zaina BoriekThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Rohan AmareThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Awj TwamThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Prasun JalalBaylor College of Medicine, Houston, TX, United States.
James LongThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Eugene KoayThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Khaled M ElsayesThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Rikita HatiaThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Manal HassanThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.
David FuentesThe University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Funding

Early Detection of Hepatocellular CarcinomaR01CA195524 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAURA BERETTA, David Fuentes · 2016 to 2026
$6.5M
Delivery optimization of a transarterial ablative therapyR01CA301555 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI ERIK N CRESSMAN, David Fuentes · 2025 to 2026
$1.1M
NCI NIH HHS R01 CA195524NCI NIH HHS R01 CA301555
6 · The paper itself

Abstract

Background: Hepatocellular carcinoma (HCC) exhibits significant morphological heterogeneity that has traditionally been attributed to etiological, environmental, and biological factors. Recent evidence suggests, however, that genetic background may influence tumor characteristics and behavior in HCC. Purpose: To investigate associations between established HCC genetic risk factors and first-order radiomic features. Methods: We evaluated a retrospective cohort of 210 patients with HCC who underwent contrast-enhanced MRI and genetic profiling for three established HCC risk variants (PNPLA3 rs738409-G, TM6SF2 rs58542926-T, and MAU2 rs58489806-T). We screened first-order radiomic features in the full cohort and then focused the primary analysis on 77 treatment-naive patients whose tumor morphology had not been modified by therapy. Patients were stratified into at-risk (≥1 risk allele, n=52) and wild-type (no risk alleles, n=25) groups. Eighteen first-order radiomic features were screened using the Mann-Whitney U test and AUC analysis. The leading feature, kurtosis, was then subjected to univariate and multivariate analysis for clinical and imaging features to identify any confounding effect. In addition, a 10-fold cross-validated cutpoint stability, sensitivity analysis, and Cox/Kaplan-Meier survival analysis were also conducted. Results: Among 18 features, kurtosis best distinguished at-risk from wild-type genotypes in treatment-naive patients (AUC = 0.67, p=0.014), with wild-type tumors showing more uniform, sharply peaked intensity patterns. Tumor volume correlated modestly with kurtosis (Spearman's ρ=0.45, p<0.0001), but volume alone did not discriminate genotype (Mann-Whitney U, p=0.3994). The genotype-kurtosis association remained significant after adjusting for volume and shape (ANCOVA, p=0.029) and strengthened after adjusting for nine clinical/demographic covariates (p=0.003), indicating kurtosis reflects biological information beyond tumor size. The empirical cutpoint was stable across 10-fold cross-validation (mean=3.35, SD = 0.05), and results were unchanged in sensitivity analysis. Kurtosis was not associated with overall survival (Cox HR = 1.19, p=0.13). No genotype-related discrimination was observed in the pooled full cohort, consistent with treatment-related alteration of tumor texture. Conclusions: In a cohort of 210 genetically profiled patients with HCC, genotype-related radiomic features were not apparent when treated and untreated tumors were analyzed together. However, restricting the analysis to the treatment-naive subgroup (n=77) revealed tumor kurtosis as a reproducible, biologically plausible first-order imaging correlate of established HCC genetic risk status.

Indexed as

geneticshepatocellular carcinomapersonalized medicineradiomicstumor morphology

Identifiers

PMID42898111
PMCPMC13677372

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.