Evidence map›Paper›PMID 41846473›Full record

ArticleThe Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology2025

Prediction of Histopathological Grade of Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced Magnetic Resonance Imaging Radiomics Features.

Bisar Akbas, Huseyin Tugsan Balli, Kairgeldy Aikimbaev, Ferhat Can Piskin, Kivilcim Eren Erdogan, Puren Sevinc Yucel

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Article in The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology, 2025. 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

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Bisar AkbasDepartment of Radiology, Çukurova University School of Medicine, Adana, Türkiye.
Huseyin Tugsan BalliDepartment of Radiology, Çukurova University School of Medicine, Adana, Türkiye.
Kairgeldy AikimbaevDepartment of Radiology, Çukurova University School of Medicine, Adana, Türkiye.
Ferhat Can PiskinDepartment of Radiology, Çukurova University School of Medicine, Adana, Türkiye.
Kivilcim Eren ErdoganDepartment of Pathology, Çukurova University School of Medicine, Adana, Türkiye.
Puren Sevinc YucelDepartment of Biostatistics, Çukurova University School of Medicine, Adana, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimsThis study aimed to evaluate preoperative models based on gadoxetic acid (Gd-EOB-DTPA [gadolinium ethoxyben zyl diethylenetriamine pentaacetic acid])-enhanced magnetic resonance imaging (MRI) radiomics for predicting the histopathological grade of hepatocellular carcinoma (HCC). MATERIALS AND

methodsThis retrospective study included 68 treatment-naïve patients with histopathologically confirmed HCC from September 2015 to November 2021. Tumors were categorized into well-differentiated and non-well-differentiated groups. Radiomics features were extracted from preoperative hepatobiliary phase MRI images. Logistic regression (LR) with least absolute shrinkage and selection operator selection was used to identify key radiomics features and clinical parameters. Three models-radiomics, clinical, and combined clinical-radiomics (CCR)-were developed to predict HCC differentiation.

resultsThe radiomics and clinical models achieved area under the curve (AUC) values of 0.803 and 0.749, respectively, while the CCR model showed superior performance (AUC 0.827). In the clinical model, the albumin-bilirubin score was an independent risk factor (P < .05). The radiomics score was significantly lower in well-differentiated tumors (P < .001). Radiomics features were independent predic tors in the CCR model (P = .005).

conclusionRadiomics features from hepatobiliary phase MRI and clinical parameters can effectively predict the differentiation grade of HCC, aiding in preoperative decision-making. However, the study is limited by its small sample size and the absence of external vali dation; therefore, further multicenter studies are necessary.   Cite this article as: Akbas B, Balli T, Aikimbaev K, et al. Prediction of histopathological grade of hepatocellular carcinoma by gadoxetic acid-enhanced magnetic resonance imaging radiomics features. Turk J Gastroenterol. 2026;37(3):322-329.

Indexed as

Carcinoma, HepatocellularContrast MediaGadolinium DTPALiver NeoplasmsMagnetic Resonance ImagingRadiomicsAdultAgedFemaleHumansMaleMiddle AgedNeoplasm GradingPredictive Value of TestsRetrospective StudiesContrast MediaGadolinium DTPAgadolinium ethoxybenzyl DTPA

Identifiers

PMID41846473
PMCPMC12994426

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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.