Evidence map›Paper›PMID 40591058›Full record

ArticleDiscover oncology2025

A T2 weighted imaging-based radiomics nomogram for the classification of hepatic blood-rich lesions: hepatocellular carcinoma and benign liver lesions.

Chen Yang, Boburjon Turgunov, Fandong Zhu, Yang Xia, Jianfeng Zhou, Zhenhua Zhao

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Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Chen Yang *Department of Radiology, Shaoxing People's Hospital, No. 568, North Zhongxing Road, Yuecheng District, Shaoxing City, Zhejiang, China.
Boburjon Turgunov *Shaoxing University School of Medicine, Shaoxing, China.
Fandong ZhuDepartment of Radiology, Shaoxing People's Hospital, No. 568, North Zhongxing Road, Yuecheng District, Shaoxing City, Zhejiang, China.
Yang XiaDepartment of Radiology, Shaoxing Women and Child's Health Hospital, Shaoxing, China.
Jianfeng ZhouDepartment of Radiology, Zhuji Second People's Hospital, Zhuji, China.
Zhenhua ZhaoDepartment of Radiology, Shaoxing People's Hospital, No. 568, North Zhongxing Road, Yuecheng District, Shaoxing City, Zhejiang, China. zhao2075@163.com.ORCID http://orcid.org/0000-0001-8952-8677

Funding

Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City 2020ZDSYSO1Medicine and Health Science and Technology Plan Project in Zhejiang Province of China 2022KY1321Shaoxing Municipal Project of Health Science, Technology Plan 2020A13027
6 · The paper itself

Abstract

purposeTo develop a radiomics nomogram based on T2-weighted imaging (T2WI) to distinguish hepatocellular carcinoma (HCC) from blood-rich benign liver lesions (BLLs) and to assess its value as an adjunct to conventional imaging.

methodsThis study analyzed imaging and clinical data from 220 patients with pathologically confirmed HCC (n = 140) and BLLs (n = 80). Patients were divided into training cohort (n = 100), test cohort (n = 44), and validation cohort (n = 76). We developed a radiomics model, a clinical model, and a fusion model integrating Rad-score with clinical factors, and identified the best predictive model among them in the test set, which was then compared with the diagnostic efficiency of two radiologists. The efficacy was assessed by the area under the receiver operating characteristic curve (AUC).

resultsFour radiomics features and two clinical factors (age, liver cirrhosis) were selected for the construction of radiomics and clinical models, respectively. The fusion model exhibited superior discriminative ability in the training set (AUC = 0.984), test set (AUC = 0.948), and validation set (AUC = 0.955). In the validation set, a statistical difference was observed between the fusion model and the radiomics model (P = 0.001). Furthermore, the diagnostic performance of the fusion model based on T2WI was comparable to that of conventional imaging based on enhanced MRI. In the cohort with conventional imaging errors, the AUCs of the clinical model, radiomics model, and fusion model were 0.841, 0.737, and 0.905, respectively.

conclusionsThe T2WI-based radiomics nomogram effectively differentiated HCC from blood-rich BLLs, and might be an adjunct to conventional imaging.

Indexed as

Blood-richHepatocellular carcinomaLiver lesionMagnetic resonance imagingRadiomics

Identifiers

PMID40591058
PMCPMC12214099

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