Evidence map›Paper›PMID 41761127›Full record

ArticleBMC medical imaging2026

Delta radiomics-based nomogram for preoperative prediction vessels encapsulating tumor clusters (VETC) and prognosis in hepatocellular carcinoma using dynamic contrast-enhanced CT.

Chao Zhang, Hai Zhong, Mengmeng Zhao, Yadong Li, Zhengjun Dai, Guodong Pang

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Article in BMC medical imaging, 2026. 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chao Zhang *Department of Radiology, The Second Qilu Hospital of Shandong University, Shandong, 250033, China.
Hai Zhong *Department of Radiology, The Second Qilu Hospital of Shandong University, Shandong, 250033, China.
Mengmeng ZhaoDepartment of Radiology, The Jinan 2nd People's Hospital, Shandong, 250000, China.
Yadong LiThe Internet Medical Department, The Second Qilu Hospital of Shandong University, Shandong, 250033, China.
Zhengjun DaiScientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, 100192, China. daizhengjun@foxmail.com.
Guodong PangDepartment of Radiology, The Second Qilu Hospital of Shandong University, Shandong, 250033, China. pgd226@aliyun.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVessels encapsulating tumor clusters (VETC) serve as a crucial adverse prognostic indicator in hepatocellular carcinoma (HCC). This study aimed to develop and validate a Delta radiomics-based nomogram model on dynamic contrast-enhanced CT (DCE-CT) to predict VETC status and patient prognosis in HCC.

methodsA cohort of 222 patients from two centers with HCC undergoing DCE-CT scans and CD34 immunochemical staining was enrolled. Each liver lesion was segmented on intratumoral and peritumoral regions in the arterial phase (AP) and portal vein phase (PP) CT images. A total of 10,128 (1,688*6) radiomics features, including absolute and relative delta radiomics features, were extracted. Using four machine-learning algorithms, the features were trained and optimized (training set), and validated (internal and external test sets) to classify VETC patterns. Multivariable logistic regression incorporating signature scores and clinical predictors generated the nomogram. Model performance was evaluated through area under the curves (AUC) analysis, calibration curves, and decision curve analysis (DCA). The Kaplan-Meier survival analysis was used to assess recurrence-free survival (RFS) in the VETC+ and VETC- patients.

resultsThe logistic regression-based nomogram incorporating three radiomic signatures and two clinical factors showed powerful predictive ability in internal and external test sets with AUCs of 0.854 and 0.803, respectively. The calibration curves, DCA showed favorable predictive performance of the nomogram. Patients classified as high-risk by the nomogram exhibited significantly shorter RFS compared to low-risk counterparts (P < 0.001).

conclusionThe developed nomogram demonstrated clinical translatability in preoperative VETC prediction and recurrence risk stratification, providing a potential imaging biomarker for guiding personalized therapeutic strategies in HCC management.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsNomogramsTomography, X-Ray ComputedAgedContrast MediaFemaleHumansMaleMiddle AgedPrognosisRadiomicsContrast MediaComputed tomographyDelta radiomicsHepatocellular carcinomaVessels encapsulating tumor clusters

Identifiers

PMID41761127
PMCPMC13040871

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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.