Evidence map›Paper›PMID 40171262›Full record

ArticleFrontiers in oncology2025

A nomogram based on quantitative MR signal intensity predicts early response to combined systemic treatment in patients with hepatocellular carcinoma.

Ran Tao, Haohao Lu, Xiangjun Dong, Qian Qian Ren, Hongjie Fan, Zhaoming Tang, Xiangwen Xia

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Article in Frontiers in oncology, 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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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

7 authors.

Ran Tao *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Haohao Lu *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiangjun DongDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qian Qian RenDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hongjie FanDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhaoming TangDepartment of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Xiangwen XiaDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to develop and evaluate the value of a nomogram based on quantitative MR signal intensity to predict response to combined systemic therapy of anti-angiogenesis and immune checkpoint inhibitor (ICI) in hepatocellular carcinoma (HCC) patients. Methods: 117 HCC patients who underwent the combined systemic treatment at a tertiary hospital between September 2020 and May 2024 were enrolled and divided into a development cohort (n = 82) and a validation cohort (n = 35). The predictive value of the relative signal intensity attenuation index (rSIAI) based on enhanced MR parameters and laboratory parameters on disease control was evaluated using receiver operating characteristic (ROC) curves, with the determination of optimal cut-off values (COVs) accomplished via Youden's index. Univariate and multivariable analyses were conducted to evaluate the association between COVs and disease control. The validity of the COVs was further confirmed through chi-square testing and calculation of Cramer's V coefficient (V). A nomogram was constructed based on the multivariable logistic regression model and evaluated for clinical applicability. Results: rSIAI from arterial to portal phase (rSI_ap) in combination with peripheral T-cell subset (CD4+) achieved the most accurate predictive performance for outcome compared to rSI_ap or CD4+ alone, with an area under the curve (AUC) of the ROC of 0.845 (95% CI, 0.748-0.915). A nomogram based on rSI_ap and CD4+ was constructed. Calibration and decision curve analyses confirmed the clinical relevance and value of the nomogram. Conclusion: The nomogram based on rSI_ap has the potential to be a non-invasive tool for predicting disease control in advanced HCC patients who have received combined anti-angiogenesis and ICI therapies.

Indexed as

hepatocellular carcinomamagnetic resonance imaging (MRI)nomogramsignal intensitysystemic therapy

Identifiers

PMID40171262
PMCPMC11959652

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