Evidence map›Paper›PMID 40597848›Full record

ArticleBMC medical imaging2025

MRI proton density fat fraction for estimation of biological characteristics in hepatocellular carcinoma.

Yingying Liang, Xiaorui Han, Zihua Wang, Xiaoxiao Lei, Xinxin Lan, Yidong Gao, Xinhua Wei, Hongzhen Wu

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Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

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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

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yingying LiangDepartment of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
Xiaorui HanDepartment of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
Zihua WangDepartment of Radiology, Foshan Hospital of Traditional Chinese Medicine, Foshan, 528000, Guangdong Province, China.
Xiaoxiao LeiDepartment of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
Xinxin LanDepartment of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
Yidong GaoDepartment of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
Xinhua Wei *Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China.
Hongzhen Wu *Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province, 510180, China. eywuhongzhen@scut.edu.cn.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2021A1515110703, 2022A1515220135Basic and Applied Basic Research Foundation of Guangdong Province 2021A1515220071, 2022A1515011470Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515010606Guangzhou Planned Project of Science and Technology 2024A03J1025National Natural Science Foundation of China 82402377
6 · The paper itself

Abstract

purposeTo evaluate whether the magnetic resonance imaging (MRI) proton density fat fraction (PDFF) can predict the biological characteristics of hepatocellular carcinoma (HCC) preoperatively.

methodsA total of 131 HCCs were included. The MRI features and PDFF values were evaluated by two independent radiologists. The intraclass correlation coefficient (ICC) was calculated in terms of inter- and intra-observer agreements. The macrotrabecular-massive (MTM) subtype, microvascular invasion (MVI) status, histological grade, and proliferative status of Ki-67 and p53 were identified in HCCs. The diagnostic performance of the PDFF was evaluated using receiver operating characteristic (ROC) curve analysis based on the area under the receiver operating characteristic curve (AUC).

resultsPDFF values showed significant differences between: MTM vs. non-MTM HCCs (p=0.048), MVI-positive vs. negative tumors (p=0.041), high- vs. low-grade lesions (p<0.001), and p53-positive vs. negative cases (p=0.015), but not for Ki-67 expression (p=0.075). The AUC values of the PDFF for predicting the MTM subtype, MVI status, histological grade, and proliferative status of p53 were 0.606, 0.588, 0.683, and 0.671, respectively. Only infiltrative appearance had significant difference between MVI-positive and MVI-negative groups. Combining PDFF with infiltrative appearance significantly improved MVI prediction (AUC = 0.681, p = 0.02).

conclusionsMRI-PDFF demonstrates potential as a quantitative biomarker for preoperative assessment of HCC aggressiveness, particularly for the MTM subtype, histological grade and p53 status, though its standalone performance for MVI prediction remains limited. Integration with morphological features enhances diagnostic accuracy, supporting its complementary role in multiparametric HCC characterization. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsMagnetic Resonance ImagingAdultAgedFemaleHumansKi-67 AntigenMaleMiddle AgedRetrospective StudiesROC CurveKi-67 AntigenDiagnosisHepatocellular carcinomaMagnetic resonance imagingProton density fat fraction

Identifiers

PMID40597848
PMCPMC12211271

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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.