Evidence map›Paper›PMID 40166482›Full record

ArticleEuropean journal of radiology open2025

Prediction early recurrence of hepatocellular carcinoma after hepatectomy using gadoxetic acid-enhanced MRI and IVIM.

Da Guo, Liping Liu, Yu Jin

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Article in European journal of radiology open, 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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5 · Who and what money

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3 authors.

Da GuoDepartment of Radiology, Physical and Mental Hospital of Nanchong City, Nanchong, Sichuan, PR China.
Liping LiuDepartment of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, PR China.
Yu JinDepartment of Radiology, Chengdu Second People's Hospital, Chengdu, Sichuan, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aims to develop and validate a predictive nomogram for early recurrence in hepatocellular carcinoma (HCC), utilizing gadoxetic acid-enhanced MRI and intravoxel incoherent motion (IVIM) imaging to improve preoperative assessment and decision-making. Materials and methods: From March 2018 and June 2022, a total of 245 patients with pathologically confirmed HCC, who underwent preoperative gadoxetic acid-enhanced MRI and IVIM, were retrospectively enrolled from two hospitals. These patients were divided into a training cohort (n = 160) and a validation cohort (n = 85). All patients were followed until death or the last follow-up date, with a minimum follow-up period of two years. Clinical indicators and pathologic information were compared between train cohort and validation cohort. Radiological features and diffusion parameters were compared between recurrence and non-recurrence groups using the chi-square test, Mann-Whitney U test and independent sample t test in training cohort. Univariate and multivariate analyses were performed to identify significant clinical-radiological variables associated with early recurrence in the training cohort. Based on these findings, a predictive nomogram integrating risk factors and diffusion parameters was developed. The predictive performance of the nomogram was evaluated in both the training and validation cohorts. Results: No statistically significant difference in clinical and pathologic characteristics were observed between the training and validation cohorts. In training cohort, significant differences were identified between the recurrence and non-recurrence groups in tumor size, nodule-in-nodule architecture, mosaic architecture, non-smooth tumor margin, intratumor necrosis, satellite nodule, and peritumoral hypo-intensity in the hepatobiliary phase (HBP). The results of multivariate analysis identified tumor size (HR, 1.435; 95 % CI, 0.702-2.026; p < 0.05), mosaic architecture (HR, 0.790; 95 % CI, 0.421-1.480; p < 0.05), non-smooth tumor margin (HR, 1.775; 95 % CI, 0.941-3.273; p < 0.05), intratumor necrosis (HR, 1.414; 95 % CI, 0.807-2.476; p < 0.05), satellite nodule (HR, 0.648; 95 % CI, 0.352-1.191; p < 0.01), peritumoral hypo-intensity on HBP (HR, 2.786; 95 % CI, 1.141-6.802; p < 0.001) and D (HR, 0.658; 95 % CI,0.487-0.889; p < 0.01) were the independent risk factor for recurrence. The nomogram exhibited excellent predictive performance with C-index of 0.913 and 0.875 in the training cohort and validation cohort, respectively. Also, based on the nomogram score, the patients were classified according to risk factor and the Kaplan-Meier curve analysis also showed that the nomogram had a good predictive efficacy. Conclusion: The nomogram, integrating radiological risk factors and diffusion parameters, offers a reliable tool for preoperative prediction of early recurrence in HCC patients.

Indexed as

DiffusionHepatocellular carcinomaMagnetic resonance imagingRecurrence

Identifiers

PMID40166482
PMCPMC11957592

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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.