The Association Between Tumor Radiomic Analysis and Peritumor Habitat-Derived Radiomic Analysis on Gadoxetate Disodium-Enhanced MRI With Microvascular Invasion in Hepatocellular Carcinoma

Cheng Wang; Fei Wu; Fang Wang; Huan-Huan Chong; Haitao Sun; Peng Huang; Yuyao Xiao; Chun Yang; Mengsu Zeng

doi:10.1002/jmri.29523

The Association Between Tumor Radiomic Analysis and Peritumor Habitat-Derived Radiomic Analysis on Gadoxetate Disodium-Enhanced MRI With Microvascular Invasion in Hepatocellular Carcinoma

J Magn Reson Imaging. 2024 Jul 12. doi: 10.1002/jmri.29523. Online ahead of print.

Authors

Cheng Wang^{1

2

3}, Fei Wu^{1

2

3}, Fang Wang⁴, Huan-Huan Chong⁵, Haitao Sun^{1

2

3}, Peng Huang^{1

2

3}, Yuyao Xiao^{1

2

3}, Chun Yang^{1

2

3}, Mengsu Zeng^{1

2

3}

Affiliations

¹ Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.
² Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
³ Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
⁴ Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
⁵ Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.

PMID: 38997242
DOI: 10.1002/jmri.29523

Abstract

Background: Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment.

Purpose: To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis.

Study type: Retrospective.

Subjects: Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94).

Field strength/sequence: 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence.

Assessment: Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews.

Statistical tests: Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance.

Results: Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival.

Data conclusion: The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

Keywords: habitat imaging; hepatocellular carcinoma; magnetic resonance imaging; microvascular invasion; radiomic analysis.

Abstract

Grants and funding