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基于动态对比增强磁共振成像的肿瘤内及肿瘤周影像组学预测单发肝细胞癌（≤3 厘米）微血管侵犯分级
Authors Li Y , Li H, Feng Y, Lu L, Zhang J, Jia N
Received 13 February 2025
Accepted for publication 17 May 2025
Published 30 May 2025 Volume 2025:12 Pages 1083—1095
DOI http://doi.org/10.2147/JHC.S519578
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr David Gerber
Yinqiao Li,* Helin Li,* Yayuan Feng, Lun Lu, Juan Zhang, Ningyang Jia
Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Juan Zhang; Ningyang Jia, Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, People’s Republic of China, Email zhangjuan0801@126.com; ningyangjia@163.com
Purpose: To explore the application value of clinical indicators, radiological features, and magnetic resonance imaging (MRI) radiomics to predict the grading of MVI in nodular hepatocellular carcinoma (≤ 3cm).
Methods: A total of 131 patients with hepatocellular carcinoma (HCC) and confirmed microvascular invasion (MVI) who underwent surgical resection between January 2016 and December 2022 were retrospectively analyzed. A clinical-radiological (CR) model was constructed using independent risk factors identified by logistic regression. Radiomics models based on MRI (arterial phase, portal venous phase, delayed phase) across various regions (AVDPintra, AVDPintra+peri3mm, AVDPintra+peri5mm, AVDPintra+peri10mm) were developed using the Logistic Regression (LR) classifiers. The optimal radiomics model was subsequently integrated with the CR model to construct a combined clinical-radiological-radiomics (CRR) model. Model performance was assessed using the area under the curve (AUC).
Results: Non-smooth margin and intratumoral artery were risk factors for MVI grading. The combined CRR model demonstrated the best predictive performance, with AUCs of 0.907 and 0.917 in the training and testing sets, respectively. Compared with the CR model alone, the CRR model showed a statistically significant improvement (p = 0.008, DeLong test).
Conclusion: The AVDPintra+peri3mm model based on MRI radiomics demonstrates good predictive performance in predicting MVI grading in HCC (≤ 3cm). Combining features from the CR model with those of the AVDPintra+peri3mm model to construct the CRR model further enhances the prediction of MVI grading.
Keywords: hepatocellular carcinoma, magnetic resonance imaging, radiomics, microvascular invasion grading
