Preoperative contrast-enhanced MRI based on radiomics analysis to predict the recurrence of hepatocellular carcinoma after resection

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• Clinical Article •

WANG Qing SHENG Ye LIU Haifeng ZHU Zuhui XING Wei

Cite this article as: Wang Q, Sheng Y, Liu HF, et al. Preoperative contrast-enhanced MRI based on radiomics analysis to predict the recurrence of hepatocellular carcinoma after resection[J]. Chin J Magn Reson Imaging, 2022, 13(12): 93-99. DOI:10.12015/issn.1674-8034.2022.12.016.

Abstract

[Abstract] Objective To develop a preoperative MRI model based on radiomics analysis for predicting recurrence of hepatocellular carcinoma (HCC) patients after resection.Materials and Methods This retrospective study included 164 HCC patients (training set: n=115, testing set: n=49) who performed hepatectomy and preoperative gadoxetic acid-enhanced MRI within 2 weeks before resection between August 2015 and August 2020. The univariable and multivariable Cox regression analyses were performed to identify clinical-pathologic-radiologic factors associated with recurrence-free survival (RFS). The radiomics models were constructed using least absolute shrinkage and selection operator Cox regression. The combined nomogram model merging independent factors and radscore was built to predict the RFS of HCC patients after resection and the predictive performance of nomogram model was evaluated with C-index and calibration curves. Kaplan-Meier survival analysis was used to assess the association of the models with RFS.Results The combined nomogram model integrating the tumor margin [HR=2.1, 95% confidence interval (CI): 1.3 to 3.3], necrosis (HR=2.5, 95% CI: 1.5 to 4.3) and the radscore (HR=64.1, 95% CI: 20.6 to 199.9) showed good predictive efficacy for recurrence of HCC patients after resection with a C-index of 0.796 (0.738 to 0.854) in the training set and 0.784 (0.684 to 0.885) in the test set. Calibration curves demonstrated good agreement between model-predicted probabilities and observed outcomes. There was significant difference for recurrence rates between predicted low-risk group and high-risk group in the training set (χ²=52.88, P＜0.001) and the test set (χ²=4.14, P=0.042).Conclusions The nomogram model demonstrated good performance for predicting recurrence of HCC patients after resection, thus may help personalized clinical management of HCC patients.

[Keywords] hepatocellular carcinoma；recurrence；magnetic resonance imaging；radiomics；nomogram

Contributor Information

WANG Qing¹ SHENG Ye² LIU Haifeng¹ ZHU Zuhui¹ XING Wei^1*

¹ Department of Radiology, the Third Affiliated Hospital of Soochow University (Changzhou First People's Hospital), Changzhou 213200, China

² Department of Interventional Radiology, the Third Affiliated Hospital of Soochow University (Changzhou First People's Hospital), Changzhou 213200, China

Xing W, E-mail: suzhxingwei@suda.edu.cn

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS Youth Project of Changzhou City Health Commission (No. QN202111).

Received 2022-08-03

Accepted 2022-12-12

DOI: 10.12015/issn.1674-8034.2022.12.016

Text

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