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• Clinical Article •
Value of texture analysis based on R2* map for predicting early recurrence of HCC after hepatectomy
XU Qihao  ZHAO Ying  WANG Yue  LIN Tao  REN Xue  SONG Qingwei  GUO Yan  LI Xin  WU Tingfan  LIU Ailian 

Cite this article as: Xu QH, Zhao Y, Wang Y, et al. Value of texture analysis based on R2* map for predicting early recurrence of HCC after hepatectomy[J]. Chin J Magn Reson Imaging, 2022, 13(12): 87-92. DOI:10.12015/issn.1674-8034.2022.12.015.


[Abstract] Objective To investigate the feasibility of predicting early postoperative recurrence of hepatocellular carcinoma (HCC) based on R2* map texture analysis of enhanced T2* weighted angiography (ESWAN) sequence.Materials and Methods A retrospective analysis was performed of all 81 cases of patients who underwent hepatectomy and were pathologically confirmed HCC between November 2011 and May 2020. According to whether there were enhanced computed tomography or MRI or surgical pathology confirmed new intrahepatic HCC lesions or extrahepatic metastases within 2 years after hepatectomy, HCC patients were divided into the early recurrence group (n=43) and the non-early recurrence group (n=38). All patients underwent 1.5 T or 3.0 T MRI scan of upper abdomen within 1 month before surgery, including T1WI, T2WI and ESWAN sequence. ESWAN image was postprocessed by Functool software (GE AW 4.6 workstation) to obtain R2* graph. Two radiologists with 3 and 7 years of MRI diagnosis experience respectively delineated all layers of the tumor along the tumor edge on R2* maps, and then extracted 107 texture features using Artificial Intelligence Kit software. It includes first-order features, shape features, gray level co-occurrence matrix (GLCM), gray level dependence matrix (GLDM), gray level size zone matrix (GLSZM), gray level run length matrix (GLRLM) and neighbouring gray tone difference matrix (NGTDM). Intra-class correlation coefficient (ICC), Spearman correlation test and gradient boosting decision tree (GBDT) were used for feature dimension reduction. Logistic regression model was established, receiver operating characteristic (ROC) curve was drawn to predict the efficacy of recurrence, and area under the curve (AUC), precision, sensitivity and specificity were calculated. Calibration curve and Hosmer-Lemeshow (H-L) were used to test the fit degree of the valence model. Clinical decision curve analysis (DCA) was performed to evaluate the clinical benefit.Results Thirteen optimal texture features were obtained, including six first-order features (nnergy, kurtosis, maximum, median, skewness and total energy), one GLCM feature (Idn), one GLDM feature (large dependence low gray level emphasis), one GLRLM feature (run entropy), two GLSZM features (size zone non uniformity and size zone non uniformity normalized), one NGTDM feature (busyness) and one shape feature (maximum 2D diameter, Slice). Logistic regression model was established to predict AUC, accuracy, sensitivity and specificity of early recurrence after hepatectomy for HCC were 0.830 (95% CI: 0.740-0.920), 79.00% (95% CI: 78.60%-79.40%), 83.70% (95% CI: 72.70%-94.80%) and 73.70% (95% CI: 59.70%-87.70%). The calibration curve showed that there was a good consistency between the predicted early recurrence probability of the model and the real early recurrence probability. H-L test showed that there was no significant difference between the predicted calibration curve of the model and the ideal model curve (P=0.493). DCA showed that R2* map texture analysis had a higher clinical net benefit in predicting early recurrence after hepatectomy for HCC.Conclusions R2* map based on ESWAN sequence combined with texture analysis has certain predictive value for early recurrence of HCC after hepatectomy based on the difference of tumor oxygen content level.
[Keywords] hepatocellular carcinoma；hepatectomy；early recurrence；R2* map；texture analysis；magnetic resonance imaging

XU Qihao1   ZHAO Ying1   WANG Yue1   LIN Tao1   REN Xue1   SONG Qingwei1   GUO Yan2   LI Xin2   WU Tingfan2   LIU Ailian1, 3*  

1 Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

2 GE Healthcare, Shanghai 210000, China

3 Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian 116011, China

Liu AL, E-mail: liuailian@dmu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 61971091).
Received  2022-07-08
Accepted  2022-11-10
DOI: 10.12015/issn.1674-8034.2022.12.015
Cite this article as: Xu QH, Zhao Y, Wang Y, et al. Value of texture analysis based on R2* map for predicting early recurrence of HCC after hepatectomy[J]. Chin J Magn Reson Imaging, 2022, 13(12): 87-92. DOI:10.12015/issn.1674-8034.2022.12.015.

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