Share：
Share this content in WeChat
X
[Chinese][PDF]3691
• 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.

[1]
Torimura T, Iwamoto H. Treatment and the prognosis of hepatocellular carcinoma in Asia[J]. Liver Int, 2022, 42(9): 2042-2054. DOI: 10.1111/liv.15130.
[2]
Mocan L. Multimodal therapy for hepatocellular carcinoma: the role of surgery[J]. Eur Rev Med Pharmacol Sci, 2021, 25(13): 4470-4477. DOI: 10.26355/eurrev_202107_26238.
[3]
Tampaki M, Papatheodoridis GV, Cholongitas E. Intrahepatic recurrence of hepatocellular carcinoma after resection: an update[J]. Clin J Gastroenterol, 2021, 14(3): 699-713. DOI: 10.1007/s12328-021-01394-7.
[4]
Liu DM, Song TQ. Changes in and challenges regarding the surgical treatment of hepatocellular carcinoma in China[J]. Biosci Trends, 2021, 15(3): 142-147. DOI: 10.5582/bst.2021.01083.
[5]
Hui T, Chuah T, Low H, et al. Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study[J/OL]. Clin Radiol, 2018, 73(12): 1056.e11-e16 [2022-05-13]. https://www.clinicalradiologyonline.net/article/S0009-9260(18)30398-2/fulltext. DOI: 10.1016/j.crad.2018.07.109.
[6]
Cheng ZJ, Yang PH, Qu SP, et al. Risk factors and management for early and late intrahepatic recurrence of solitary hepatocellular carcinoma after curative resection[J]. HPB (Oxford), 2015, 17(5): 422-427. DOI: 10.1111/hpb.12367.
[7]
Wang LY, Ma XH, Zhao XM. Study of magnetic resonance imaging in predicting early postoperative recurrence of hepatocellular carcinoma[J]. Radiol Pract, 2021, 36(6): 803-806. DOI: 10.13609/j.cnki.1000-0313.2021.06.021.
[8]
Muhi A, Ichikawa T, Motosugi U, et al. Diffusion-weighted imaging of hepatocellular carcinoma for predicting early recurrence and survival after hepatectomy[J]. Hepatol Int, 2013, 7(2): 662-668. DOI: 10.1007/s12072-012-9383-2.
[9]
Cao LK, Chen J, Duan T, et al. Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade[J]. Quant Imaging Med Surg, 2019, 9(4): 590-602. DOI: 10.21037/qims.2019.02.14.
[10]
Li J, Yang JM, Qiao J, et al. Evaluation of intravoxel incoherent motion-diffusion weighted imaging in predicting early recurrence of hepatocellular carcinoma after curative hepatectomy[J]. Chin J Hepatobiliary Surg, 2019, 25(6): 422-425. DOI: 10.3760/cma.j.issn.1007-8118.2019.06.006.
[11]
Han X, Sun MY, Wang MY, et al. The enhanced T2 star weighted angiography (ESWAN) value for differentiating borderline from malignant epithelial ovarian tumors[J]. Eur J Radiol, 2019, 118: 187-193. DOI: 10.1016/j.ejrad.2019.07.011.
[12]
Xiong H, He XJ, Guo DJ. Value of MRI texture analysis for predicting high-grade prostate cancer[J]. Clin Imaging, 2021, 72: 168-174. DOI: 10.1016/j.clinimag.2020.10.028.
[13]
Baecker A, Liu X, La Vecchia C, et al. Worldwide incidence of hepatocellular carcinoma cases attributable to major risk factors[J]. Eur J Cancer Prev, 2018, 27(3): 205-212. DOI: 10.1097/CEJ.0000000000000428.
[14]
Zheng BH, Liu LZ, Zhang ZZ, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients[J/OL]. BMC Cancer, 2018, 18(1): 1148 [2022-05-16]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6249916. DOI: 10.1186/s12885-018-5024-z.
[15]
Pinato DJ, Fessas P, Sapisochin G, et al. Perspectives on the neoadjuvant use of immunotherapy in hepatocellular carcinoma[J]. Hepatology, 2021, 74(1): 483-490. DOI: 10.1002/hep.31697.
[16]
Qi WQ, Zhang Q, Xu Y, et al. Peg-interferon and nucleos(t)ide analogue combination at inception of antiviral therapy improves both anti-HBV efficacy and long-term survival among HBV DNA-positive hepatocellular carcinoma patients after hepatectomy/ablation[J]. J Viral Hepat, 2020, 27(4): 387-396. DOI: 10.1111/jvh.13236.
[17]
Xin JY, Gao SS, Liu JG, et al. The value of ESWAN in diagnosis and differential diagnosis of prostate cancer: preliminary study[J]. Magn Reson Imaging, 2017, 44: 26-31. DOI: 10.1016/j.mri.2017.08.002.
[18]
Ji SQ, Zhang S, Mao ZJ, et al. Quantitative assessment of iron deposition in Parkinson's disease using enhanced T2 star-weighted angiography[J]. Neurol India, 2016, 64(3): 428-435. DOI: 10.4103/0028-3886.181532.
[19]
Chen XQ, Niu JP, Peng RQ, et al. The early diagnosis of Parkinson's disease through combined biomarkers[J]. Acta Neurol Scand, 2019, 140(4): 268-273. DOI: 10.1111/ane.13140.
[20]
Tian SF, Liu AL, Li Y, et al. R2* value in predicting the pathologic grade of endometrial adenocarcinoma[J]. Chin J Med Imaging, 2016, 24(11): 864-867. DOI: 10.3969/j.issn.1005-5185.2016.11.016.
[21]
Li Y, Song QW, Sun MY, et al. Use of enhanced T2 star-weighted angiography (ESWAN) and R2* values to distinguish ovarian cysts due to endometriosis from other causes[J]. Abdom Imaging, 2015, 40(6): 1733-1741. DOI: 10.1007/s00261-014-0314-7.
[22]
Wang D, Chen TW, Liu QY, et al. Liver ESWAN and serum ferritin for quantitative evaluation of liver fibrosis: an experimental study[J]. Chin J Magn Reson Imaging, 2017, 8(12): 917-922. DOI: 10.12015/issn.1674-8034.2017.12.008.
[23]
Wang S. Using ESWAN to differentiate hepatocellular carcinomas from cavernous hemangiomas: a preliminary research[D]. Dalian: Dalian Medical University, 2013.
[24]
Tian SF, Liu AL, Guo WY, et al. Feasibility of R2* value in predicting pathological grade of hepatocellular carcinoma[J]. Chin J Med Imaging Technol, 2016, 32(11): 1693-1696. DOI: 10.13929/j.1003-3289.2016.11.017.
[25]
Wang YC, Shen YQ, Hu XM, et al. Application of R2* and apparent diffusion coefficient in estimating tumor grade and T category of bladder cancer[J]. AJR Am J Roentgenol, 2020, 214(2): 383-389. DOI: 10.2214/AJR.19.21668.
[26]
Ahn SJ, Kim JH, Park SJ, et al. Hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MR imaging can predict early recurrence after curative resection using image features and texture analysis[J]. Abdom Radiol (NY), 2019, 44(2): 539-548. DOI: 10.1007/s00261-018-1768-9.
[27]
Du J, Li K, Wang W, et al. Qualitative and quantitative diagnosis of intramuscular hemangioma subtypes: Diagnostic performance comparison of ESWAN and conventional MRI[J/OL]. Acta Radiologica, 2021: 2841851211065145 [2022-05-27]. https://journals.sagepub.com/doi/10.1177/02841851211065145. DOI: 10.1177/02841851211065145.

PREV Voxel-based morphological study on the changes of brain structure during radiotherapy of nasopharyngeal carcinoma
NEXT Preoperative contrast-enhanced MRI based on radiomics analysis to predict the recurrence of hepatocellular carcinoma after resection
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn