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Methods Inf Med 2019; 58(01): 042-049
DOI: 10.1055/s-0039-1688758
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Correlations between P53 Mutation Status and Texture Features of CT Images for Hepatocellular Carcinoma

Hongzhen Wu
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
2   Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
,
Xin Chen
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
,
Jiawei Chen
3   Southern Medical University, Guangzhou, China
,
Yuqi Luo
4   Department of General Surgery, Nansha Hospital, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
5   The Second Affiliated Hospital, South China University of Technology, Guangzhou, China
,
Xinqing Jiang
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
2   Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
,
Xinhua Wei
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
,
Wenjie Tang
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
,
Yu Liu
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
,
Yingying Liang
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
,
Weifeng Liu
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
,
Yuan Guo
1   Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
› Author Affiliations Funding This study was supported by Guangdong modern hospital management institute hospital management research (No. 2016009), the National Natural Science Foundation of China (No.81571665) and the Science and Technology Planning Project of Guangzhou (No. 201804010032).
Further Information

Publication History

20 June 2018

21 March 2019

Publication Date:
04 June 2019 (online)

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Abstract

Objectives To investigate the performance of texture analysis in characterizing P53 mutations of hepatocellular carcinomas (HCCs) based on computed tomography (CT).

Methods A total of 63 HCC patients underwent CT scans and were tested for P53 mutations. Patients were divided into two groups of P53(−) and P53(+) according to the P53 scores. First- and second-order texture features were computed from the CT images and compared between groups using independent Student's t-test. A Spearman's correlation coefficient was used for correlations to assess the relationship between the different P53 sores and CT data. The performance of texture features in differentiating the P53 mutations of HCC was assessed using receiver operating characteristic analysis.

Results The mean values of angular second moment (ASM; mean = 0.001) and contrast (mean = 194.727) for P53(−) were higher than those of P53(+). Meanwhile the mean values of correlation (mean = 0.735), sum variance (mean = 1,111.052), inverse difference moment (IDM; mean = 0.090), and entropy (mean = 3.016) for P53(−) were lower than those of P53(+). Significant correlations were found between P53 scores and ASM (r =  − 0.439), contrast (r =  − 0.263), correlation (r = 0.551), sum of squares (r = 0.282), sum variance (r = 0.417), IDM (r = 0.308), and entropy (r = 0.569). Five texture parameters (ASM, contrast, correlation, IDM, and entropy) were predictive of P53 mutation status, with areas under the curve ranging from 0.621 to 0.792.

Conclusions There was a direct relationship between P53 mutations and gray-level co-occurrence matrix, but not with histograms for HCC patients. Correlation and entropy seemed to be the most promising in differentiating P53 (−) from P53(+).

Keywords

hepatocellular carcinoma - P53 gene - computed tomography
 

  • References

  • 1 Kloth C, Thaiss WM, Kärgel R. , et al. Evaluation of texture analysis parameter for response prediction in patients with hepatocellular carcinoma undergoing drug-eluting bead transarterial chemoembolization (DEB-TACE) using biphasic contrast-enhanced CT image data: correlation with liver perfusion CT. Acad Radiol 2017; 24 (11) 1352-1363
    CrossrefPubMedGoogle Scholar
  • 2 Wang D, Zhang S, Chen Y, Hu B, Lu C. Low expression of NKD2 is associated with enhanced cell proliferation and poor prognosis in human hepatocellular carcinoma. Hum Pathol 2018; 72: 80-90
    CrossrefPubMedGoogle Scholar
  • 3 Kiryu S, Akai H, Nojima M. , et al. Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator. Sci Rep 2017; 7 (01) 12689
    CrossrefPubMedGoogle Scholar
  • 4 Vousden KH, Prives C. Blinded by the light: the growing complexity of p53. Cell 2009; 137 (03) 413-431
    CrossrefPubMedGoogle Scholar
  • 5 Kruse JP, Gu W. Modes of p53 regulation. Cell 2009; 137 (04) 609-622
    CrossrefPubMedGoogle Scholar
  • 6 Li Q, Liu X, Jin K. , et al. NAT10 is upregulated in hepatocellular carcinoma and enhances mutant p53 activity. BMC Cancer 2017; 17 (01) 605
    CrossrefPubMedGoogle Scholar
  • 7 Lai PB, Chi TY, Chen GG. Different levels of p53 induced either apoptosis or cell cycle arrest in a doxycycline-regulated hepatocellular carcinoma cell line in vitro. Apoptosis 2007; 12 (02) 387-393
    CrossrefPubMedGoogle Scholar
  • 8 Liu J, Ma Q, Zhang M. , et al. Alterations of TP53 are associated with a poor outcome for patients with hepatocellular carcinoma: evidence from a systematic review and meta-analysis. Eur J Cancer 2012; 48 (15) 2328-2338
    CrossrefPubMedGoogle Scholar
  • 9 Maniwa Y, Yoshimura M, Obayashi C. , et al. Association of p53 gene mutation and telomerase activity in resectable non-small cell lung cancer. Chest 2001; 120 (02) 589-594
    CrossrefPubMedGoogle Scholar
  • 10 He X, Liu F, Yan J. , et al. Trans-splicing repair of mutant p53 suppresses the growth of hepatocellular carcinoma cells in vitro and in vivo. Sci Rep 2015; 5: 8705
    CrossrefPubMedGoogle Scholar
  • 11 Hayashi Y, Tsujii M, Kodama T. , et al. p53 functional deficiency in human colon cancer cells promotes fibroblast-mediated angiogenesis and tumor growth. Carcinogenesis 2016; 37 (10) 972-984
    CrossrefPubMedGoogle Scholar
  • 12 Graziano SL, Tatum A, Herndon II JE. , et al. Use of neuroendocrine markers, p53, and HER2 to predict response to chemotherapy in patients with stage III non-small cell lung cancer: a cancer and leukemia group B study. Lung Cancer 2001; 33 (2–3): 115-123
    CrossrefPubMedGoogle Scholar
  • 13 Li Z, Han C, Feng J. Relationship of the expression levels of XIAP and p53 genes in hepatocellular carcinoma and the prognosis of patients. Oncol Lett 2017; 14 (04) 4037-4042
    CrossrefPubMedGoogle Scholar
  • 14 Mantovani F, Walerych D, Sal GD. Targeting mutant p53 in cancer: a long road to precision therapy. FEBS J 2017; 284 (06) 837-850
    CrossrefPubMedGoogle Scholar
  • 15 Zhang DG, Zheng JN, Pei DS. P53/microRNA-34-induced metabolic regulation: new opportunities in anticancer therapy. Mol Cancer 2014; 13: 115
    CrossrefPubMedGoogle Scholar
  • 16 Gillies RJ, Anderson AR, Gatenby RA, Morse DL. The biology underlying molecular imaging in oncology: from genome to anatome and back again. Clin Radiol 2010; 65 (07) 517-521
    CrossrefPubMedGoogle Scholar
  • 17 Yu H, Touret AS, Li B. , et al. Application of texture analysis on parametric T1 and T2 maps for detection of hepatic fibrosis. J Magn Reson Imaging 2017; 45 (01) 250-259
    CrossrefPubMedGoogle Scholar
  • 18 Aerts HJ, Velazquez ER, Leijenaar RT. , et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006
    CrossrefPubMedGoogle Scholar
  • 19 Coroller TP, Grossmann P, Hou Y. , et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 2015; 114 (03) 345-350
    CrossrefPubMedGoogle Scholar
  • 20 Tixier F, Le Rest CC, Hatt M. , et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 2011; 52 (03) 369-378
    CrossrefPubMedGoogle Scholar
  • 21 Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 2013; 266 (01) 326-336
    CrossrefPubMedGoogle Scholar
  • 22 Atupelage C, Nagahashi H, Yamaguchi M, Abe T, Hashiguchi A, Sakamoto M. Computational grading of hepatocellular carcinoma using multifractal feature description. Comput Med Imaging Graph 2013; 37 (01) 61-71
    CrossrefPubMedGoogle Scholar
  • 23 Echegaray S, Gevaert O, Shah R. , et al. Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma. J Med Imaging (Bellingham) 2015; 2 (04) 041011
    PubMedGoogle Scholar
  • 24 Chen S, Zhu Y, Liu Z, Liang C. Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: a retrospective pilot study. Eur J Radiol 2017; 90: 198-204
    CrossrefPubMedGoogle Scholar
  • 25 Miao S, Wang SM, Cheng X. , et al. Erythropoietin promoted the proliferation of hepatocellular carcinoma through hypoxia induced translocation of its specific receptor. Cancer Cell Int 2017; 17: 119
    CrossrefPubMedGoogle Scholar
  • 26 Zhou W, Zhang L, Wang K. , et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging 2017; 45 (05) 1476-1484
    CrossrefPubMedGoogle Scholar
  • 27 Banerjee S, Wang DS, Kim HJ. , et al. A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology 2015; 62 (03) 792-800
    CrossrefPubMedGoogle Scholar
  • 28 Szczypiński PM, Strzelecki M, Materka A, Klepaczko A. MaZda--a software package for image texture analysis. Comput Methods Programs Biomed 2009; 94 (01) 66-76
    CrossrefPubMedGoogle Scholar
  • 29 Wan T, Bloch BN, Plecha D. , et al. A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: preliminary results in predicting OncotypeDX risk scores. Sci Rep 2016; 6: 21394
    CrossrefPubMedGoogle Scholar
  • 30 Khalvati F, Wong A, Haider MA. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging 2015; 15: 27
    CrossrefPubMedGoogle Scholar
  • 31 Yang X, Tridandapani S, Beitler JJ. , et al. Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity. Med Phys 2012; 39 (09) 5732-5739
    CrossrefPubMedGoogle Scholar
  • 32 Herlidou-Même S, Constans JM, Carsin B. , et al. MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging 2003; 21 (09) 989-993
    CrossrefPubMedGoogle Scholar
  • 33 Zhao Q, Shi CZ, Luo LP. Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes. Chin J Cancer Res 2014; 26 (04) 451-458
    PubMedGoogle Scholar
  • 34 Farhang Ghahremani M, Goossens S, Nittner D. , et al. p53 promotes VEGF expression and angiogenesis in the absence of an intact p21-Rb pathway. Cell Death Differ 2013; 20 (07) 888-897
    CrossrefPubMedGoogle Scholar
  • 35 Vousden KH, Lu X. Live or let die: the cell's response to p53. Nat Rev Cancer 2002; 2 (08) 594-604
    CrossrefPubMedGoogle Scholar