Preliminary study on differential diagnosis of liver cancer and hepatic hemangioma by texture analysis of non-enhanced CT images_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

WANG Yongqin ^1,2 , HUANG Zixing ¹ , YUAN Fang ¹ ,  SONG Bin ¹

1. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;
2. Department of Radiology, Affiliated Hospital of Chengdu University, Chengdu 610081, P.R.China;

Corresponding author：

SONG Bin, Email: anicesong@vip.sina.com

Keywords：

CT; liver cancer; hepatic hemangioma; texture analysis; image processing

DOI：

10.7507/1007-9424.201611102

Video：

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Objective To determine feasibility of texture analysis of non-enhanced CT scan for differential diagnosis of liver cancer and hepatic hemangioma. Methods Fifty-six patients with liver cancer or hepatic hemangioma confirmed by pathology were enrolled in this retrospective study. After exclusion of images of 4 patients with artifacts and lesion diameter less than 1.0 cm, images of 52 patients (57 lesions) were available to further analyze. Texture features derived from the gray-level histogram, co-occurrence and run-length matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher, probability of classification error and average correlation (POE+ACC), and mutual information coefficients (MI) were used to extract 10 optimized texture features. The texture characteristics were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) provided by B11 module in the Mazda software, the minimum error probability of differential diagnosis of liver cancer and hepatic hemangioma was calculated. Most discriminating features (MDF) of LDA was applied to K nearest neighbor classification (KNN); NDA to extract the data used in artificial neural network (ANN) for differential diagnosis. Results The NDA/ANN-POE+ACC was the best for identifying liver cancer and hepatic hemangioma, and the minimum error probability was the lowest as compared with the LDA/KNN-Fisher, LDA/KNN-POE+ACC, LDA/KNN-MI, NDA/ANN-Fisher, and NDA/ANN-MI respectively, the differences were statistically significant (χ²=4.56, 4.26, 3.14, 3.14, 3.33;P=0.020, 0.018, 0.026, 0.026, 0.022). Conclusions The minimum error probability is low for different texture feature selection methods and different analysis methods of Mazda texture analysis software in identifying liver cancer and hepatic hemangioma, and NDA/ANN-POE+ACC method is best. So it is feasible to use texture analysis of non-enhanced CT images to identify liver cancer and hepatic hemangioma.

Citation： WANGYongqin, HUANGZixing, YUANFang, SONGBin. Preliminary study on differential diagnosis of liver cancer and hepatic hemangioma by texture analysis of non-enhanced CT images. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2017, 24(2): 254-258. doi: 10.7507/1007-9424.201611102 Copy

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23.	Materka A, Szypinski P. MaZda User’s Manual [MaZda 4.6. download link].1999–2006. Available at: http://www.eletel.p.lodz. pl/mazda/download/MaZda.zip.
24.	Tourassi GD, Frederick ED, Markey MK,et al. Application of the mutual information criterion for feature selection in computer-aided diagnosis. Med Phys, 2001, 28(12): 2394-2402.
25.	Hajek M, Dezortova M, Materka A,et al. Texture analysis for magnetic resonance imaging. Prague: Med4 Publishing S.R.O, 2006: 234.
26.	Mayerhoefer ME, Breitenseher M, Amann G,et al. Are signal intensity and homogeneity useful parameters for distinguishing between benign and malignant soft tissue masses on MR images? Objective evaluation by means of texture analysis. Magn Reson Imaging, 2008, 26(9): 1316-1322.

1. van Ginneken B, ter Haar Romeny BM, Viergever MA. Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging, 2001, 20(12): 1228-1241.
2. 陈美龙, 戴声奎. 基于 GLCM 算法的图像纹理特征分析. 通信技术, 2012, 45(42): 108-111.
3. Bahl G, Cruite I, Wolfson T,et al. Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images. J Magn Reson Imaging, 2012, 36(5): 1154-1161.
4. Mayerhoefer ME, Schima W, Trattnig S,et al. Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas. J Magn Reson Imaging, 2010, 32(2): 352-359.
5. Nachimuthu DS, Baladhandapani A. Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI. J Digit Imaging, 2014, 27(4): 496-506.
6. Orphanidou-Vlachou E, Vlachos N, Davies NP,et al. Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children. NMR Biomed, 2014, 27(6): 632-639.
7. Holli K, Lääperi AL, Harrison L,et al. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol, 2010, 17(2): 135-141.
8. Yan L, Liu Z, Wang G,et al. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol, 2015, 22(9): 1115-1121.
9. Goh V, Ganeshan B, Nathan P,et al. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology, 2011, 261(1): 165-171.
10. Fruehwald-Pallamar J, Czerny C, Holzer-Fruehwald L,et al. Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla. NMR Biomed, 2013, 26(11): 1372-1379.
11. Michoux N, Guillet A, Rommel D,et al. Texture analysis of T2-weighted MR images to assess acute inflammation in brain MS lesions. PLoS One, 2015, 10(12): e0145497.
12. Ginsburg SB, Zhao J, Humphries S,et al. Texture-based quantification of centrilobular emphysema and centrilobular nodularity in longitudinal CT scans of current and former smokers. Acad Radiol, 2016, 23(11): 1349-1358.
13. Mattonen SA, Tetar S, Palma DA,et al. Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy. J Med Imaging (Bellingham), 2015, 2(4): 041010.
14. Harrison LC, Luukkaala T, Pertovaara H,et al. Non-Hodgkin lymphoma response evaluation with MRI texture classification. J Exp Clin Cancer Res, 2009, 28: 87.
15. Fu S, Chen S, Liang C,et al. Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients’ selection of transcatheter arterial chemoembolization and sorafenib. Oncotarget, 2016 Nov 29. [Epub ahead of print].
16. Rao SX, Lambregts DM, Schnerr RS,et al. CT texture analysis in colorectal liver metastases: A better way than size and volume measurements to assess response to chemotherapy? United European Gastroenterol J, 2016, 4(2): 257-263.
17. Chen G, Jespersen S, Pedersen M,et al. Evaluation of anti-vascular therapy with texture analysis. Anticancer Res, 2005, 25: 3399-3405.
18. Kim JH, Ko ES, Lim Y,et al. Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology, 2016 Oct 4: 160261. [Epub ahead of print].
19. Gastounioti A, Conant EF, Kontos D,et al. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Breast Cancer Res, 2016, 18(1): 91.
20. Strzelecki M, Szczypinski P, Materka A,et al. A software tool for automatic classification and segmentation of 2D/3D medical images. Nucl Instrum Methods Phys Res A, 2013, 702(1): 137-140.
21. Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging, 2004, 22(1): 81-91.
22. Szczypiński PM, Strzelecki M, Materka A,et al. MaZda—a software package for image texture analysis. Comput Methods Programs Biomed, 2009, 94(1): 66-76.
23. Materka A, Szypinski P. MaZda User’s Manual [MaZda 4.6. download link].1999–2006. Available at: http://www.eletel.p.lodz. pl/mazda/download/MaZda.zip.
24. Tourassi GD, Frederick ED, Markey MK,et al. Application of the mutual information criterion for feature selection in computer-aided diagnosis. Med Phys, 2001, 28(12): 2394-2402.
25. Hajek M, Dezortova M, Materka A,et al. Texture analysis for magnetic resonance imaging. Prague: Med4 Publishing S.R.O, 2006: 234.
26. Mayerhoefer ME, Breitenseher M, Amann G,et al. Are signal intensity and homogeneity useful parameters for distinguishing between benign and malignant soft tissue masses on MR images? Objective evaluation by means of texture analysis. Magn Reson Imaging, 2008, 26(9): 1316-1322.

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Preliminary study on differential diagnosis of liver cancer and hepatic hemangioma by texture analysis of non-enhanced CT images

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