The texture analysis of CT images for the discrimination of hepatic epithelioid hemangioendothelioma and liver metastases of colon cancer: a preliminary study_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LIU Lu ^1,2 , WU Shuang ² ,  WU Bing ¹

1. Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
2. Department of Radiology, West China NO.4 Hospital of Sichuan University, Chengdu 610041, P. R. China;

Corresponding?author：

WU Bing, Email: bingwu69@163.com

Keywords：

texture analysis of CT image; hepatic epithelioid hemangioendothelioma; liver metastasis of colon cancer; differential diagnosis

DOI：

10.7507/1007-9424.201802007

Video：

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Objective To determine feasibility of texture analysis of CT images for the discrimination of hepatic epithelioid hemangioendothelioma (HEHE) and liver metastases of colon cancer. Methods CT images of 9 patients with 19 pathologically proved HEHEs and 18 patients with 38 liver metastases of colon cancer who received treatment in West China Hospital of Sichuan University from July 2012 to August 2016 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (22 parameters), run-length matrix (1 parameter), histogram (4 parameters), gradient (1 parameter), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (18 parameters), run-length matrix (2 parameters), histogram (7 parameters), gradient (2 parameters), and autoregressive model (1 parameter). In AP, the misclassification rates of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 38.60% (22/57), 42.11% (24/57), 8.77% (5/57), and 7.02% (4/57), respectively. In PVP, the misclassification rates of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 26.32% (15/57), 28.07% (16/57), 15.79% (9/57), and 10.53% (6/57), respectively. The misclassification rates of AP and PVP images had no statistical significance on the misclassification rates of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN between AP and PVP (P>0.05). Conclusion The texture analysis of CT images is feasible to identify HEHE and liver metastases of colon cancer.

Citation： LIU Lu, WU Shuang, WU Bing. The texture analysis of CT images for the discrimination of hepatic epithelioid hemangioendothelioma and liver metastases of colon cancer: a preliminary study. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2018, 25(4): 483-487. doi: 10.7507/1007-9424.201802007 Copy

1.	Makhlouf HR, Ishak KG, Goodman ZD. Epithelioid hemangioendothelioma of the liver: a clinicopathologic study of 137 cases. Cancer, 1999, 85(3): 562-582.
2.	Demuynck F, Morvan J, Brochart C, et al. Hepatic epithelioid hemangioendothelioma: a rare liver tumor. J Radiol, 2009, 90(7-8 Pt 1): 845-848.
3.	Matsushita M, Shimizu S, Nagasawa M, et al. Epithelioid hemangioendothelioma of the liver: imaging diagnosis of a rare hepatic tumor. Dig Surg, 2005, 22(6): 416-418.
4.	Campione S, Cozzolino I, Mainenti P, et al. Hepatic epithelioidhemangioendothelioma: pitfalls in the diagnosis on fine needle cytology and " small biopsy” and review of the literature. Pathol Res Pract, 2015, 211(9): 702-705.
5.	Ekfors TO, Joensuu K, Toivio I, et al. Fatal epithelioidhaemangioendothelioma presenting in the lung and liver. Virchows Arch A Pathol Anat Histopathol, 1986, 410(1): 9-16.
6.	梁曉, 張紅梅, 葉楓, 等. 肝臟上皮樣血管內皮細胞瘤的影像學和病理學特征. 中華腫瘤雜志, 2015, 37(4): 278-282.
7.	Lubner MG, Smith AD, Sandrasegaran K, et al. CT Texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
8.	Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. ClinRadiol, 2004, 59(12): 1061-1069.
9.	馬向宏, 袁冠前, 徐志華, 等. 常規MRI圖像紋理分析對顳葉癲癇海馬硬化的診斷價值. 磁共振成像, 2017, 8(10): 732-736.
10.	王永芹, 黃子星, 袁放, 等. CT平掃圖像紋理分析對肝癌與肝血管瘤鑒別診斷的初步研究. 中國普外基礎與臨床雜志, 2017, 22(2): 254-258.
11.	潘立陽. 原發性肝癌相關因素1∶2病例對照研究. 大連: 大連醫科大學, 2008.
12.	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.
13.	Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. MagnReson Imaging, 2004, 22(1): 81-91.
14.	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.
15.	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. AcadRadiol, 2015, 22(9): 1115-1121.
16.	黃燕琪, 馬澤蘭, 何蘭, 等. 基于CT圖像的紋理分析鑒別肝臟實性局灶性病變. 中國醫學影像學雜志, 2016, 24(4): 289-292, 297.
17.	Summers RM. Texture analysis in radiology: does the emperor have no clothes? AbdomRadiol (NY), 2017, 42(2): 342-345.
18.	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 MagnReson Imaging, 2010, 32(2): 352-359.
19.	Miles KA, Ganeshan B, Rodriguez-Justo M, et al. Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer. J Nucl Med, 2014, 55(3): 386-391.
20.	Lubner MG, Stabo N, Lubner SJ, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging, 2015, 40(7): 2331-2337.
21.	Ganeshan B, Goh V, Mandeville HC, et al. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology, 2013, 266(1): 326-336.
22.	Otrock ZK, Al-Kutoubi A, Kattar MM, et al. Spontaneous complete regression of hepatic epithelioid haemangioendothelioma. Lancet Oncol, 2006, 7(5): 439-441.
23.	劉權, 彭衛軍, 王堅. 肝上皮樣血管內皮瘤影像學表現及征象分析. 腫瘤影像學, 2014, 23(1): 8-13.
24.	Galletto Pregliasco A, Wendum D, Goumard C, et al. Hepatic epithelioid hemangioendothelioma. Clin Res Hepatol Gastroenterol, 2016, 40(2): 136-138.
25.	孫淑杰, 迮興宇, 趙新顏. 肝上皮樣血管內皮瘤文獻復習及臨床特點分析. 臨床和實驗醫學雜志, 2012, 11(9): 654-656.
26.	石雙任, 陳宏偉, 陸志華. 肝上皮樣血管內皮細胞瘤的影像表現. 臨床放射學雜志, 2011, 30(12): 1839-1842.
27.	Hu HJ, Jin YW, Jing QY, et al. Hepatic epithelioid hemangioendothelioma: dilemma and challenges in the preoperative diagnosis. World J Gastroenterol, 2016, 22(41): 9247-9250.

1. Makhlouf HR, Ishak KG, Goodman ZD. Epithelioid hemangioendothelioma of the liver: a clinicopathologic study of 137 cases. Cancer, 1999, 85(3): 562-582.
2. Demuynck F, Morvan J, Brochart C, et al. Hepatic epithelioid hemangioendothelioma: a rare liver tumor. J Radiol, 2009, 90(7-8 Pt 1): 845-848.
3. Matsushita M, Shimizu S, Nagasawa M, et al. Epithelioid hemangioendothelioma of the liver: imaging diagnosis of a rare hepatic tumor. Dig Surg, 2005, 22(6): 416-418.
4. Campione S, Cozzolino I, Mainenti P, et al. Hepatic epithelioidhemangioendothelioma: pitfalls in the diagnosis on fine needle cytology and " small biopsy” and review of the literature. Pathol Res Pract, 2015, 211(9): 702-705.
5. Ekfors TO, Joensuu K, Toivio I, et al. Fatal epithelioidhaemangioendothelioma presenting in the lung and liver. Virchows Arch A Pathol Anat Histopathol, 1986, 410(1): 9-16.
6. 梁曉, 張紅梅, 葉楓, 等. 肝臟上皮樣血管內皮細胞瘤的影像學和病理學特征. 中華腫瘤雜志, 2015, 37(4): 278-282.
7. Lubner MG, Smith AD, Sandrasegaran K, et al. CT Texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
8. Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. ClinRadiol, 2004, 59(12): 1061-1069.
9. 馬向宏, 袁冠前, 徐志華, 等. 常規MRI圖像紋理分析對顳葉癲癇海馬硬化的診斷價值. 磁共振成像, 2017, 8(10): 732-736.
10. 王永芹, 黃子星, 袁放, 等. CT平掃圖像紋理分析對肝癌與肝血管瘤鑒別診斷的初步研究. 中國普外基礎與臨床雜志, 2017, 22(2): 254-258.
11. 潘立陽. 原發性肝癌相關因素1∶2病例對照研究. 大連: 大連醫科大學, 2008.
12. 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.
13. Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. MagnReson Imaging, 2004, 22(1): 81-91.
14. 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.
15. 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. AcadRadiol, 2015, 22(9): 1115-1121.
16. 黃燕琪, 馬澤蘭, 何蘭, 等. 基于CT圖像的紋理分析鑒別肝臟實性局灶性病變. 中國醫學影像學雜志, 2016, 24(4): 289-292, 297.
17. Summers RM. Texture analysis in radiology: does the emperor have no clothes? AbdomRadiol (NY), 2017, 42(2): 342-345.
18. 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 MagnReson Imaging, 2010, 32(2): 352-359.
19. Miles KA, Ganeshan B, Rodriguez-Justo M, et al. Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer. J Nucl Med, 2014, 55(3): 386-391.
20. Lubner MG, Stabo N, Lubner SJ, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging, 2015, 40(7): 2331-2337.
21. Ganeshan B, Goh V, Mandeville HC, et al. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology, 2013, 266(1): 326-336.
22. Otrock ZK, Al-Kutoubi A, Kattar MM, et al. Spontaneous complete regression of hepatic epithelioid haemangioendothelioma. Lancet Oncol, 2006, 7(5): 439-441.
23. 劉權, 彭衛軍, 王堅. 肝上皮樣血管內皮瘤影像學表現及征象分析. 腫瘤影像學, 2014, 23(1): 8-13.
24. Galletto Pregliasco A, Wendum D, Goumard C, et al. Hepatic epithelioid hemangioendothelioma. Clin Res Hepatol Gastroenterol, 2016, 40(2): 136-138.
25. 孫淑杰, 迮興宇, 趙新顏. 肝上皮樣血管內皮瘤文獻復習及臨床特點分析. 臨床和實驗醫學雜志, 2012, 11(9): 654-656.
26. 石雙任, 陳宏偉, 陸志華. 肝上皮樣血管內皮細胞瘤的影像表現. 臨床放射學雜志, 2011, 30(12): 1839-1842.
27. Hu HJ, Jin YW, Jing QY, et al. Hepatic epithelioid hemangioendothelioma: dilemma and challenges in the preoperative diagnosis. World J Gastroenterol, 2016, 22(41): 9247-9250.

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The texture analysis of CT images for the discrimination of hepatic epithelioid hemangioendothelioma and liver metastases of colon cancer: a preliminary study

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