Discrimination of HER2 expression in gastric cancer by texture analysis based on plain CT images: a feasibility study_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHANG Xiao ¹ , HUANG Wei ¹ ,  SONG Bin ²

1. Department of Radiology, People’s Hospital of Leshan City, Leshan, Sichuan 614000, P. R. China;
2. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding?author：

SONG Bin, Email: cjr.songbin@vip.163.com

Keywords：

gastric cancer; human epidermal growth factor receptor 2; texture analysis; MaZda software; misjudgment rate

DOI：

10.7507/1007-9424.202106102

Video：

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Objective To explore the correlation between the texture features of gastric cancer plain CT images and the expression of HER2.Methods A retrospective collection the datas of 62 patients with gastric cancer who underwent CT scans of the upper abdomen and (or) the whole abdomen from January 2017 to January 2021 in Leshan City People’s Hospital. The treatment method was surgery. The HER2 expression of gastric cancer tissue was detected after the operation. There were 45 male patients and 17 female patients. Lauren classification: 18 cases of intestinal type, 30 cases of diffuse type, and 14 cases of mixed type. Fifty-two cases were HER2 expression negative [age: (63.54±10.32) years], and 10 cases were HER2 expression positive [age: (61.70±11.70) years]. The MaZda module in the MaZda 4.6 version software was used to perform the image normalization, interest area delineation, texture feature extraction, and texture feature selection on the CT plain scan image, and perform texture feature discrimination and misjudgment rate analysis in the B11 module.Results There was no correlation between HER2 expression and age, gender of patients and Lauren classification of tumors (P>0.05). The analysis methods of nonlinear discriminant analysis (NDA)/artificial neural network (ANN), linear discriminant analysis (LDA)/1-nearest-neighbor (1-NN), principal component analysis (PCA)/1-NN, and raw-data analysis (RDA)/1-NN can better correspond to the CT plain scan texture feature parameters of gastric cancer and the expression level of HER2.Conclusions Texture analysis based on CT plain images has the potential to non-invasively detect the HER2 expression in gastric cancer. The best comprehensive performance texture discrimination method is NDA/ANN and LDA/1-NN.

Citation： ZHANG Xiao, HUANG Wei, SONG Bin. Discrimination of HER2 expression in gastric cancer by texture analysis based on plain CT images: a feasibility study. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(9): 1221-1226. doi: 10.7507/1007-9424.202106102 Copy

1.	Van Cutsem E, Bang YJ, Feng-Yi F, et al. HER2 screening data from ToGA: targeting HER2 in gastric and gastroesophageal junction cancer. Gastric Cancer, 2015, 18(3): 476-484.
2.	《胃癌 HER2 檢測指南》編寫組. 胃癌 HER2 檢測指南. 中華病理學雜志, 2011, 40(8): 553-557.
3.	胃癌 HER2 檢測指南(2016 版)專家組. 胃癌 HER2 檢測指南(2016 版). 中華病理學雜志. 2016, 45(8): 528-532.
4.	梁盼, 趙曦曈, 趙慧萍, 等. CT 對胃癌診斷和臨床應用價值. 中華放射學雜志, 2020, 54(11): 1141-1144.
5.	Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
6.	Szczypinski PM, Strzelecki M, Materka A. MaZda—a software for texture analysis. 2007 International Symposium on Information Technology Convergence. 2007: 245-249.
7.	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.
8.	Materka A SP. MaZda User’s Manual [MaZda 4.6. download link]. 1999. http://www.eletel.p.lodz.pl/programy/mazda/.
9.	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, 24(2): 254-258.
10.	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.
11.	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.
12.	Yoon HH, Sukov WR, Shi Q, et al. HER-2/neu gene amplification in relation to expression of HER2 and HER3 proteins in patients with esophageal adenocarcinoma. Cancer, 2014, 120(3): 415-424.
13.	Bang YJ, Van Cutsem E, Feyereislova A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet, 2010, 376(9742): 687-697.
14.	Tominaga N, Gotoda T, Hara M, et al. Five biopsy specimens from the proximal part of the tumor reliably determine HER2 protein expression status in gastric cancer. Gastric Cancer, 2016, 19(2): 553-560.
15.	Gullo I, Grillo F, Molinaro L, et al. Minimum biopsy set for HER2 evaluation in gastric and gastro-esophageal junction cancer. Endosc Int Open, 2015, 3(2): E165-E170.
16.	俞悅, 周愛萍, 曾益新. HER2 陽性胃癌的治療研究進展. 中國腫瘤臨床, 2017, 44(2): 59-63.
17.	Materka A, Strzelecki M. Texture analysis methods - a review. COST B11 report, 1998. https://www.researchgate.net/publication/249723259_Texture_Analysis_Methods_-_A_Review.
18.	陳瑾, 王海屹, 葉慧義. 紋理分析在腫瘤影像學中的研究進展. 中華放射學雜志, 2017, 51(12): 979-982.
19.	Yi X, Guan X, Chen C, et al. Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma. J Cancer, 2018, 9(19): 3577-3582.
20.	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.
21.	Court LE, Fave X, Mackin D, et al. Computational resources for radiomics. Transl Cancer Res, 2016, 5(4): 340-348.

1. Van Cutsem E, Bang YJ, Feng-Yi F, et al. HER2 screening data from ToGA: targeting HER2 in gastric and gastroesophageal junction cancer. Gastric Cancer, 2015, 18(3): 476-484.
2. 《胃癌 HER2 檢測指南》編寫組. 胃癌 HER2 檢測指南. 中華病理學雜志, 2011, 40(8): 553-557.
3. 胃癌 HER2 檢測指南(2016 版)專家組. 胃癌 HER2 檢測指南(2016 版). 中華病理學雜志. 2016, 45(8): 528-532.
4. 梁盼, 趙曦曈, 趙慧萍, 等. CT 對胃癌診斷和臨床應用價值. 中華放射學雜志, 2020, 54(11): 1141-1144.
5. Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
6. Szczypinski PM, Strzelecki M, Materka A. MaZda—a software for texture analysis. 2007 International Symposium on Information Technology Convergence. 2007: 245-249.
7. 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.
8. Materka A SP. MaZda User’s Manual [MaZda 4.6. download link]. 1999. http://www.eletel.p.lodz.pl/programy/mazda/.
9. 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, 24(2): 254-258.
10. 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.
11. 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.
12. Yoon HH, Sukov WR, Shi Q, et al. HER-2/neu gene amplification in relation to expression of HER2 and HER3 proteins in patients with esophageal adenocarcinoma. Cancer, 2014, 120(3): 415-424.
13. Bang YJ, Van Cutsem E, Feyereislova A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet, 2010, 376(9742): 687-697.
14. Tominaga N, Gotoda T, Hara M, et al. Five biopsy specimens from the proximal part of the tumor reliably determine HER2 protein expression status in gastric cancer. Gastric Cancer, 2016, 19(2): 553-560.
15. Gullo I, Grillo F, Molinaro L, et al. Minimum biopsy set for HER2 evaluation in gastric and gastro-esophageal junction cancer. Endosc Int Open, 2015, 3(2): E165-E170.
16. 俞悅, 周愛萍, 曾益新. HER2 陽性胃癌的治療研究進展. 中國腫瘤臨床, 2017, 44(2): 59-63.
17. Materka A, Strzelecki M. Texture analysis methods - a review. COST B11 report, 1998. https://www.researchgate.net/publication/249723259_Texture_Analysis_Methods_-_A_Review.
18. 陳瑾, 王海屹, 葉慧義. 紋理分析在腫瘤影像學中的研究進展. 中華放射學雜志, 2017, 51(12): 979-982.
19. Yi X, Guan X, Chen C, et al. Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma. J Cancer, 2018, 9(19): 3577-3582.
20. 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.
21. Court LE, Fave X, Mackin D, et al. Computational resources for radiomics. Transl Cancer Res, 2016, 5(4): 340-348.

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CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Discrimination of HER2 expression in gastric cancer by texture analysis based on plain CT images: a feasibility study

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