Application of image texture analysis in diagnosis and treatment of gastric cancer_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：

texture analysis; gastric cancer; image post-processing; review

DOI：

10.7507/1007-9424.202106103

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To summarize the application of image texture analysis in the diagnosis and treatment of gastric cancer.Methods Reviewed the literatures on the application of image texture analysis related methods in the diagnosis and treatment of gastric cancer, and summarized the value of texture analysis in the diagnosis and treatment of gastric cancer in terms of diagnosis, staging, curative effect evaluation, and prognosis prediction.Results Image texture analysis had been widely used in diagnosis, staging, curative effect evaluation, prognosis prediction of gastric cancer, and other related diagnosis and treatment applications.Conclusions Image texture analysis is an important part of the diagnosis and treatment of gastric cancer, which has a good development prospect.

Citation： ZHANG Xiao, HUANG Wei, SONG Bin. Application of image texture analysis in diagnosis and treatment of gastric cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(8): 1081-1085. doi: 10.7507/1007-9424.202106103 Copy

1.	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	鄭榮壽, 孫可欣, 張思維, 等. 2015 年中國惡性腫瘤流行情況分析. 中華腫瘤雜志, 2019, 41(1): 19-28.
3.	國家衛生健康委辦公廳發布 2021 年國家醫療質量安全改進目標. 上海護理, 2021, 21(3): 6.
4.	國家衛生健康委員會. 胃癌診療規范 (2018 年版). 中華消化病與影像雜志 (電子版), 2019, 9(3): 118-144.
5.	Amin MB, Edge SB, Greene FL, et al. AJCC Cancer Staging Manual. 8th Editioned. New York: Springer, 2017: 203-220.
6.	有上貴明, 柳田茂寛, 夏越祥次. 第 15 版胃癌取扱い規約のボイント. 日本臨牀, 2018.
7.	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.
8.	劉洋, 高劍波, 岳松偉, 等. 胃癌 MSCT 表現與 HER2 基因表達情況的相關性研究. 實用放射學雜志, 2015, 31(3): 427-430.
9.	Lordick F, Mariette C, Haustermans K, et al. Oesophageal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2016, 27(suppl 5): v50-v57.
10.	Lee G, I H, Kim SJ, et al. Clinical implication of PET/MR imaging in preoperative esophageal cancer staging: comparison with PET/CT, endoscopic ultrasonography, and CT. J Nucl Med, 2014, 55(8): 1242-1247.
11.	Materka A, Strzelecki M. Texture analysis methods-A review. Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 1998.
12.	劉偉, 李月卿. 醫學圖像紋理分析綜述. 泰山醫學院學報, 2006, 27(1): 78-80.
13.	El Naqa I, Grigsby P, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit, 2009, 42(6): 1162-1171.
14.	Agner SC, Soman S, Libfeld E, et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging, 2011, 24(3): 446-463.
15.	Karahaliou A, Vassiou K, Arikidis NS, et al. Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. Br J Radiol, 2010, 83(988): 296-309.
16.	Ganeshan B, Panayiotou E, Burnand K, et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol, 2012, 22(4): 796-802.
17.	Rao SX, Lambregts DM, Schnerr RS, et al. Whole-liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver? United European Gastroenterol J, 2014, 2(6): 530-538.
18.	Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. Clin Radiol, 2004, 59(12): 1061-1069.
19.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
20.	Lubner MG, Smith AD, Sandrasegaran K, et al. CT Texture analysis: Definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
21.	Court LE, Fave X, Mackin D, et al. Computational resources for radiomics. Transl Cancer Res, 2016, 5(4): 340-348.
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.	Brusan A, Durmaz FA, Yaman A, et al. iBEX: Modular open-source software for digital radiography. J Digit Imaging, 2020, 33(3): 708-721.
24.	Fang YH, Lin CY, Shih MJ, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int, 2014, 2014: 248505.
25.	Lee HN, Kim JI, Shin SY, et al. Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer. Br J Radiol, 2020, 93(1111): 20190827.
26.	Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging, 2012, 30(9): 1323-1341.
27.	Yushkevich PA, Gerig G. ITK-SNAP: An intractive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical images. IEEE Pulse, 2017, 8(4): 54-57.
28.	Rode JB, Ringel MM. Statistical software output in the classroom: A comparison of R and SPSS. Teach Psychol, 2019, 46(4): 319-327.
29.	Smith TC, Frank E. Introducing machine learning concepts with WEKA. Methods Mol Biol, 2016, 1418: 353-378.
30.	范園, 李宏江, 陳修遠, 等. CT 紋理分析在胃癌中的應用研究. 華西醫學, 2020, 35(11): 1404-1408.
31.	李曉樂. CT 紋理分析在胃癌中的應用研究進展. 長治醫學院學報, 2020, 34(6): 478-480.
32.	Sah BR, Owczarczyk K, Siddique M, et al. Radiomics in esophageal and gastric cancer. Abdom Radiol (NY), 2019, 44(6): 2048-2058.
33.	Ba-Ssalamah A, Muin D, Schernthaner R, et al. Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol, 2013, 82(10): e537-e543.
34.	Sun YW, Ji CF, Wang H, et al. Differentiating gastric cancer and gastric lymphoma using texture analysis (TA) of positron emission tomography (PET). Chin Med J (Engl), 2020, 134(4): 439-447.
35.	Ali H, Sharif M, Yasmin M, et al. Computer-based classification of chromoendoscopy images using homogeneous texture descriptors. Comput Biol Med, 2017, 88: 84-92.
36.	Ali H, Yasmin M, Sharif M, et al. Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. Comput Methods Programs Biomed, 2018, 157: 39-47.
37.	Hiroyasu T, Hayashinuma K, Ichikawa H, et al. Preprocessing with image denoising and histogram equalization for endoscopy image analysis using texture analysis. Annu Int Conf IEEE Eng Med Biol Soc, 2015, 2015: 789-792.
38.	Xu F, Ma X, Wang Y, et al. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation. Eur J Radiol, 2018, 107: 90-97.
39.	沈健, 宋斌, 儲海瑞, 等. CT 檢查紋理分析對胃腸間質瘤 c-KIT Exon 11 基因突變的預測價值. 中華消化外科雜志, 2020, 19(12): 1324-1331.
40.	任采月, 王升平, 任敏, 等. CT 紋理分析在胃腸間質瘤危險度分級價值的初步研究. 實用放射學雜志, 2019, 35(2): 228-231.
41.	劉靜妮, 翟亞楠, 鄭悠, 等. 計算機斷層增強掃描圖像結合動脈期紋理分析對胃腸道間質瘤危險度分級的評估價值. 中華消化雜志, 2020, 40(12): 831-836.
42.	李雙, 龍學穎, 劉慧. 胃間質瘤 CT 影像特征及紋理參數與危險度分級的相關性. 中南大學學報(醫學版), 2019, 44(3): 264-270.
43.	Lee MW, Kim GH. Diagnosing gastric mesenchymal tumors by digital endoscopic ultrasonography image analysis. Clin Endosc, 2021, 54(3): 324-328.
44.	Liu S, Shi H, Ji C, et al. Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging. Clin Radiol, 2018, 73(8): 756.e1-756.e9.
45.	Giganti F, Antunes S, Salerno A, et al. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol, 2017, 27(5): 1831-1839.
46.	Yardimci AH, Sel I, Bektas CT, et al. Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment. Jpn J Radiol, 2020, 38(6): 553-560.
47.	Yard?mc? AH, Ko?ak B, Turan Bekta? C, et al. Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion. Diagn Interv Radiol, 2020, 26(6): 515-522.
48.	Liu S, Zheng H, Zhang Y, et al. Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness. J Magn Reson Imaging, 2018, 47(1): 168-175.
49.	Qiao X, Li Z, Li L, et al. Preoperative T2-weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages. Abdom Radiol (NY), 2021, 46(4): 1487-1497.
50.	王小雷, 高玉青, 徐鶴, 等. 基于能譜 CT 紋理分析在預測胃癌術前淋巴結轉移中的價值. 蚌埠醫學院學報, 2021, 46(1): 21-24.
51.	Kim HY, Kim YH, Yun G, et al. Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer? PLoS One, 2018, 13(3): e0194755.
52.	Liu S, Liu S, Ji C, et al. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol, 2017, 27(12): 4951-4959.
53.	Liu S, Shi H, Ji C, et al. CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers. Sci Rep, 2018, 8(1): 11844.
54.	Yoon SH, Kim YH, Lee YJ, et al. Tumor heterogeneity in human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer assessed by CT texture analysis: association with survival after trastuzumab treatment. PLoS One, 2016, 11(8): e0161278.
55.	Giganti F, Marra P, Ambrosi A, et al. Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: Comparison with tumour regression grade at final histology. Eur J Radiol, 2017, 90: 129-137.
56.	陳佳, 丁茜琳, 王錚, 等. 基于常規 CT 圖像的紋理分析在進展期胃癌新輔助化療療效預測中的價值. 臨床放射學雜志, 2019, 38(11): 2100-2105.
57.	Jiang Y, Chen C, Xie J, et al. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine, 2018, 36: 171-182.
58.	Jiang Y, Yuan Q, Lv W, et al. Radiomic signature of ¹⁸F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics, 2018, 8(21): 5915-5928.
59.	Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, et al. Radiomics of CT features may be nonreproducible and redundant: Influence of CT acquisition parameters. Radiology, 2018, 288(2): 407-415.
60.	Meyer M, Ronald J, Vernuccio F, et al. Reproducibility of CT radiomic features within the same patient: Influence of radiation dose and CT reconstruction settings. Radiology, 2019, 293(3): 583-591.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. 鄭榮壽, 孫可欣, 張思維, 等. 2015 年中國惡性腫瘤流行情況分析. 中華腫瘤雜志, 2019, 41(1): 19-28.
3. 國家衛生健康委辦公廳發布 2021 年國家醫療質量安全改進目標. 上海護理, 2021, 21(3): 6.
4. 國家衛生健康委員會. 胃癌診療規范 (2018 年版). 中華消化病與影像雜志 (電子版), 2019, 9(3): 118-144.
5. Amin MB, Edge SB, Greene FL, et al. AJCC Cancer Staging Manual. 8th Editioned. New York: Springer, 2017: 203-220.
6. 有上貴明, 柳田茂寛, 夏越祥次. 第 15 版胃癌取扱い規約のボイント. 日本臨牀, 2018.
7. 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.
8. 劉洋, 高劍波, 岳松偉, 等. 胃癌 MSCT 表現與 HER2 基因表達情況的相關性研究. 實用放射學雜志, 2015, 31(3): 427-430.
9. Lordick F, Mariette C, Haustermans K, et al. Oesophageal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2016, 27(suppl 5): v50-v57.
10. Lee G, I H, Kim SJ, et al. Clinical implication of PET/MR imaging in preoperative esophageal cancer staging: comparison with PET/CT, endoscopic ultrasonography, and CT. J Nucl Med, 2014, 55(8): 1242-1247.
11. Materka A, Strzelecki M. Texture analysis methods-A review. Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 1998.
12. 劉偉, 李月卿. 醫學圖像紋理分析綜述. 泰山醫學院學報, 2006, 27(1): 78-80.
13. El Naqa I, Grigsby P, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit, 2009, 42(6): 1162-1171.
14. Agner SC, Soman S, Libfeld E, et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging, 2011, 24(3): 446-463.
15. Karahaliou A, Vassiou K, Arikidis NS, et al. Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. Br J Radiol, 2010, 83(988): 296-309.
16. Ganeshan B, Panayiotou E, Burnand K, et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol, 2012, 22(4): 796-802.
17. Rao SX, Lambregts DM, Schnerr RS, et al. Whole-liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver? United European Gastroenterol J, 2014, 2(6): 530-538.
18. Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. Clin Radiol, 2004, 59(12): 1061-1069.
19. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
20. Lubner MG, Smith AD, Sandrasegaran K, et al. CT Texture analysis: Definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
21. Court LE, Fave X, Mackin D, et al. Computational resources for radiomics. Transl Cancer Res, 2016, 5(4): 340-348.
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. Brusan A, Durmaz FA, Yaman A, et al. iBEX: Modular open-source software for digital radiography. J Digit Imaging, 2020, 33(3): 708-721.
24. Fang YH, Lin CY, Shih MJ, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int, 2014, 2014: 248505.
25. Lee HN, Kim JI, Shin SY, et al. Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer. Br J Radiol, 2020, 93(1111): 20190827.
26. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging, 2012, 30(9): 1323-1341.
27. Yushkevich PA, Gerig G. ITK-SNAP: An intractive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical images. IEEE Pulse, 2017, 8(4): 54-57.
28. Rode JB, Ringel MM. Statistical software output in the classroom: A comparison of R and SPSS. Teach Psychol, 2019, 46(4): 319-327.
29. Smith TC, Frank E. Introducing machine learning concepts with WEKA. Methods Mol Biol, 2016, 1418: 353-378.
30. 范園, 李宏江, 陳修遠, 等. CT 紋理分析在胃癌中的應用研究. 華西醫學, 2020, 35(11): 1404-1408.
31. 李曉樂. CT 紋理分析在胃癌中的應用研究進展. 長治醫學院學報, 2020, 34(6): 478-480.
32. Sah BR, Owczarczyk K, Siddique M, et al. Radiomics in esophageal and gastric cancer. Abdom Radiol (NY), 2019, 44(6): 2048-2058.
33. Ba-Ssalamah A, Muin D, Schernthaner R, et al. Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol, 2013, 82(10): e537-e543.
34. Sun YW, Ji CF, Wang H, et al. Differentiating gastric cancer and gastric lymphoma using texture analysis (TA) of positron emission tomography (PET). Chin Med J (Engl), 2020, 134(4): 439-447.
35. Ali H, Sharif M, Yasmin M, et al. Computer-based classification of chromoendoscopy images using homogeneous texture descriptors. Comput Biol Med, 2017, 88: 84-92.
36. Ali H, Yasmin M, Sharif M, et al. Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. Comput Methods Programs Biomed, 2018, 157: 39-47.
37. Hiroyasu T, Hayashinuma K, Ichikawa H, et al. Preprocessing with image denoising and histogram equalization for endoscopy image analysis using texture analysis. Annu Int Conf IEEE Eng Med Biol Soc, 2015, 2015: 789-792.
38. Xu F, Ma X, Wang Y, et al. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation. Eur J Radiol, 2018, 107: 90-97.
39. 沈健, 宋斌, 儲海瑞, 等. CT 檢查紋理分析對胃腸間質瘤 c-KIT Exon 11 基因突變的預測價值. 中華消化外科雜志, 2020, 19(12): 1324-1331.
40. 任采月, 王升平, 任敏, 等. CT 紋理分析在胃腸間質瘤危險度分級價值的初步研究. 實用放射學雜志, 2019, 35(2): 228-231.
41. 劉靜妮, 翟亞楠, 鄭悠, 等. 計算機斷層增強掃描圖像結合動脈期紋理分析對胃腸道間質瘤危險度分級的評估價值. 中華消化雜志, 2020, 40(12): 831-836.
42. 李雙, 龍學穎, 劉慧. 胃間質瘤 CT 影像特征及紋理參數與危險度分級的相關性. 中南大學學報(醫學版), 2019, 44(3): 264-270.
43. Lee MW, Kim GH. Diagnosing gastric mesenchymal tumors by digital endoscopic ultrasonography image analysis. Clin Endosc, 2021, 54(3): 324-328.
44. Liu S, Shi H, Ji C, et al. Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging. Clin Radiol, 2018, 73(8): 756.e1-756.e9.
45. Giganti F, Antunes S, Salerno A, et al. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol, 2017, 27(5): 1831-1839.
46. Yardimci AH, Sel I, Bektas CT, et al. Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment. Jpn J Radiol, 2020, 38(6): 553-560.
47. Yard?mc? AH, Ko?ak B, Turan Bekta? C, et al. Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion. Diagn Interv Radiol, 2020, 26(6): 515-522.
48. Liu S, Zheng H, Zhang Y, et al. Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness. J Magn Reson Imaging, 2018, 47(1): 168-175.
49. Qiao X, Li Z, Li L, et al. Preoperative T2-weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages. Abdom Radiol (NY), 2021, 46(4): 1487-1497.
50. 王小雷, 高玉青, 徐鶴, 等. 基于能譜 CT 紋理分析在預測胃癌術前淋巴結轉移中的價值. 蚌埠醫學院學報, 2021, 46(1): 21-24.
51. Kim HY, Kim YH, Yun G, et al. Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer? PLoS One, 2018, 13(3): e0194755.
52. Liu S, Liu S, Ji C, et al. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol, 2017, 27(12): 4951-4959.
53. Liu S, Shi H, Ji C, et al. CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers. Sci Rep, 2018, 8(1): 11844.
54. Yoon SH, Kim YH, Lee YJ, et al. Tumor heterogeneity in human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer assessed by CT texture analysis: association with survival after trastuzumab treatment. PLoS One, 2016, 11(8): e0161278.
55. Giganti F, Marra P, Ambrosi A, et al. Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: Comparison with tumour regression grade at final histology. Eur J Radiol, 2017, 90: 129-137.
56. 陳佳, 丁茜琳, 王錚, 等. 基于常規 CT 圖像的紋理分析在進展期胃癌新輔助化療療效預測中的價值. 臨床放射學雜志, 2019, 38(11): 2100-2105.
57. Jiang Y, Chen C, Xie J, et al. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine, 2018, 36: 171-182.
58. Jiang Y, Yuan Q, Lv W, et al. Radiomic signature of ¹⁸F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics, 2018, 8(21): 5915-5928.
59. Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, et al. Radiomics of CT features may be nonreproducible and redundant: Influence of CT acquisition parameters. Radiology, 2018, 288(2): 407-415.
60. Meyer M, Ronald J, Vernuccio F, et al. Reproducibility of CT radiomic features within the same patient: Influence of radiation dose and CT reconstruction settings. Radiology, 2019, 293(3): 583-591.

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Application of image texture analysis in diagnosis and treatment of gastric cancer

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