CT texture analysis in gastric cancers_West China Medical Journal

Authors：

FAN Yuan ¹ , LI Hongjiang ¹ , CHEN Xiuyuan ¹ , HE Tao ¹ , WANG Zhu ² ,  CHEN Jie ¹

1. Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Laboratory of Molecular Diagnosis of Cancer, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

CHEN Jie, Email: chenjiewestchina@163.com

Keywords：

Radiomics; Computed tomography texture analysis; Gastric carcinoma

DOI：

10.7507/1002-0179.202001184

Video：

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CT texture analysis (CTTA) can objectively evaluate the heterogeneity of tissues and their lesions beyond the ability of subjective visual interpretation by extracting the texture features of CT images, then performing analysis and quantitative and objective evaluation, reflecting the tissue micro environmental information. This article reviews the recent studies on the applications of CTTA in gastric cancers, in the aspects of identification of gastric tumors, prediction of stage, correlation with Lauren classification, prediction of occult peritoneal carcinomatosis, evaluation of efficacy and prognosis, and prediction of biomarkers. It is regarded that CTTA has a good application prospect in gastric cancers.

Citation： FAN Yuan, LI Hongjiang, CHEN Xiuyuan, HE Tao, WANG Zhu, CHEN Jie. CT texture analysis in gastric cancers. West China Medical Journal, 2020, 35(11): 1404-1408. doi: 10.7507/1002-0179.202001184 Copy

1.	Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012 v1.0. Cancer incidence and mortality worldwide. (2018-10-04)[2020-01-02]. https://gco.iarc.fr/today/data/factsheets/cancers/7-Stomach-fact-sheet.pdf.
2.	孙可欣, 郑荣寿, 张思维, 等. 2015年中国分地区恶性肿瘤发病和死亡分析. 中国肿瘤, 2019, 28(1): 1-11.
3.	Smyth EC, Verheij M, Allum W, et al. Gastric cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2016, 27(5): v38-v49.
4.	国家卫生健康委员会. 胃癌诊疗规范(2018年版). 中华消化病与影像杂志(电子版), 2019, 9(3): 118-144.
5.	严宇, 宋威. 颜色和纹理混合描述符图像检索方法. 计算机科学与探索, 2016(11): 120-129.
6.	Khademi A, Krishnan S. Medical image texture analysis: a case study with small bowel, retinal and mammogram images//2008 Canadian Conference on Electrical and Computer Engineering. Niagara Falls, ON: IEEE, 2008: 1949-1954. doi: 10.1109/CCECE.2008.4564884.
7.	Filho PLP, Oliveira LS, Britto AS, et al. Forest species recognition using color-based features//2010 20th International Conference on Pattern Recognition. Istanbul: IEEE, 2010: 4178-4181. doi: 10.1109/ICPR.2010.1015.
8.	Jhuria M, Kumar A, Borse R. Image processing for smart farming: detection of disease and fruit grading//2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013). Shimla: IEEE, 2013: 521-526. doi: 10.1109/ICIIP.2013.6707647.
9.	杨采薇, 蒋涵羽, 宋彬, 等. 影像组学在胰腺肿瘤病变影像学评估中的研究进展. 放射学实践, 2019, 34(9): 963-968.
10.	Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
11.	Ding J, Xing Z, Jiang Z, et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol, 2018, 103(6): 51-56.
12.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
13.	Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp, 2018, 2(1): 36.
14.	Sun ZQ, Hu SD, Li J, et al. Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images. J Xray Sci Technol, 2019, 27(6): 1021-1031.
15.	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.
16.	Ma ZL, Fang MJ, Huang YQ, et al. CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. Eur J Radiol, 2017, 91(91): 142-147.
17.	Ajani JA, Bentrem DJ, Besh S, et al. Gastric cancer, version 2.2013: featured updates to the NCCN Guidelines. J Natl Compr Canc Netw, 2013, 11(5): 531-546.
18.	Ajani JA, D’Amico TA, Almhanna K, et al. Gastric cancer, Version 3.2016, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2016, 14(10): 1286-1312.
19.	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.
20.	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.
21.	Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand, 1965, 64: 31-49.
22.	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.
23.	Thomassen I, van Gestel YR, Van Ramshorst B, et al. Peritoneal carcinomatosis of gastric origin: a population-based study on incidence, survival and risk factors. Int J Cancer, 2014, 134(3): 622-628.
24.	Ji ZH, Peng KW, Yu Y, et al. Current status and future prospects of clinical trials on CRS?+?HIPEC for gastric cancer peritoneal metastases. Int J Hyperthermia, 2017, 33(5): 562-570.
25.	Gill RS, Al-Adra DP, Nagendran J, et al. Treatment of gastric cancer with peritoneal carcinomatosis by cytoreductive surgery and HIPEC: a systematic review of survival, mortality, and morbidity. J Surg Oncol, 2011, 104(6): 692-698.
26.	Rivera F, Romero C, Jimenez-Fonseca P, et al. Phase II study to evaluate the efficacy of Trastuzumab in combination with capecitabine and oxaliplatin in first-line treatment of HER2-positive advanced gastric cancer: HERXO trial. Cancer Chemother Pharmacol, 2019, 83(6): 1175-1181.
27.	Coccolini F, Cotte E, Glehen O, et al. Intraperitoneal chemotherapy in advanced gastric cancer. Meta-analysis of randomized trials. Eur J Surg Oncol, 2014, 40(1): 12-26.
28.	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.
29.	Dong D, Tang L, Li ZY, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol, 2019, 30(3): 431-438.
30.	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.
31.	Cunningham D, Allum WH, Stenning SP, et al. Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med, 2006, 355(1): 11-20.
32.	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.
33.	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.
34.	Hou Z, Yang Y, Li S, et al. Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis. Quant Imaging Med Surg, 2018, 8(4): 410-420.
35.	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.
36.	He X, Chen Z, Fu T, et al. Ki-67 is a valuable prognostic predictor of lymphoma but its utility varies in lymphoma subtypes: evidence from a systematic meta-analysis. BMC Cancer, 2014, 14: 153.
37.	Liu S, Shi H, Ji C, et al. CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers. Sci Rep, 2018, 8(1): 11844.

1. Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012 v1.0. Cancer incidence and mortality worldwide. (2018-10-04)[2020-01-02]. https://gco.iarc.fr/today/data/factsheets/cancers/7-Stomach-fact-sheet.pdf.
2. 孙可欣, 郑荣寿, 张思维, 等. 2015年中国分地区恶性肿瘤发病和死亡分析. 中国肿瘤, 2019, 28(1): 1-11.
3. Smyth EC, Verheij M, Allum W, et al. Gastric cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2016, 27(5): v38-v49.
4. 国家卫生健康委员会. 胃癌诊疗规范(2018年版). 中华消化病与影像杂志(电子版), 2019, 9(3): 118-144.
5. 严宇, 宋威. 颜色和纹理混合描述符图像检索方法. 计算机科学与探索, 2016(11): 120-129.
6. Khademi A, Krishnan S. Medical image texture analysis: a case study with small bowel, retinal and mammogram images//2008 Canadian Conference on Electrical and Computer Engineering. Niagara Falls, ON: IEEE, 2008: 1949-1954. doi: 10.1109/CCECE.2008.4564884.
7. Filho PLP, Oliveira LS, Britto AS, et al. Forest species recognition using color-based features//2010 20th International Conference on Pattern Recognition. Istanbul: IEEE, 2010: 4178-4181. doi: 10.1109/ICPR.2010.1015.
8. Jhuria M, Kumar A, Borse R. Image processing for smart farming: detection of disease and fruit grading//2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013). Shimla: IEEE, 2013: 521-526. doi: 10.1109/ICIIP.2013.6707647.
9. 杨采薇, 蒋涵羽, 宋彬, 等. 影像组学在胰腺肿瘤病变影像学评估中的研究进展. 放射学实践, 2019, 34(9): 963-968.
10. Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
11. Ding J, Xing Z, Jiang Z, et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol, 2018, 103(6): 51-56.
12. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
13. Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp, 2018, 2(1): 36.
14. Sun ZQ, Hu SD, Li J, et al. Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images. J Xray Sci Technol, 2019, 27(6): 1021-1031.
15. 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.
16. Ma ZL, Fang MJ, Huang YQ, et al. CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. Eur J Radiol, 2017, 91(91): 142-147.
17. Ajani JA, Bentrem DJ, Besh S, et al. Gastric cancer, version 2.2013: featured updates to the NCCN Guidelines. J Natl Compr Canc Netw, 2013, 11(5): 531-546.
18. Ajani JA, D’Amico TA, Almhanna K, et al. Gastric cancer, Version 3.2016, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2016, 14(10): 1286-1312.
19. 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.
20. 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.
21. Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand, 1965, 64: 31-49.
22. 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.
23. Thomassen I, van Gestel YR, Van Ramshorst B, et al. Peritoneal carcinomatosis of gastric origin: a population-based study on incidence, survival and risk factors. Int J Cancer, 2014, 134(3): 622-628.
24. Ji ZH, Peng KW, Yu Y, et al. Current status and future prospects of clinical trials on CRS?+?HIPEC for gastric cancer peritoneal metastases. Int J Hyperthermia, 2017, 33(5): 562-570.
25. Gill RS, Al-Adra DP, Nagendran J, et al. Treatment of gastric cancer with peritoneal carcinomatosis by cytoreductive surgery and HIPEC: a systematic review of survival, mortality, and morbidity. J Surg Oncol, 2011, 104(6): 692-698.
26. Rivera F, Romero C, Jimenez-Fonseca P, et al. Phase II study to evaluate the efficacy of Trastuzumab in combination with capecitabine and oxaliplatin in first-line treatment of HER2-positive advanced gastric cancer: HERXO trial. Cancer Chemother Pharmacol, 2019, 83(6): 1175-1181.
27. Coccolini F, Cotte E, Glehen O, et al. Intraperitoneal chemotherapy in advanced gastric cancer. Meta-analysis of randomized trials. Eur J Surg Oncol, 2014, 40(1): 12-26.
28. 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.
29. Dong D, Tang L, Li ZY, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol, 2019, 30(3): 431-438.
30. 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.
31. Cunningham D, Allum WH, Stenning SP, et al. Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med, 2006, 355(1): 11-20.
32. 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.
33. 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.
34. Hou Z, Yang Y, Li S, et al. Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis. Quant Imaging Med Surg, 2018, 8(4): 410-420.
35. 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.
36. He X, Chen Z, Fu T, et al. Ki-67 is a valuable prognostic predictor of lymphoma but its utility varies in lymphoma subtypes: evidence from a systematic meta-analysis. BMC Cancer, 2014, 14: 153.
37. Liu S, Shi H, Ji C, et al. CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers. Sci Rep, 2018, 8(1): 11844.

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