Prediction of the therapeutic response after target-combined chemotherapy treatment for patients with liver metastasis from colorectal cancer using computed tomography texture analysis_Journal of Biomedical Engineering

Authors：

ZHANG Jing ¹ , ZHOU Yuwen ² , QIU Meng ² ,  WU Bing ¹

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

Corresponding?author：

WU Bing, Email: bingwu69@163.com

Keywords：

computed tomography; texture analysis; colorectal cancer liver metastasis; target-combined chemotherapy; therapeutic response

DOI：

10.7507/1001-5515.201801062

Video：

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This study aims to investigate the value of pre-treatment computed tomography (CT) texture analysis in predicting therapeutic response of liver metastasis from colorectal cancer after combined targeting chemotherapy. A total of 82 patients with colorectal cancer liver metastases who underwent chemotherapy combined with targeted therapy (cetuximab) between March 2011 and October 2017 comprised this retrospective study population. According to the RECIST1.1, the best curative effect evaluation of patients was recorded. Complete response (CR) and partial response (PR) were assigned to the response group, and the stable disease (SD) and progressive disease (PD) were assigned to the non-response group. The CT texture analysis was based on the Omini-Kinetics software, and the three-dimensional (3D) texture analysis was performed on the marked lesion on portal phase. The differences of texture parameters between the response group and the non-response group were compared. The receiver operating characteristic (ROC) curves were depicted on the parameters which with statistically difference, to characterize value in predicting the response to target-combined chemotherapy. The differences of Entropy, Energy, Variance, std. Deviation, Quantile95 and sumEntropy between the two groups in pre-treatment lesions were significant (P < 0.05). And lesions with higher Entropy, lower Energy, higher Variance, higher std Deviation and higher sumEntropy seemed to indicate a better therapeutic response. When sumEntropy > 0.867, good diagnostic efficiency could be obtained, with sensitivity of 60.5% and specificity of 79.5%, respectively. In conclusion, texture parameters derived from baseline CT images of colorectal cancer liver metastasis have the potential value acting as imaging biomarkers in predicting tumor response to combined target chemotherapy.

Citation： ZHANG Jing, ZHOU Yuwen, QIU Meng, WU Bing. Prediction of the therapeutic response after target-combined chemotherapy treatment for patients with liver metastasis from colorectal cancer using computed tomography texture analysis. Journal of Biomedical Engineering, 2018, 35(6): 914-920. doi: 10.7507/1001-5515.201801062 Copy

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2.	Siegel R L, Miller K D, Jemal A. Cancer statistics, 2018. CA Cancer J Clin, 2018, 68(1): 7-30.
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4.	Simpson A L, Doussot A, Creasy J M, et al. Computed tomography image texture: a noninvasive prognostic marker of hepatic recurrence after hepatectomy for metastatic colorectal cancer. Ann Surg Oncol, 2017, 24(9): 2482-2490.
5.	郭桂芳, 夏良平, 張蓓, 等. 西妥昔單抗聯合化療對 53 例晚期結直腸癌的療效. 癌癥, 2009, 28(12): 1317-1323.
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10.	Eisenhauer E A, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer, 2009, 45(2): 228-247.
11.	Wagner F, Hakami Y A, Warnock G, et al. Comparison of contrast-enhanced CT and [18F] FDG PET/CT analysis using kurtosis and skewness in patients with primary colorectal cancer. Mol Imaging Biol, 2017, 19(5): 795-803.
12.	Kim J H, Ko E S, Lim Y, et al. Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology, 2017, 282(3): 665-675.
13.	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.
14.	Raman S P, Chen Yifei, Schroeder J L, et al. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol, 2014, 21(12): 1587-1596.
15.	Smith A D, Gray M R, del Campo S M, et al. Predicting overall survival in patients with metastatic melanoma on antiangiogenic therapy and RECIST stable disease on initial posttherapy images using CT texture analysis. Am J Roentgenol, 2015, 205(3): W283-W293.
16.	Lubner M G, Stabo N, Abel E J, et al. CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. Am J Roentgenol, 2016, 207(1): 96-105.
17.	Yu H, Scalera J, Khalid M, et al. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol, 2017, 42(10): 2470-2478.
18.	于魯, 夏翃, 劉衛芳. 基于磁共振圖像海馬三維紋理特征的阿爾茨海默病及健康對照的分類研究. 生物醫學工程學雜志, 2016, 33(6): 1090-1094.
19.	Ahn S J, Kim J H, Park S J, et al. Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol, 2016, 85(10): 1867-1874.
20.	Chaddad A, Tanougast C. Texture analysis of abnormal cell images for predicting the continuum of colorectal cancer. Anal Cell Pathol (Amst), 2017: 8428102.
21.	Beckers R C J, Lambregts D M J, Schnerr R S, et al. Whole liver CT texture analysis to predict the development of colorectal liver metastases–A multicentre study. Eur J Radiol, 2017, 92: 64-71.
22.	Lee S J, Zea R, Kim D H, et al. CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer. Eur Radiol, 2018, 28(4): 1520-1528.
23.	Cui C, Cai H, Liu L, et al. Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging. Eur Radiol, 2011, 21(11): 2318-2325.
24.	Grove O, Berglund A E, Schabath M B, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One, 2015, 10(3): e0118261.
25.	Yang Xiangyu, Knopp M V. Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol, 2011: 732848.
26.	Mayerhoefer M E, 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.
27.	Ganeshan B, Miles K A. Quantifying tumour heterogeneity with CT. Cancer Imaging, 2013, 13(1): 140-149.
28.	Ng F, Ganeshan B, Kozarski R, et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology, 2013, 266(1): 177-184.
29.	Lubner M G, Stabo N, Lubner S J, 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.
30.	Bezy-Wendling J, Kretowski M, Rolland Y, et al. Toward a better understanding of texture in vascular CT scan simulated images. IEEE Trans Biomed Eng, 2001, 48(1): 120-124.
31.	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.
32.	Chen S, Zhu Y, Liu Z, et al. Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: A retrospective pilot study. Eur J Radiol, 2017, 90: 198-204.

1. Chen Wanqing, Zheng Rongshou, Baade P D, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
2. Siegel R L, Miller K D, Jemal A. Cancer statistics, 2018. CA Cancer J Clin, 2018, 68(1): 7-30.
3. Shah S A, Bromberg R, Coates A, et al. Survival after liver resection for metastatic colorectal carcinoma in a large population. J Am Coll Surg, 2007, 205(5): 676-683.
4. Simpson A L, Doussot A, Creasy J M, et al. Computed tomography image texture: a noninvasive prognostic marker of hepatic recurrence after hepatectomy for metastatic colorectal cancer. Ann Surg Oncol, 2017, 24(9): 2482-2490.
5. 郭桂芳, 夏良平, 張蓓, 等. 西妥昔單抗聯合化療對 53 例晚期結直腸癌的療效. 癌癥, 2009, 28(12): 1317-1323.
6. Humblet Y, Peeters M, Gelderblom H, et al. Cetuximab dose-escalation in patients (pts) with metastatic colorectal cancer (mCRC) with no or slight skin reactions on standard treatment: pharmacokinetic (PK), pharmacodynamic (PD) and efficacy data from the EVEREST study. EJC Supplements, 2007, 5(4): 240.
7. Andreyev H J N, Norman A R, Cunningham D, et al. Kirsten ras mutations in patients with colorectal cancer: the 'RASCAL II' study. Br J Cancer, 2001, 85(5): 692-696.
8. 郭桂芳, 夏良平, 徐瑞華, 等. 西妥昔單抗聯合化療治療晚期結直腸癌的生存分析及 KRAS 對療效的影響. 中山大學學報:醫學科學版, 2011, 32(5): 637-643.
9. Castellano G, Bonilha L, Li Lm, et al. Texture analysis of medical images. Clin Radiol, 2004, 59(12): 1061-1069.
10. Eisenhauer E A, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer, 2009, 45(2): 228-247.
11. Wagner F, Hakami Y A, Warnock G, et al. Comparison of contrast-enhanced CT and [18F] FDG PET/CT analysis using kurtosis and skewness in patients with primary colorectal cancer. Mol Imaging Biol, 2017, 19(5): 795-803.
12. Kim J H, Ko E S, Lim Y, et al. Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology, 2017, 282(3): 665-675.
13. 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.
14. Raman S P, Chen Yifei, Schroeder J L, et al. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol, 2014, 21(12): 1587-1596.
15. Smith A D, Gray M R, del Campo S M, et al. Predicting overall survival in patients with metastatic melanoma on antiangiogenic therapy and RECIST stable disease on initial posttherapy images using CT texture analysis. Am J Roentgenol, 2015, 205(3): W283-W293.
16. Lubner M G, Stabo N, Abel E J, et al. CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. Am J Roentgenol, 2016, 207(1): 96-105.
17. Yu H, Scalera J, Khalid M, et al. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol, 2017, 42(10): 2470-2478.
18. 于魯, 夏翃, 劉衛芳. 基于磁共振圖像海馬三維紋理特征的阿爾茨海默病及健康對照的分類研究. 生物醫學工程學雜志, 2016, 33(6): 1090-1094.
19. Ahn S J, Kim J H, Park S J, et al. Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol, 2016, 85(10): 1867-1874.
20. Chaddad A, Tanougast C. Texture analysis of abnormal cell images for predicting the continuum of colorectal cancer. Anal Cell Pathol (Amst), 2017: 8428102.
21. Beckers R C J, Lambregts D M J, Schnerr R S, et al. Whole liver CT texture analysis to predict the development of colorectal liver metastases–A multicentre study. Eur J Radiol, 2017, 92: 64-71.
22. Lee S J, Zea R, Kim D H, et al. CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer. Eur Radiol, 2018, 28(4): 1520-1528.
23. Cui C, Cai H, Liu L, et al. Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging. Eur Radiol, 2011, 21(11): 2318-2325.
24. Grove O, Berglund A E, Schabath M B, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One, 2015, 10(3): e0118261.
25. Yang Xiangyu, Knopp M V. Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol, 2011: 732848.
26. Mayerhoefer M E, 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.
27. Ganeshan B, Miles K A. Quantifying tumour heterogeneity with CT. Cancer Imaging, 2013, 13(1): 140-149.
28. Ng F, Ganeshan B, Kozarski R, et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology, 2013, 266(1): 177-184.
29. Lubner M G, Stabo N, Lubner S J, 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.
30. Bezy-Wendling J, Kretowski M, Rolland Y, et al. Toward a better understanding of texture in vascular CT scan simulated images. IEEE Trans Biomed Eng, 2001, 48(1): 120-124.
31. 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.
32. Chen S, Zhu Y, Liu Z, et al. Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: A retrospective pilot study. Eur J Radiol, 2017, 90: 198-204.

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Prediction of the therapeutic response after target-combined chemotherapy treatment for patients with liver metastasis from colorectal cancer using computed tomography texture analysis

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