Research progress of radiomics in colorectal cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHANG Wei ¹ , HUANG Zixing ² , ZHANG Xiao ³ ,  SONG Bin ²

1. Department of Radiology, Sichuan Provincial Corps Hospital, Chinese People’s Armed Police Forces, Leshan, Sichuan 614000, P. R. China;
2. Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
3. Department of Radiology, The People’s Hospital of Leshan, Leshan, Sichuan 614000, P. R. China;

Corresponding?author：

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

Keywords：

colorectal cancer; radiomics; precision medicine; review

DOI：

10.7507/1007-9424.201812041

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To summarize the application of radiomics in colorectal cancer.Methods Relevant literatures about the therapeutic decision-making, therapeutic, and prognostic evaluation of colorectal cancer using radiomics were collected to make an review.Results Radiomics is of great value in preoperative stages, therapeutic, and prognostic evaluation in colorectal cancer.Conclusion Radiomics is an important part of precision medical imaging for colorectal cancer.

Citation： ZHANG Wei, HUANG Zixing, ZHANG Xiao, SONG Bin. Research progress of radiomics in colorectal cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2019, 26(4): 484-487. doi: 10.7507/1007-9424.201812041 Copy

1.	Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
2.	Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin, 2018, 68(1): 7-30.
3.	王敏, 宋彬, 黃子星, 等. 大數據時代的精準影像醫學: 放射組學. 中國普外基礎與臨床雜志, 2016, 23(6): 752-755.
4.	Yang L, Dong D, Fang M, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol, 2018, 28(5): 2058-2067.
5.	Lee SJ, Zea R, Kim DH, et al. CT texture features of liver paren-chyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer. Eur Radiol, 2018, 28(4): 1520-1528.
6.	Lovinfosse P, Polus M, Van Daele D, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging, 2018, 45(3): 365-375.
7.	Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology, 2018, 287(3): 833-843.
8.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
9.	Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
10.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
11.	Fernández-Martos C, Pericay C, Aparicio J, et al. Phase Ⅱ, randomized study of concomitant chemoradiotherapy followed by surgery and adjuvant capecitabine plus oxaliplatin (CAPOX) compared with induction CAPOX followed by concomitant chemoradiotherapy and surgery in magnetic resonance imaging-defined, locally advanced rectal cancer: grupo cancer de recto 3 study. J Clin Oncol, 2010, 28(5): 859-865.
12.	Liu L, Liu Y, Xu L, et al. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging, 2017, 45(6): 1798-1808.
13.	Liang C, Huang Y, He L, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and stage Ⅲ-Ⅳ colorectal cancer. Oncotarget, 2016, 7(21): 31401-31412.
14.	鐘紅霞. MRI紋理分析在預測直腸癌NCRT療效、TN分期、神經脈管侵犯方面的作用. 北京: 北京協和醫學院, 2017.
15.	Langman G, Patel A, Bowley DM. Size and distribution of lymph nodes in rectal cancer resection specimens. Dis Colon Rectum, 2015, 58(4): 406-414.
16.	Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2157-2164.
17.	van der Pool AE, Damhuis RA, Ijzermans JN, et al. Trends in incidence, treatment and survival of patients with stage Ⅳ colorectal cancer: a population-based series. Colorectal Dis, 2012, 14(1): 56-61.
18.	Manfredi S, Lepage C, Hatem C, et al. Epidemiology and mana-gement of liver metastases from colorectal cancer. Ann Surg, 2006, 244(2): 254-259.
19.	Wakai T, Shirai Y, Sakata J, et al. Histologic evaluation of intrahepatic micrometastases in patients treated with or without neoadjuvant chemotherapy for colorectal carcinoma liver metastasis. Int J Clin Exp Pathol, 2012, 5(4): 308-314.
20.	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.
21.	Labianca R, Nordlinger B, Beretta GD, et al. Primary colon cancer: ESMO Clinical Practice Guidelines for diagnosis, adjuvant treatment and follow-up. Ann Oncol, 2010, 21 Suppl 5: v70-v77.
22.	Wu B, Yao Y, Zhang K, et al. RAS testing and cetuximab treatment for metastatic colorectal cancer: a cost-effectiveness analysis in a setting with limited health resources. Oncotarget, 2017, 8(41): 71164-71172.
23.	王國蓉, 王志偉, 薛華丹, 等. CT紋理分析對鑒別直腸癌患者K-ras基因突變的可行性研究. 放射學實踐, 2017, 32(12): 1215-1220.
24.	Sauer R, Liersch T, Merkel S, et al. Preoperative versus post-operative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase Ⅲ trial after a median follow-up of 11 years. J Clin Oncol, 2012, 30(16): 1926-1933.
25.	De Cecco CN, Ciolina M, Caruso D, et al. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY), 2016, 41(9): 1728-1735.
26.	Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neo-adjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016, 22(21): 5256-5264.
27.	Nougaret S, Vargas HA, Lakhman Y, et al. Intravoxel incoherent motion-derived histogram metrics for assessment of response after combined chemotherapy and radiation therapy in rectal cancer: initial experience and comparison between single-section and volumetric analyses. Radiology, 2016, 280(2): 446-454.
28.	De Cecco CN, Ganeshan B, Ciolina M, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol, 2015, 50(4): 239-245.
29.	Jalil O, Afaq A, Ganeshan B, et al. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis, 2017, 19(4): 349-362.
30.	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.
31.	Miles KA, Ganeshan B, Griffiths MR, et al. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology, 2009, 250(2): 444-452.
32.	Ganeshan B, Miles KA, Young RC, et al. Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic hemodynamics and patient survival. Acad Radiol, 2007, 14(12): 1520-1530.
33.	魏煒, 劉振宇, 王碩, 等. 影像組學技術研究進展及其在結直腸癌中的臨床應用. 中國生物醫學工程學報, 2018, 37(5): 513-520.

1. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin, 2018, 68(1): 7-30.
3. 王敏, 宋彬, 黃子星, 等. 大數據時代的精準影像醫學: 放射組學. 中國普外基礎與臨床雜志, 2016, 23(6): 752-755.
4. Yang L, Dong D, Fang M, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol, 2018, 28(5): 2058-2067.
5. Lee SJ, Zea R, Kim DH, et al. CT texture features of liver paren-chyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer. Eur Radiol, 2018, 28(4): 1520-1528.
6. Lovinfosse P, Polus M, Van Daele D, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging, 2018, 45(3): 365-375.
7. Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology, 2018, 287(3): 833-843.
8. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
9. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
10. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
11. Fernández-Martos C, Pericay C, Aparicio J, et al. Phase Ⅱ, randomized study of concomitant chemoradiotherapy followed by surgery and adjuvant capecitabine plus oxaliplatin (CAPOX) compared with induction CAPOX followed by concomitant chemoradiotherapy and surgery in magnetic resonance imaging-defined, locally advanced rectal cancer: grupo cancer de recto 3 study. J Clin Oncol, 2010, 28(5): 859-865.
12. Liu L, Liu Y, Xu L, et al. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging, 2017, 45(6): 1798-1808.
13. Liang C, Huang Y, He L, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and stage Ⅲ-Ⅳ colorectal cancer. Oncotarget, 2016, 7(21): 31401-31412.
14. 鐘紅霞. MRI紋理分析在預測直腸癌NCRT療效、TN分期、神經脈管侵犯方面的作用. 北京: 北京協和醫學院, 2017.
15. Langman G, Patel A, Bowley DM. Size and distribution of lymph nodes in rectal cancer resection specimens. Dis Colon Rectum, 2015, 58(4): 406-414.
16. Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2157-2164.
17. van der Pool AE, Damhuis RA, Ijzermans JN, et al. Trends in incidence, treatment and survival of patients with stage Ⅳ colorectal cancer: a population-based series. Colorectal Dis, 2012, 14(1): 56-61.
18. Manfredi S, Lepage C, Hatem C, et al. Epidemiology and mana-gement of liver metastases from colorectal cancer. Ann Surg, 2006, 244(2): 254-259.
19. Wakai T, Shirai Y, Sakata J, et al. Histologic evaluation of intrahepatic micrometastases in patients treated with or without neoadjuvant chemotherapy for colorectal carcinoma liver metastasis. Int J Clin Exp Pathol, 2012, 5(4): 308-314.
20. 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.
21. Labianca R, Nordlinger B, Beretta GD, et al. Primary colon cancer: ESMO Clinical Practice Guidelines for diagnosis, adjuvant treatment and follow-up. Ann Oncol, 2010, 21 Suppl 5: v70-v77.
22. Wu B, Yao Y, Zhang K, et al. RAS testing and cetuximab treatment for metastatic colorectal cancer: a cost-effectiveness analysis in a setting with limited health resources. Oncotarget, 2017, 8(41): 71164-71172.
23. 王國蓉, 王志偉, 薛華丹, 等. CT紋理分析對鑒別直腸癌患者K-ras基因突變的可行性研究. 放射學實踐, 2017, 32(12): 1215-1220.
24. Sauer R, Liersch T, Merkel S, et al. Preoperative versus post-operative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase Ⅲ trial after a median follow-up of 11 years. J Clin Oncol, 2012, 30(16): 1926-1933.
25. De Cecco CN, Ciolina M, Caruso D, et al. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY), 2016, 41(9): 1728-1735.
26. Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neo-adjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016, 22(21): 5256-5264.
27. Nougaret S, Vargas HA, Lakhman Y, et al. Intravoxel incoherent motion-derived histogram metrics for assessment of response after combined chemotherapy and radiation therapy in rectal cancer: initial experience and comparison between single-section and volumetric analyses. Radiology, 2016, 280(2): 446-454.
28. De Cecco CN, Ganeshan B, Ciolina M, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol, 2015, 50(4): 239-245.
29. Jalil O, Afaq A, Ganeshan B, et al. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis, 2017, 19(4): 349-362.
30. 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.
31. Miles KA, Ganeshan B, Griffiths MR, et al. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology, 2009, 250(2): 444-452.
32. Ganeshan B, Miles KA, Young RC, et al. Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic hemodynamics and patient survival. Acad Radiol, 2007, 14(12): 1520-1530.
33. 魏煒, 劉振宇, 王碩, 等. 影像組學技術研究進展及其在結直腸癌中的臨床應用. 中國生物醫學工程學報, 2018, 37(5): 513-520.

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Research progress of radiomics in colorectal cancer

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