Advances on clinical application of radiomics and colorectal cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LI Qian ^1,2 , ZHOU Xinyuan ³ ,  WANG Xiaodong ¹ , LI Li ¹

1. Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. West China School of Medicine in Sichuan University, Chengdu 610041, P. R. China;
3. School of Computer Science, Sichuan University, Chengdu 610065, P. R. China;

Corresponding author：

WANG Xiaodong, Email: lockwan@163.com

Keywords：

radiomics; colorectal cancer; precision medicine; artificial intelligence

DOI：

10.7507/1007-9424.201903009

Video：

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Objective To review the progress of radiomics in the field of colorectal cancer in recent years and summarize its value in the imaging diagnosis of colorectal cancer.Methods Eighty English and seven Chinese articles were retrieved through PUBMED, OVID, CNKI, Weipu and Wanfang. The structure and content of these literatures were classified and analyzed.Results In five studies predicting the preoperative stages of colorectal cancer based on CT radiomics, the area under curve (AUC) ranged from 0.736 to 0.817; in two studies predicting the preoperative stages of colorectal cancer based on MRI radiomics, the AUC were 0.87 and 0.827 respectively. In two studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on CT, the AUC were 0.79 and 0.72 respectively; in four studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on MRI, the AUC ranged from 0.84 to 0.979. In one study evaluating the sensitivity of neoadjuvant chemotherapy based on MRI radiomics, the AUC was 0.79. In one study predicting the postoperative survival rate based on MRI radiomics, the AUC value of the final model was 0.827. In one study, the accuracy of the model based on PET/CT radiomics in 4-year disease-free survival (DSS), progression-free survival (DFS) and overall survival (OS) were 0.87, 0.79 and 0.79 respectively.Conclusion At present, radiomics has a valuable impact on preoperative staging, neoadjuvant therapy evaluation, and survival analysis of colorectal cancer.

Citation： LI Qian, ZHOU Xinyuan, WANG Xiaodong, LI Li. Advances on clinical application of radiomics and colorectal cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2019, 26(10): 1253-1258. doi: 10.7507/1007-9424.201903009 Copy

1.	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.
2.	Huang X, Cheng Z, Huang Y, et al. CT-based radiomics signature to discriminate high-grade from low-grade colorectal adenocarcinoma. Acad Radiol, 2018, 25(10): 1285-1297.
3.	Bibault JE, Giraud P, Housset M, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 12611.
4.	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.
5.	史张, 刘崎. 影像组学技术方法的研究及挑战. 放射学实践, 2018, 33(6): 633-636.
6.	Peeken JC, Nüsslin F, Combs SE. "Radio-oncomics": The potential of radiomics in radiation oncology. Strahlenther Onkol, 2017, 193(10): 767-779.
7.	Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med, 2017, 38: 122-139.
8.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
9.	Herz C, Fillion-Robin JC, Onken M, et al. dcmqi: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer Res, 2017, 77(21): e87-e90.
10.	Haak D, Page CE, Deserno TM. A survey of DICOM viewer software to integrate clinical research and medical imaging. Digit Imaging, 2016, 29(2): 206-215.
11.	Hu P, Wang J, Zhong H, et al. Reproducibility with repeat CT in radiomics study for rectal cancer. Oncotarget, 2016, 7(44): 71440-71446.
12.	Glimelus BL. The role of preoperative and postoperatival radiotherapy in rectal cancer. Clin Colorectal Cancer, 2002, 2(2): 82-92.
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.	Kim CH, Yeom SS, Lee SY, et al. Prognostic impact of perineural invasion in rectal cancer after neoadjuvant chemoradiotherapy. World J Surg, 2019, 43(1): 260-272.
15.	Chablani P, Nguyen P, Pan X, et al. Perineural invasion predicts for distant metastasis in locally advanced rectal cancer treated with neoadjuvant chemoradiation and surgery. Am J Clin Oncol, 2017, 40(6): 561-568.
16.	Huang Y, He L, Dong D, et al. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model. Chin J Cancer Res, 2018, 30(1): 40-50.
17.	Lee SH, Lee JL, Kim CW, et al. Oncologic significance of para-aortic lymph node and inferior mesenteric lymph node metastasis in sigmoid and rectal adenocarcinoma. Eur J Surg Oncol, 2017, 43(11): 2076-2083.
18.	Ishihara S, Kawai K, Tanaka T, et al. Oncological outcomes of lateral pelvic lymph node metastasis in rectal cancer treated with preoperative chemoradiotherapy. Dis Colon Rectum, 2017, 60(5): 469-476.
19.	Nakai N, Yamaguchi T, Kinugasa Y, et al. Long-term outcomes after resection of para-aortic lymph node metastasis from left-sided colon and rectal cancer. Int J Colorectal Dis, 2017, 32(7): 999-1007.
20.	Chang GJ, Kaiser AM, Mills S, et al. Standards Practice Task Force of the American society of colon and rectal surgeons. practice parameters for the management of colon cancer. Dis Colon Rectum, 2012, 55(8): 831-843.
21.	Manfredi S, Lepage C, Hatem C, et al. Epidemiology and management of liver metastases from colorectal cancer. Ann Surg, 2006, 244(2): 254-259.
22.	Liang M, Cai Z, Zhang H, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad Radiol, 2019: pii: S1076-6332(19)30012-1.
23.	Allen PJ, Kemeny N, Jarnagin W, et al. Importance of response to neoadjuvant chemotherapy in patients undergoing resection of synchronous colorectal liver metastases. J Gastrointest Surg, 2003, 7(1): 109-117.
24.	Viganò L. Treatment strategy for colorectal cancer with resectable synchronous liver metastases: Is any evidence-based strategy possible? World J Hepatol, 2012 Aug 27;4(8): 237-41.
25.	Liu H, Zhang C, Wang L, et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol, 2018 Nov 9. [Epub ahead of print].
26.	Gao XH, Yu GY, Gong HF, et al. Differences of protein expression profiles, KRAS and BRAF mutation, and prognosis in right-sided colon, left-sided colon and rectal cancer. Sci Rep, 2017, 7(1): 7882.
27.	Sideris M, Moorhead J, Diaz-Cano S, et al. KRAS mutant status may be associated with distant recurrence in early-stage rectal cancer. Anticancer Res, 2017, 37(3): 1349-1357.
28.	Krajnović M, Marković B, Knežević-Ušaj S, et al. Locally advanced rectal cancers with simultaneous occurrence of KRAS mutation and high VEGF expression show invasive characteristics. Pathol Res Pract, 2016, 212(7): 598-603.
29.	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.
30.	Chen SW, Shen WC, Chen WT, et al. Metabolic imaging phenotype using radiomics of [¹⁸F] FDG PET/CT associated with genetic alterations of colorectal cancer. Mol Imaging Biol, 2019, 21(1): 183-190.
31.	Hupkens BJP, Martens MH, Stoot JH, et al. Quality of Life in Rectal Cancer Patients After Chemoradiation: Watch-and-Wait Policy Versus Standard Resection-A Matched-Controlled Study. Dis Colon Rectum, 2017, 60(10): 1032-1040.
32.	Li J, Liu H, Yin J, et al. Wait-and-see or radical surgery for rectal cancer patients with a clinicalcomplete response after neoadjuvant chemoradiotherapy: a cohort study. Oncotarget, 2015, 6(39): 42354-42361.
33.	曹伟, 荣耀, 刘洪. CT 与 MRI 对直肠癌术前诊断与分期的临床价值对比. 实用癌症杂志, 2016, 31(11): 1851-1853.
34.	Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res, 2017, 23(23): 7253-7262.
35.	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.
36.	Cui Y, Yang X, Shi Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol, 2019, 29(3): 1211-1220.
37.	Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with ¹⁸F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging, 2019 Jan 13. [Epub ahead of print].
38.	Bibault JE, Giraud P, Housset M, et al. Author correction: deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 16914.
39.	Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016, 22(21): 5256-5264.
40.	Meng Y, Zhang Y, Dong D, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging, 2018 Feb 13. [Epub ahead of print].
41.	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.
42.	Bahcall O. Precision medicine. Nature, 2015, 526(7573): 335.
43.	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
44.	汪晓东, 李希, 何欣林, 等. 数据库研究第一部分: 区域性医疗中心的结直肠癌与人群特征. 中国普外基础与临床杂志, 2019, 26(2): 212-220.
45.	汪晓东, 李立. 真实场景与大数据下的整体微创理念, 大幅提高结直肠癌远期生存率. 中国普外基础与临床杂志, 2019, 26(1): 92-95.

1. 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.
2. Huang X, Cheng Z, Huang Y, et al. CT-based radiomics signature to discriminate high-grade from low-grade colorectal adenocarcinoma. Acad Radiol, 2018, 25(10): 1285-1297.
3. Bibault JE, Giraud P, Housset M, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 12611.
4. 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.
5. 史张, 刘崎. 影像组学技术方法的研究及挑战. 放射学实践, 2018, 33(6): 633-636.
6. Peeken JC, Nüsslin F, Combs SE. "Radio-oncomics": The potential of radiomics in radiation oncology. Strahlenther Onkol, 2017, 193(10): 767-779.
7. Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med, 2017, 38: 122-139.
8. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
9. Herz C, Fillion-Robin JC, Onken M, et al. dcmqi: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer Res, 2017, 77(21): e87-e90.
10. Haak D, Page CE, Deserno TM. A survey of DICOM viewer software to integrate clinical research and medical imaging. Digit Imaging, 2016, 29(2): 206-215.
11. Hu P, Wang J, Zhong H, et al. Reproducibility with repeat CT in radiomics study for rectal cancer. Oncotarget, 2016, 7(44): 71440-71446.
12. Glimelus BL. The role of preoperative and postoperatival radiotherapy in rectal cancer. Clin Colorectal Cancer, 2002, 2(2): 82-92.
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. Kim CH, Yeom SS, Lee SY, et al. Prognostic impact of perineural invasion in rectal cancer after neoadjuvant chemoradiotherapy. World J Surg, 2019, 43(1): 260-272.
15. Chablani P, Nguyen P, Pan X, et al. Perineural invasion predicts for distant metastasis in locally advanced rectal cancer treated with neoadjuvant chemoradiation and surgery. Am J Clin Oncol, 2017, 40(6): 561-568.
16. Huang Y, He L, Dong D, et al. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model. Chin J Cancer Res, 2018, 30(1): 40-50.
17. Lee SH, Lee JL, Kim CW, et al. Oncologic significance of para-aortic lymph node and inferior mesenteric lymph node metastasis in sigmoid and rectal adenocarcinoma. Eur J Surg Oncol, 2017, 43(11): 2076-2083.
18. Ishihara S, Kawai K, Tanaka T, et al. Oncological outcomes of lateral pelvic lymph node metastasis in rectal cancer treated with preoperative chemoradiotherapy. Dis Colon Rectum, 2017, 60(5): 469-476.
19. Nakai N, Yamaguchi T, Kinugasa Y, et al. Long-term outcomes after resection of para-aortic lymph node metastasis from left-sided colon and rectal cancer. Int J Colorectal Dis, 2017, 32(7): 999-1007.
20. Chang GJ, Kaiser AM, Mills S, et al. Standards Practice Task Force of the American society of colon and rectal surgeons. practice parameters for the management of colon cancer. Dis Colon Rectum, 2012, 55(8): 831-843.
21. Manfredi S, Lepage C, Hatem C, et al. Epidemiology and management of liver metastases from colorectal cancer. Ann Surg, 2006, 244(2): 254-259.
22. Liang M, Cai Z, Zhang H, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad Radiol, 2019: pii: S1076-6332(19)30012-1.
23. Allen PJ, Kemeny N, Jarnagin W, et al. Importance of response to neoadjuvant chemotherapy in patients undergoing resection of synchronous colorectal liver metastases. J Gastrointest Surg, 2003, 7(1): 109-117.
24. Viganò L. Treatment strategy for colorectal cancer with resectable synchronous liver metastases: Is any evidence-based strategy possible? World J Hepatol, 2012 Aug 27;4(8): 237-41.
25. Liu H, Zhang C, Wang L, et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol, 2018 Nov 9. [Epub ahead of print].
26. Gao XH, Yu GY, Gong HF, et al. Differences of protein expression profiles, KRAS and BRAF mutation, and prognosis in right-sided colon, left-sided colon and rectal cancer. Sci Rep, 2017, 7(1): 7882.
27. Sideris M, Moorhead J, Diaz-Cano S, et al. KRAS mutant status may be associated with distant recurrence in early-stage rectal cancer. Anticancer Res, 2017, 37(3): 1349-1357.
28. Krajnović M, Marković B, Knežević-Ušaj S, et al. Locally advanced rectal cancers with simultaneous occurrence of KRAS mutation and high VEGF expression show invasive characteristics. Pathol Res Pract, 2016, 212(7): 598-603.
29. 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.
30. Chen SW, Shen WC, Chen WT, et al. Metabolic imaging phenotype using radiomics of [¹⁸F] FDG PET/CT associated with genetic alterations of colorectal cancer. Mol Imaging Biol, 2019, 21(1): 183-190.
31. Hupkens BJP, Martens MH, Stoot JH, et al. Quality of Life in Rectal Cancer Patients After Chemoradiation: Watch-and-Wait Policy Versus Standard Resection-A Matched-Controlled Study. Dis Colon Rectum, 2017, 60(10): 1032-1040.
32. Li J, Liu H, Yin J, et al. Wait-and-see or radical surgery for rectal cancer patients with a clinicalcomplete response after neoadjuvant chemoradiotherapy: a cohort study. Oncotarget, 2015, 6(39): 42354-42361.
33. 曹伟, 荣耀, 刘洪. CT 与 MRI 对直肠癌术前诊断与分期的临床价值对比. 实用癌症杂志, 2016, 31(11): 1851-1853.
34. Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res, 2017, 23(23): 7253-7262.
35. 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.
36. Cui Y, Yang X, Shi Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol, 2019, 29(3): 1211-1220.
37. Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with ¹⁸F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging, 2019 Jan 13. [Epub ahead of print].
38. Bibault JE, Giraud P, Housset M, et al. Author correction: deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 16914.
39. Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016, 22(21): 5256-5264.
40. Meng Y, Zhang Y, Dong D, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging, 2018 Feb 13. [Epub ahead of print].
41. 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.
42. Bahcall O. Precision medicine. Nature, 2015, 526(7573): 335.
43. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
44. 汪晓东, 李希, 何欣林, 等. 数据库研究第一部分: 区域性医疗中心的结直肠癌与人群特征. 中国普外基础与临床杂志, 2019, 26(2): 212-220.
45. 汪晓东, 李立. 真实场景与大数据下的整体微创理念, 大幅提高结直肠癌远期生存率. 中国普外基础与临床杂志, 2019, 26(1): 92-95.

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Advances on clinical application of radiomics and colorectal cancer

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