The value of radiomics for prediction of treatment response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LI Zhengyan ¹ , WANG Xiaodong ² , HUANG Zixing ¹ ,  SONG Bin ¹

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

Corresponding author：

SONG Bin, Email: songlab_radiology@163.com

Keywords：

locally advanced rectal cancer; neoadjuvant chemoradiotherapy; radiomics; review

DOI：

10.7507/1007-9424.202006028

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To make a comprehensive review of the value of radiomics for prediction of therapeutic responses to neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC).Method Relevant literatures about the therapeutic response evaluation of LARC by using radiomics were collected to make an review.Result Radiomics had good predictive value in terms of complete pathologic response (pCR) and treatment effectiveness of NCRT in patients with LARC.Conclusion Radiomics, a new imaging diagnostic technique, plays an important role in the prediction of the efficacy of NCRT in LARC.

Citation： LI Zhengyan, WANG Xiaodong, HUANG Zixing, SONG Bin. The value of radiomics for prediction of treatment response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2020, 27(8): 1016-1022. doi: 10.7507/1007-9424.202006028 Copy

1.	Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2.	Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
3.	Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med, 2004, 351(17): 1731-1740.
4.	Benson AB, Venook AP, Al-Hawary MM, et al. Rectal Cancer, Version 2.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2018, 16(7): 874-901.
5.	Bosset JF, Collette L, Calais G, et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med, 2006, 355(11): 1114-1123.
6.	Rödel C, Liersch T, Becker H, et al. Preoperative chemo-radiotherapy and postoperative chemotherapy with fluorouracil and oxaliplatin versus fluorouracil alone in locally advanced rectal cancer: initial results of the German CAO/ARO/AIO-04 randomised phase 3 trial. Lancet Oncol, 2012, 13(7): 679-687.
7.	Yang C, Jiang ZK, Liu LH, et al. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy. Int J Colorectal Dis, 2020, 35(1): 101-107.
8.	Zhou X, Yi Y, Liu Z, et al. Radiomics-based pretherapeutic prediction of non-response to neoadjuvant therapy in locally advanced rectal cancer. Ann Surg Oncol, 2019, 26(6): 1676-1684.
9.	Park IJ, You YN, Agarwal A, et al. Neoadjuvant treatment response as an early response indicator for patients with rectal cancer. J Clin Oncol, 2012, 30(15): 1770-1776.
10.	Janssen MH, Ollers MC, Riedl RG, et al. Accurate prediction of pathological rectal tumor response after two weeks of preoperative radiochemotherapy using (18)F-fluorodeoxyglucose-positron emission tomography-computed tomography imaging. Int J Radiat Oncol Biol Phys, 2010, 77(2): 392-399.
11.	Engelen SM, Beets-Tan RG, Lahaye MJ, et al. MRI after chemoradiotherapy of rectal cancer: a useful tool to select patients for local excision. Dis Colon Rectum, 2010, 53(7): 979-986.
12.	Intven M, Reerink O, Philippens ME. Dynamic contrast enhanced MR imaging for rectal cancer response assessment after neo-adjuvant chemoradiation. J Magn Reson Imaging, 2015, 41(6): 1646-1653.
13.	De Felice F, Magnante AL, Musio D, et al. Diffusion-weighted magnetic resonance imaging in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy. Eur J Surg Oncol, 2017, 43(7): 1324-1329.
14.	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.
15.	Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology, 2019, 290(3): 783-792.
16.	Sidhu HS, Benigno S, Ganeshan B, et al. “Textural analysis of multiparametric MRI detects transition zone prostate cancer”. Eur Radiol, 2017, 27(6): 2348-2358.
17.	Ueno Y, Forghani B, Forghani R, et al. Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification-a preliminary analysis. Radiology, 2017, 284(3): 748-757.
18.	Lakhman Y, Veeraraghavan H, Chaim J, et al. Differentiation of uterine leiomyosarcoma from atypical leiomyoma: diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis. Eur Radiol, 2017, 27(7): 2903-2915.
19.	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.
20.	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.
21.	Chee CG, Kim YH, Lee KH, et al. CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a potential imaging biomarker for treatment response and prognosis. PLoS One, 2017, 12(8): e0182883.
22.	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.
23.	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.
24.	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.
25.	魏炜, 刘振宇, 王硕, 等. 影像组学技术研究进展及其在结直肠癌中的临床应用. 中国生物医学工程学报, 2018, 37(5): 513-520.
26.	Larue RTHM, Defraene G, De Ruysscher D, et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol, 2017, 90(1070): 20160665.
27.	Polan DF, Brady SL, Kaufman RA. Tissue segmentation of computed tomography images using a random forest algorithm: a feasibility study. Phys Med Biol, 2016, 61(17): 6553-6569.
28.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
29.	Ma Z, Tavares JM, Jorge RN, et al. A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Engin, 2010, 13(2): 235-246.
30.	Song J, Yang C, Fan L, et al. Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE Trans Med Imaging, 2016, 35(1): 337-353.
31.	Despotovic I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med, 2015, 2015: 450341.
32.	Zhang J, Wang Y, Shi X. An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node’s shape assessment. Comput Med Imaging Graph, 2009, 33(8): 602-607.
33.	Lu K, Higgins WE. Segmentation of the central-chest lymph nodes in 3D MDCT images. Comput Biol Med, 2011, 41(9): 780-789.
34.	Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
35.	Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep, 2015, 5: 13087.
36.	Nobili F, Salmaso D, Morbelli S, et al. Principal component analysis of FDG PET in amnestic MCI. Eur J Nucl Med Mol Imaging, 2008, 35(12): 2191-2202.
37.	张利文, 方梦捷, 臧亚丽, 等. 影像组学的发展与应用. 中华放射学杂志, 2017, 51(1): 75-77.
38.	Meng X, Xia W, Xie P, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol, 2019, 29(6): 3200-3209.
39.	Peng YT, Zhou CY, Lin P, et al. Preoperative ultrasound radiomics signatures for noninvasive evaluation of biological characteristics of intrahepatic cholangiocarcinoma. Acad Radiol, 2020, 27(6): 785-797.
40.	Soike MH, McTyre ER, Shah N, et al. Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns? Neuroradiology, 2018, 60(10): 1043-1051.
41.	吴佩琪, 刘再毅, 何兰, 等. 影像组学与大数据结合的研究现状. 中华放射学杂志, 2017, 51(7): 554-558.
42.	Vickers AJ. Prediction models: revolutionary in principle, but do they do more good than harm? J Clin Oncol, 2011, 29(22): 2951-2952.
43.	Trakarnsanga A, Gönen M, Shia J, et al. Comparison of tumor regression grade systems for locally advanced rectal cancer after multimodality treatment. J Natl Cancer Inst, 2014, 106(10): dju248.
44.	Liang P, Nakada I, Hong JW, et al. Prognostic significance of immunohistochemically detected blood and lymphatic vessel invasion in colorectal carcinoma: its impact on prognosis. Ann Surg Oncol, 2007, 14(2): 470-477.
45.	Suzuki T, Sadahiro S, Tanaka A, et al. Relationship between histologic response and the degree of tumor shrinkage after chemoradiotherapy in patients with locally advanced rectal cancer. J Surg Oncol, 2014, 109(7): 659-664.
46.	Siddiqui MRS, Simillis C, Hunter C, et al. A meta-analysis comparing the risk of metastases in patients with rectal cancer and MRI-detected extramural vascular invasion (mrEMVI) vs mrEMVI-negative cases. Br J Cancer, 2017, 116(12): 1513-1519.
47.	Sohn B, Lim JS, Kim H, et al. MRI-detected extramural vascular invasion is an independent prognostic factor for synchronous metastasis in patients with rectal cancer. Eur Radiol, 2015, 25(5): 1347-1355.
48.	Sanghera P, Wong DW, McConkey CC, et al. Chemoradiotherapy for rectal cancer: an updated analysis of factors affecting pathological response. Clin Oncol (R Coll Radiol), 2008, 20(2): 176-183.
49.	Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol, 2010, 11(9): 835-844.
50.	Maas M, Beets-Tan RG, Lambregts DM, et al. Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer. J Clin Oncol, 2011, 29(35): 4633-4640.
51.	Renehan AG, Malcomson L, Emsley R, et al. Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer (The OnCoRe Project): a propensity-score matched cohort analysis. Lancet Oncol, 2016, 17(2): 174-183.
52.	Shu Z, Fang S, Ye Q, et al. Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdom Radiol (NY), 2019, 44(11): 3775-3784.
53.	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.
54.	Jakob C, Liersch T, Meyer W, et al. Predictive value of Ki67 and p53 in locally advanced rectal cancer: correlation with thymidylate synthase and histopathological tumor regression after neoadjuvant 5-FU-based chemoradiotherapy. World J Gastroenterol, 2008, 14(7): 1060-1066.
55.	Imaizumi K, Suzuki T, Kojima M, et al. Ki67 expression and localization of T cells after neoadjuvant therapies as reliable predictive markers in rectal cancer. Cancer Sci, 2020, 111(1): 23-35.
56.	Meng X, Huang Z, Wang R, et al. The prognostic role of EZH2 expression in rectal cancer patients treated with neoadjuvant chemoradiotherapy. Radiat Oncol, 2014, 9: 188.
57.	Bertolini F, Bengala C, Losi L, et al. Prognostic and predictive value of baseline and posttreatment molecular marker expression in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy. Int J Radiat Oncol Biol Phys, 2007, 68(5): 1455-1461.
58.	Aschele C, Cionini L, Lonardi S, et al. Primary tumor response to preoperative chemoradiation with or without oxaliplatin in locally advanced rectal cancer: pathologic results of the STAR-01 randomized phase Ⅲ trial. J Clin Oncol, 2011, 29(20): 2773-2780.
59.	Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with¹⁸F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging, 2019, 46(4): 878-888.
60.	Li ZY, Wang XD, Li M, et al. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J Gastroenterol, 2020, 26(19): 2388-2402.
61.	Yi X, Pei Q, Zhang Y, et al. MRI-based radiomics predicts tumor response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front Oncol, 2019, 9: 552.
62.	Zhu H, Zhang X, Li X, et al. Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy. Chin J Cancer Res, 2019, 31(6): 984-992.
63.	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, [published online ahead of print].
64.	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.
65.	He L, Huang Y, Ma Z, et al. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep, 2016, 6: 34921.
66.	Ganeshan B, Miles KA, Young RC, et al. In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol, 2007, 14(9): 1058-1068.
67.	Sauer R, Liersch T, Merkel S, et al. Preoperative versus postoperative 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.
68.	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.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
3. Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med, 2004, 351(17): 1731-1740.
4. Benson AB, Venook AP, Al-Hawary MM, et al. Rectal Cancer, Version 2.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2018, 16(7): 874-901.
5. Bosset JF, Collette L, Calais G, et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med, 2006, 355(11): 1114-1123.
6. Rödel C, Liersch T, Becker H, et al. Preoperative chemo-radiotherapy and postoperative chemotherapy with fluorouracil and oxaliplatin versus fluorouracil alone in locally advanced rectal cancer: initial results of the German CAO/ARO/AIO-04 randomised phase 3 trial. Lancet Oncol, 2012, 13(7): 679-687.
7. Yang C, Jiang ZK, Liu LH, et al. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy. Int J Colorectal Dis, 2020, 35(1): 101-107.
8. Zhou X, Yi Y, Liu Z, et al. Radiomics-based pretherapeutic prediction of non-response to neoadjuvant therapy in locally advanced rectal cancer. Ann Surg Oncol, 2019, 26(6): 1676-1684.
9. Park IJ, You YN, Agarwal A, et al. Neoadjuvant treatment response as an early response indicator for patients with rectal cancer. J Clin Oncol, 2012, 30(15): 1770-1776.
10. Janssen MH, Ollers MC, Riedl RG, et al. Accurate prediction of pathological rectal tumor response after two weeks of preoperative radiochemotherapy using (18)F-fluorodeoxyglucose-positron emission tomography-computed tomography imaging. Int J Radiat Oncol Biol Phys, 2010, 77(2): 392-399.
11. Engelen SM, Beets-Tan RG, Lahaye MJ, et al. MRI after chemoradiotherapy of rectal cancer: a useful tool to select patients for local excision. Dis Colon Rectum, 2010, 53(7): 979-986.
12. Intven M, Reerink O, Philippens ME. Dynamic contrast enhanced MR imaging for rectal cancer response assessment after neo-adjuvant chemoradiation. J Magn Reson Imaging, 2015, 41(6): 1646-1653.
13. De Felice F, Magnante AL, Musio D, et al. Diffusion-weighted magnetic resonance imaging in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy. Eur J Surg Oncol, 2017, 43(7): 1324-1329.
14. 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.
15. Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology, 2019, 290(3): 783-792.
16. Sidhu HS, Benigno S, Ganeshan B, et al. “Textural analysis of multiparametric MRI detects transition zone prostate cancer”. Eur Radiol, 2017, 27(6): 2348-2358.
17. Ueno Y, Forghani B, Forghani R, et al. Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification-a preliminary analysis. Radiology, 2017, 284(3): 748-757.
18. Lakhman Y, Veeraraghavan H, Chaim J, et al. Differentiation of uterine leiomyosarcoma from atypical leiomyoma: diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis. Eur Radiol, 2017, 27(7): 2903-2915.
19. 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.
20. 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.
21. Chee CG, Kim YH, Lee KH, et al. CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a potential imaging biomarker for treatment response and prognosis. PLoS One, 2017, 12(8): e0182883.
22. 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.
23. 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.
24. 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.
25. 魏炜, 刘振宇, 王硕, 等. 影像组学技术研究进展及其在结直肠癌中的临床应用. 中国生物医学工程学报, 2018, 37(5): 513-520.
26. Larue RTHM, Defraene G, De Ruysscher D, et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol, 2017, 90(1070): 20160665.
27. Polan DF, Brady SL, Kaufman RA. Tissue segmentation of computed tomography images using a random forest algorithm: a feasibility study. Phys Med Biol, 2016, 61(17): 6553-6569.
28. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
29. Ma Z, Tavares JM, Jorge RN, et al. A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Engin, 2010, 13(2): 235-246.
30. Song J, Yang C, Fan L, et al. Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE Trans Med Imaging, 2016, 35(1): 337-353.
31. Despotovic I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med, 2015, 2015: 450341.
32. Zhang J, Wang Y, Shi X. An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node’s shape assessment. Comput Med Imaging Graph, 2009, 33(8): 602-607.
33. Lu K, Higgins WE. Segmentation of the central-chest lymph nodes in 3D MDCT images. Comput Biol Med, 2011, 41(9): 780-789.
34. Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
35. Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep, 2015, 5: 13087.
36. Nobili F, Salmaso D, Morbelli S, et al. Principal component analysis of FDG PET in amnestic MCI. Eur J Nucl Med Mol Imaging, 2008, 35(12): 2191-2202.
37. 张利文, 方梦捷, 臧亚丽, 等. 影像组学的发展与应用. 中华放射学杂志, 2017, 51(1): 75-77.
38. Meng X, Xia W, Xie P, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol, 2019, 29(6): 3200-3209.
39. Peng YT, Zhou CY, Lin P, et al. Preoperative ultrasound radiomics signatures for noninvasive evaluation of biological characteristics of intrahepatic cholangiocarcinoma. Acad Radiol, 2020, 27(6): 785-797.
40. Soike MH, McTyre ER, Shah N, et al. Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns? Neuroradiology, 2018, 60(10): 1043-1051.
41. 吴佩琪, 刘再毅, 何兰, 等. 影像组学与大数据结合的研究现状. 中华放射学杂志, 2017, 51(7): 554-558.
42. Vickers AJ. Prediction models: revolutionary in principle, but do they do more good than harm? J Clin Oncol, 2011, 29(22): 2951-2952.
43. Trakarnsanga A, Gönen M, Shia J, et al. Comparison of tumor regression grade systems for locally advanced rectal cancer after multimodality treatment. J Natl Cancer Inst, 2014, 106(10): dju248.
44. Liang P, Nakada I, Hong JW, et al. Prognostic significance of immunohistochemically detected blood and lymphatic vessel invasion in colorectal carcinoma: its impact on prognosis. Ann Surg Oncol, 2007, 14(2): 470-477.
45. Suzuki T, Sadahiro S, Tanaka A, et al. Relationship between histologic response and the degree of tumor shrinkage after chemoradiotherapy in patients with locally advanced rectal cancer. J Surg Oncol, 2014, 109(7): 659-664.
46. Siddiqui MRS, Simillis C, Hunter C, et al. A meta-analysis comparing the risk of metastases in patients with rectal cancer and MRI-detected extramural vascular invasion (mrEMVI) vs mrEMVI-negative cases. Br J Cancer, 2017, 116(12): 1513-1519.
47. Sohn B, Lim JS, Kim H, et al. MRI-detected extramural vascular invasion is an independent prognostic factor for synchronous metastasis in patients with rectal cancer. Eur Radiol, 2015, 25(5): 1347-1355.
48. Sanghera P, Wong DW, McConkey CC, et al. Chemoradiotherapy for rectal cancer: an updated analysis of factors affecting pathological response. Clin Oncol (R Coll Radiol), 2008, 20(2): 176-183.
49. Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol, 2010, 11(9): 835-844.
50. Maas M, Beets-Tan RG, Lambregts DM, et al. Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer. J Clin Oncol, 2011, 29(35): 4633-4640.
51. Renehan AG, Malcomson L, Emsley R, et al. Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer (The OnCoRe Project): a propensity-score matched cohort analysis. Lancet Oncol, 2016, 17(2): 174-183.
52. Shu Z, Fang S, Ye Q, et al. Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdom Radiol (NY), 2019, 44(11): 3775-3784.
53. 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.
54. Jakob C, Liersch T, Meyer W, et al. Predictive value of Ki67 and p53 in locally advanced rectal cancer: correlation with thymidylate synthase and histopathological tumor regression after neoadjuvant 5-FU-based chemoradiotherapy. World J Gastroenterol, 2008, 14(7): 1060-1066.
55. Imaizumi K, Suzuki T, Kojima M, et al. Ki67 expression and localization of T cells after neoadjuvant therapies as reliable predictive markers in rectal cancer. Cancer Sci, 2020, 111(1): 23-35.
56. Meng X, Huang Z, Wang R, et al. The prognostic role of EZH2 expression in rectal cancer patients treated with neoadjuvant chemoradiotherapy. Radiat Oncol, 2014, 9: 188.
57. Bertolini F, Bengala C, Losi L, et al. Prognostic and predictive value of baseline and posttreatment molecular marker expression in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy. Int J Radiat Oncol Biol Phys, 2007, 68(5): 1455-1461.
58. Aschele C, Cionini L, Lonardi S, et al. Primary tumor response to preoperative chemoradiation with or without oxaliplatin in locally advanced rectal cancer: pathologic results of the STAR-01 randomized phase Ⅲ trial. J Clin Oncol, 2011, 29(20): 2773-2780.
59. Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with¹⁸F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging, 2019, 46(4): 878-888.
60. Li ZY, Wang XD, Li M, et al. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J Gastroenterol, 2020, 26(19): 2388-2402.
61. Yi X, Pei Q, Zhang Y, et al. MRI-based radiomics predicts tumor response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front Oncol, 2019, 9: 552.
62. Zhu H, Zhang X, Li X, et al. Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy. Chin J Cancer Res, 2019, 31(6): 984-992.
63. 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, [published online ahead of print].
64. 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.
65. He L, Huang Y, Ma Z, et al. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep, 2016, 6: 34921.
66. Ganeshan B, Miles KA, Young RC, et al. In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol, 2007, 14(9): 1058-1068.
67. Sauer R, Liersch T, Merkel S, et al. Preoperative versus postoperative 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.
68. 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.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

The value of radiomics for prediction of treatment response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content