Share：
Share this content in WeChat
X
[Chinese][PDF]914123
• Review •
Advances in application of radiomics in colorectal cancer
ZHANG Keke  XU Yongsheng  GAO Ya  KANG Yuchen  LEI Junqiang 

Cite this article as: Zhang KK, Xu YS, Gao Y,et al. Advances in application of radiomics in colorectal cancer[J]. Chin J Magn Reson Imaging, 2021, 12(3):112-115. DOI:10.12015/issn.1674-8034.2021.03.028.


[Abstract] In recent years, with the continuous development of image processing and computer technology, the use of imaging histological analysis methods for accurate preoperative evaluation of patients with colorectal cancer, efficacy prediction, the development of accurate individual treatment is a new research hotspot in this field. In this article, we summarize the research results of imaging techniques in the different stages of colorectal cancer diagnosis and treatment, such as preoperative staging, curative effect evaluation and prognosis evaluation.
[Keywords] radiomics；colorectal cancer；magnetic resonance imaging；deep learning

ZHANG Keke1, 2, 3, 4   XU Yongsheng2, 3, 4   GAO Ya1, 2, 3, 4   KANG Yuchen1   LEI Junqiang2, 3, 4*  

1 First Clinical Medical College, Lanzhou University, Lanzhou 730000, China

2 Department of Radiology, First Hospital of Lanzhou University, Lanzhou 730000, China

3 Gansu Intelligent Imaging Medical Engineering Research Center, Lanzhou 730000, China

4 Accurate Imaging Collaborative Innovation Gansu International Science and Technology Cooperation Base, Lanzhou 730000, China

Lei JQ, E-mail: leijq1990@163.com

Conflicts of interest   None.

Received  2020-09-03
Accepted  2020-11-28
DOI: 10.12015/issn.1674-8034.2021.03.028
Cite this article as: Zhang KK, Xu YS, Gao Y,et al. Advances in application of radiomics in colorectal cancer[J]. Chin J Magn Reson Imaging, 2021, 12(3):112-115. DOI:10.12015/issn.1674-8034.2021.03.028.

1
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132. DOI: 10.3322/caac.21338
2
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. DOI: 10.1016/j.ejca.2011.11.036
3
Lee SJ, Zea R, Kim DH, 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. DOI: 10.1007/s00330-017-5111-6
4
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. DOI: 10.1007/s00259-017-3855-5
5
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. DOI: 10.1148/radiol.2018172300
6
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169
7
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017, 19: 221-248. DOI: 10.1146/annurev-bioeng-071516-044442
8
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. DOI: 10.1200/JCO.2011.40.1836
9
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. DOI: 10.1016/S1470-2045(10)70172-8
10
Dossa F, Chesney TR, Acuna SA, et al. A watch-and-wait approach for locally advanced rectal cancer after a clinical complete response following neoadjuvant chemoradiation: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol, 2017, 2(7): 501-513. DOI: 10.1016/S2468-1253(17)30074-2
11
Aker M, Ganeshan B, Afaq A, et al. Magnetic resonance texture analysis in identifying complete pathological response to neoadjuvant treatment in locally advanced rectal cancer. Dis Colon Rectum, 2019, 62(2): 163-170. DOI: 10.1097/DCR.0000000000001224
12
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. DOI: 10.1097/RLI.0000000000000116
13
Meng Y, Zhang C, Zou S, et al. MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget, 2018, 9(15): 11999-12008. DOI: 10.18632/oncotarget.23813
14
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): 1728-12735. DOI: 10.1007/s00261-016-0733-8
15
Cusumano D, Dinapoli N, Boldrini L, et al. Fractal-based radiomic approach to predict complete pathological response after chemo- radiotherapy in rectal cancer. Radiol Med, 2018, 123(4): 286-295. DOI: 10.1007/s11547-017-0838-3
16
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. DOI: 10.1007/s00330-018-5683-9
17
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. DOI: 10.1158/1078-0432.CCR-17-1038
18
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. DOI: 10.1158/1078-0432.CCR-15-2997
19
Ferrari R, Mancini-Terracciano C, Voena C, et al. MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. Eur J Radiol, 2019, 118: 1-9. DOI: 10.1016/j.ejrad.2019.06.013
20
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. DOI: 10.1200/JCO.2009.25.8541
21
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. DOI: 10.1002/jmri.25460
22
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. DOI: 10.18632/oncotarget.8919
23
Lu Z, Wang L, Xia K, et al. Prediction of clinical pathologic prognostic factors for rectal adenocarcinoma: volumetric texture analysis based on apparent diffusion coefficient maps. J Med Syst, 2019, 43(12): 331. DOI: 10.1007/s10916-019-1464-5
24
Ma X, Shen F, Jia Y, et al. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging, 2019, 19(1): 86. DOI: 10.1186/s12880-019-0392-7
25
Schmoll HJ, Van Cutsem E, Stein A, et al. ESMO Consensus Guidelines for management of patients with colon and rectal cancer. a personalized approach to clinical decision making. Ann Oncol, 2012, 23(10): 2479-2516. DOI: 10.1093/annonc/mds236
26
Al-Sukhni E, Milot L, Fruitman M, et al. Diagnostic accuracy of MRI for assessment of T category, lymph node metastases, and circumferential resection margin involvement in patients with rectal cancer: a systematic review and meta-analysis. Ann Surg Oncol, 2012, 19(7): 2212-2223. DOI: 10.1245/s10434-011-2210-5
27
Chen LD, Liang JY, Wu H, et al. Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics. Life Sci, 2018, 208: 55-63. DOI: 10.1016/j.lfs.2018.07.007
28
Engstrand J, Nilsson H, Strömberg C, et al. Colorectal cancer liver metastases: a population-based study on incidence, management and survival. BMC Cancer, 2018, 18(1): 78. DOI: 10.1186/s12885-017-3925-x
29
Beckers RC, Lambregts DM, Schnerr RS, 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. DOI: 10.1016/j.ejrad.2017.04.019
30
Kruskal JB, Thomas P, Kane RA, et al. Hepatic perfusion changes in mice livers with developing colorectal cancer metastases. Radiology, 2004, 231(2): 482-490. DOI: 10.1148/radiol.2312030160
31
Li M, Li X, Guo Y, et al. Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases. Quant Imaging Med Surg, 2020, 10(2): 397-414. DOI: 10.21037/qims.2019.12.16
32
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, 26(11):1495-1504. DOI: 10.1016/j.acra.2018.12.019
33
Van Helden EJ, Vacher YJ, Van Wieringen WN, et al. Radiomics analysis of pre-treatment [(18)F]FDG PET/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment. Eur J Nucl Med Mol Imaging, 2018, 45(13): 2307-2317. DOI: 10.1007/s00259-018-4100-6. DOI:
34
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, 48(3): 605-614. DOI: 10.1002/jmri.25968
35
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. DOI: 10.1111/codi.13496
36
Shin YR, Kim KA, Im S, et al. Prediction of KRAS mutation in rectal cancer using MRI. anticancer res, 2016, 36(9): 4799-4804. DOI: 10.21873/anticanres.11039
37
Xu Y, Xu Q, Ma Y, et al. Characterizing MRI features of rectal cancers with different KRAS status. BMC Cancer, 2019, 19(1): 1111. DOI: 10.1186/s12885-019-6341-6

PREV The application of radiomics in intrahepatic cholangiocarcinoma
NEXT Advances in risk factors and imaging prediction for pathological fractures of spinal metastases
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn