Share：
Share this content in WeChat
X
[Chinese][PDF]7134
• Review •
Research progress of colorectal cancer radiogenomics
JIA Lulu  CUI Yaqiong  HUANG Gang 

Cite this article as: Jia LL, Cui YQ, Huang G. Research progress of colorectal cancer radiogenomics[J]. Chin J Magn Reson Imaging, 2022, 13(12): 159-162. DOI:10.12015/issn.1674-8034.2022.12.030.


[Abstract] Colorectal cancer (CRC) is one of the malignant tumors with the highest morbidity and mortality in China. Genotyping has important guiding significance for personalized treatment and prognostic analysis of CRC patients. By extracting and analyzing a large number of image features and establishing the link between tumor genotype and imaging phenotype, radiomics can non-invasively predict tumor-related genotypes before surgery. At present, more and more research is devoted to analyzing the correlation between image characteristics and CRC genotypes, which provides more accurate information for the diagnosis and prediction of CRC genotypes. This review will summarize the clinical application, development prospects and current shortcomings of CRC radiogenomics, in order to facilitate clinicians to better understand CRC radiogenomics and apply it more widely in clinical work, and provide new diagnosis and treatment methods and ideas for clinical diagnosis and treatment.
[Keywords] colorectal cancer；radiomics；radiogenomics；molecular typing；Kirsten rats arcoma viral oncogene；microsatellite instability；magnetic resonance imaging

JIA Lulu1   CUI Yaqiong2   HUANG Gang2*  

1 The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, China

2 Department of Radiology, Gansu Provincial People's Hospital, Lanzhou 730000, China

Huang G, E-mail: keen0999@163.com

Conflicts of interest   None.

Received  2022-05-25
Accepted  2022-11-04
DOI: 10.12015/issn.1674-8034.2022.12.030
Cite this article as: Jia LL, Cui YQ, Huang G. Research progress of colorectal cancer radiogenomics[J]. Chin J Magn Reson Imaging, 2022, 13(12): 159-162. DOI:10.12015/issn.1674-8034.2022.12.030.

[1]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
Chen WQ, Zheng RS, Baade PD, et al. Cancer statistics in China, 2015[J]. CA A Cancer J Clin, 2016, 66(2): 115-132. DOI: 10.3322/caac.21338.
[3]
Zhou J, Ji Q, Li Q. Resistance to anti-EGFR therapies in metastatic colorectal cancer: underlying mechanisms and reversal strategies[J/OL]. J Exp Clin Cancer Res, 2021, 40(1): 328 [2022-05-08]. https://jeccr.biomedcentral.com/articles/10.1186/s13046-021-02130-2. DOI: 10.1186/s13046-021-02130-2.
[4]
Biller LH, Schrag D. Diagnosis and treatment of metastatic colorectal cancer: a review[J]. JAMA, 2021, 325(7): 669-685. DOI: 10.1001/jama.2021.0106.
[5]
Herreros-Villanueva M, Chen CC, Yuan SS, et al. KRAS mutations: analytical considerations[J]. Clin Chim Acta, 2014, 431: 211-220. DOI: 10.1016/j.cca.2014.01.049.
[6]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[7]
Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, et al. MRI of rectal cancer: tumor staging, imaging techniques, and management[J]. Radiographics, 2019, 39(2): 367-387. DOI: 10.1148/rg.2019180114.
[8]
Qi YN, Zhao TT, Han MY. The application of radiomics in predicting gene mutations in cancer[J]. Eur Radiol, 2022, 32(6): 4014-4024. DOI: 10.1007/s00330-021-08520-6.
[9]
Andreyev HJ, Norman AR, Cunningham D, et al. Kirsten ras mutations in patients with colorectal cancer: the multicenter "RASCAL" study[J]. J Natl Cancer Inst, 1998, 90(9): 675-684. DOI: 10.1093/jnci/90.9.675.
[10]
Pershad Y, Govindan S, Hara AK, et al. Using naïve bayesian analysis to determine imaging characteristics of KRAS mutations in metastatic colon cancer[J/OL]. Diagnostics (Basel), 2017, 7(3): 50 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/28869500. DOI: 10.3390/diagnostics7030050.
[11]
Song CY, Shen BQ, Dong Z, et al. Diameter of superior rectal vein - CT predictor of KRAS mutation in rectal carcinoma[J]. Cancer Manag Res, 2020, 12: 10919-10928. DOI: 10.2147/CMAR.S270727.
[12]
Promsorn J, Chadbunchachai P, Somsap K, et al. Imaging features associated with survival outcomes among colorectal cancer patients with and without KRAS mutation[J/OL]. Egypt J Radiol Nucl Med, 2021, 52(1): 15 [2022-05-08]. https://ejrnm.springeropen.com/articles/10.1186/s43055-020-00393-x. DOI: 10.1186/s43055-020-00393-x.
[13]
Jo SJ, Kim SH. Association between oncogenic RAS mutation and radiologic-pathologic findings in patients with primary rectal cancer[J]. Quant Imaging Med Surg, 2019, 9(2): 238-246. DOI: 10.21037/qims.2018.12.10.
[14]
Xu YY, Xu QY, Sun HL, et al. Could IVIM and ADC help in predicting the KRAS status in patients with rectal cancer?[J]. Eur Radiol, 2018, 28(7): 3059-3065. DOI: 10.1007/s00330-018-5329-y.
[15]
Gültekin MA, Türk HM, Beşiroğlu M, et al. Relationship between KRAS mutation and diffusion weighted imaging in colorectal liver metastases; Preliminary study[J/OL]. Eur J Radiol, 2020, 125: 108895 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/32109834. DOI: 10.1016/j.ejrad.2020.108895.
[16]
Arslan E, Aksoy T, Gürsu RU, et al. The prognostic value of 18F-FDG PET/CT and KRAS mutation in colorectal cancers[J]. Mol Imaging Radionucl Ther, 2020, 29(1): 17-24. DOI: 10.4274/mirt.galenos.2019.33866.
[17]
He P, Zou Y, Qiu J, et al. Pretreatment (18)F-FDG PET/CT Imaging Predicts the KRAS/NRAS/BRAF Gene Mutational Status in Colorectal Cancer[J/OL]. J Oncol, 2021, 2021: 6687291 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/34239564. DOI: 10.1155/2021/6687291.
[18]
Liu X, Wang SC, Ni M, et al. Correlation between 18F-FDG PET/CT intra-tumor metabolic heterogeneity parameters and KRAS mutation in colorectal cancer[J]. Abdom Radiol (NY), 2022, 47(4): 1255-1264. DOI: 10.1007/s00261-022-03432-5.
[19]
Lv Y, Wang X, Liang LR, et al. SUVmax and metabolic tumor volume: surrogate image biomarkers of KRAS mutation status in colorectal cancer[J]. Onco Targets Ther, 2019, 12: 2115-2121. DOI: 10.2147/OTT.S196725.
[20]
Oner AO, Budak ES, Yıldırım S, et al. The value of 18FDG PET/CT parameters, hematological parameters and tumor markers in predicting KRAS oncogene mutation in colorectal cancer[J]. Hell J Nucl Med, 2017, 20(2): 160-165. DOI: 10.1967/s002449910557.
[21]
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[J]. Mol Imaging Biol, 2019, 21(1): 183-190. DOI: 10.1007/s11307-018-1225-8.
[22]
Miles KA, Ganeshan B, Rodriguez-Justo M, et al. Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer[J]. J Nucl Med, 2014, 55(3): 386-391. DOI: 10.2967/jnumed.113.120485.
[23]
Taguchi N, Oda S, Yokota Y, et al. CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach[J]. Eur J Radiol, 2019, 118: 38-43. DOI: 10.1016/j.ejrad.2019.06.028.
[24]
González-Castro V, Cernadas E, Huelga E, et al. CT radiomics in colorectal cancer: detection of KRAS mutation using texture analysis and machine learning[J/OL]. Appl Sci, 2020, 10(18): 6214 [2022-05-08]. https://doi.org/10.3390/app10186214. DOI: 10.3390/app10186214.
[25]
Li Y, Eresen A, Shangguan JJ, et al. Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning[J]. J Cancer Res Clin Oncol, 2020, 146(12): 3165-3174. DOI: 10.1007/s00432-020-03354-z.
[26]
Lubner MG, Stabo N, Lubner SJ, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes[J]. Abdom Imaging, 2015, 40(7): 2331-2337. DOI: 10.1007/s00261-015-0438-4.
[27]
Cao J, Wang GR, Wang ZW, et al. CT texture analysis: a potential biomarker for evaluating KRAS mutational status in colorectal cancer[J]. Chung Kuo I Hsueh K'o Hsueh Tsa Chih, 2020, 35(4): 306-314. DOI: 10.24920/003770.
[28]
Cao YT. Prediction of molecular classification in colorectal cancer based on CT radiomics[D]. Lanzhou: Lanzhou University, 2021.
[29]
Oh JE, Kim MJ, Lee J, et al. Magnetic resonance-based texture analysis differentiating KRAS mutation status in rectal cancer[J]. Cancer Res Treat, 2020, 52(1): 51-59. DOI: 10.4143/crt.2019.050.
[30]
Xu Y, Xu Q, Ma Y, et al. Characterizing MRI features of rectal cancers with different KRAS status[J/OL]. BMC Cancer, 2019, 19(1): 1111 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/31727020. DOI: 10.1186/s12885-019-6341-6.
[31]
Fan RN, Li H, Lin LB, et al. MRI radiomics for predicting KRASgene mutation in colorectal cancer patients[J]. Chin J Med Imaging Technol, 2022, 38(3): 392-397. DOI: 10.13929/j.issn.1003-3289.2022.03.017.
[32]
Ma Y, Wang J, Song K, et al. Spatial-Frequency dual-branch attention model for determining KRAS mutation status in colorectal cancer with T2-weighted MRI[J/OL]. Comput Methods Programs Biomed, 2021, 209: 106311 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/34352652. DOI: 10.1016/j.cmpb.2021.106311.
[33]
Zhang Z, Shen L, Wang Y, et al. MRI Radiomics Signature as a Potential Biomarker for Predicting KRAS Status in Locally Advanced Rectal Cancer Patients[J/OL]. Front Oncol, 2021, 11: 614052 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/34026605. DOI: 10.3389/fonc.2021.614052.
[34]
Guo XF, Yang WQ, Yang Q, et al. Feasibility of MRI radiomics for predicting KRAS mutation in rectal cancer[J]. Curr Med Sci, 2020, 40(6): 1156-1160. DOI: 10.1007/s11596-020-2298-6.
[35]
Sun DQ, Wang LH, Li GZ, et al. To predict KRAS mutation in rectal cancer patients with texture analysis and functional MRI[J]. J Clin Radiol, 2021, 40(5): 924-929. DOI: 10.13437/j.cnki.jcr.2021.05.020.
[36]
Meng XC, Xia W, Xie PY, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer[J]. Eur Radiol, 2019, 29(6): 3200-3209. DOI: 10.1007/s00330-018-5763-x.
[37]
Tang X, Peng YJ, Chen YX, et al. Multiparametric MRI radiomics signature for prediction of KRAS gene mutation in rectal cancer[J]. Chin J Magn Reson Imaging, 2021, 12(11): 31-36. DOI: 10.12015/issn.1674-8034.2021.11.007.
[38]
Jiricny J. The multifaceted mismatch-repair system[J]. Nat Rev Mol Cell Biol, 2006, 7(5): 335-346. DOI: 10.1038/nrm1907.
[39]
Bao X, Zhang H, Wu W, et al. Analysis of the molecular nature associated with microsatellite status in colon cancer identifies clinical implications for immunotherapy[J/OL]. J Immunother Cancer, 2020, 8(2): e001437 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/33028695. DOI: 10.1136/jitc-2020-001437.
[40]
Golia Pernicka JS, Gagniere J, Chakraborty J, et al. Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation[J]. Abdom Radiol (NY), 2019, 44(11): 3755-3763. DOI: 10.1007/s00261-019-02117-w.
[41]
Fan SX, Li XB, Cui XN, et al. Computed tomography-based radiomic features could potentially predict microsatellite instability status in stage Ⅱ colorectal cancer: a preliminary study[J]. Acad Radiol, 2019, 26(12): 1633-1640. DOI: 10.1016/j.acra.2019.02.009.
[42]
Li Z, Zhong Q, Zhang L, et al. Computed Tomography-Based Radiomics Model to Preoperatively Predict Microsatellite Instability Status in Colorectal Cancer: A Multicenter Study[J/OL]. Front Oncol, 2021, 11: 666786 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/34277413. DOI: 10.3389/fonc.2021.666786.
[43]
Wu J, Zhang Q, Zhao Y, et al. Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer[J/OL]. Front Oncol, 2019, 9: 1250 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/31824843. DOI: 10.3389/fonc.2019.01250.
[44]
Huang ZX, Zhang W, He D, et al. Development and validation of a radiomics model based on T2WI images for preoperative prediction of microsatellite instability status in rectal cancer: Study Protocol Clinical Trial (SPIRIT Compliant)[J/OL]. Medicine (Baltimore), 2020, 99(10): e19428 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/32150094. DOI: 10.1097/MD.0000000000019428.
[45]
Zhang W, Huang Z, Zhao J, et al. Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer[J/OL]. Ann Transl Med, 2021, 9(2): 134 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/33569436. DOI: 10.21037/atm-20-7673.
[46]
Li Z, Dai H, Liu Y, et al. Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer[J/OL]. Front Oncol, 2021, 11: 697497 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/34307164. DOI: 10.3389/fonc.2021.697497.
[47]
Negreros-Osuna AA, Parakh A, Corcoran RB, et al. Radiomics Texture Features in Advanced Colorectal Cancer: Correlation with BRAF Mutation and 5-year Overall Survival[J/OL]. Radiol Imaging Cancer, 2020, 2(5): e190084 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/33778733. DOI: 10.1148/rycan.2020190084.
[48]
Yang L, Dong D, Fang MJ, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?[J]. Eur Radiol, 2018, 28(5): 2058-2067. DOI: 10.1007/s00330-017-5146-8.
[49]
Chu Y, Li J, Zeng Z, et al. A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer[J/OL]. Front Oncol, 2020, 10: 575422 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/33178604. DOI: 10.3389/fonc.2020.575422.
[50]
Badic B, Hatt M, Durand S, et al. Radiogenomics-based cancer prognosis in colorectal cancer[J/OL]. Sci Rep, 2019, 9(1): 9743 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/31278324. DOI: 10.1038/s41598-019-46286-6.
[51]
He JH, Wu HB, Zhou WL, et al. Comparison of PET conventional parameters and texture parameters in prediction of KRAS gene mutation in colorectal cancer[J]. Chin J Nucl Med Mol Imaging, 2018, 38(10): 662-667. DOI: 10.3760/cma.j.issn.2095-2848.2018.10.004.
[52]
Langen KM, Jones DTL. Organ motion and its management[J]. Int J Radiat Oncol, 2001, 50(1): 265-278. DOI: 10.1016/S0360-3016(01)01453-5.
[53]
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
[54]
Hong SN. Genetic and epigenetic alterations of colorectal cancer[J]. Intest Res, 2018, 16(3): 327-337. DOI: 10.5217/ir.2018.16.3.327.
[55]
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[J/OL]. Sci Rep, 2017, 7(1): 7882 [2022-05-08]. https://pubmed.ncbi.nlm.nih.gov/28801584. DOI: 10.1038/s41598-017-08413-z.
[56]
Dai SD, Inoue M, Tanabe T, et al. Prognostic impact of primary tumor location in Stage Ⅲ colorectal cancer-right-sided colon versus left-sided colon versus rectum: a nationwide multicenter retrospective study[J]. J Gastroenterol, 2020, 55(10): 958-968. DOI: 10.1007/s00535-020-01706-7.

PREV Research progress of machine learning model based on CT and MRI radiomics for predicting early recurrence of hepatocellular carcinoma
NEXT Mobile techniques in medical imaging: Challenges and advances
  


LINK


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