ISSN 1004-4140
CN 11-3017/P
Volume 32 Issue 1
Jan.  2023
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JIA J Y, DING C, ZHOU W, et al. Advances in Research on Image-based Prediction of Colorectal in Cancer Gene Mutation Status[J]. CT Theory and Applications, 2023, 32(1): 147-152. DOI: 10.15953/j.ctta.2022.028. (in Chinese).
Citation: JIA J Y, DING C, ZHOU W, et al. Advances in Research on Image-based Prediction of Colorectal in Cancer Gene Mutation Status[J]. CT Theory and Applications, 2023, 32(1): 147-152. DOI: 10.15953/j.ctta.2022.028. (in Chinese).
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Advances in Research on Image-based Prediction of Colorectal in Cancer Gene Mutation Status

  • The First Affiliated Huai án Hospital of Nanjing Medical University, Huaian 223300, China

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  • Received Date: February 23, 2022
  • Revised Date: April 10, 2022
  • Accepted Date: April 11, 2022
  • Available Online: April 27, 2022
  • Published Date: January 30, 2023
    Graphical Abstract
    • Abstract
  • Clinicians are increasingly demanding personalized treatment strategies for patients with colorectal cancer (CRC). Detection of mutated gene profiles is particularly important when patients are diagnosed with CRC or metastatic CRC. Effective prediction of the gene status of patients with CRC by analyzing their tumor biological characteristics through non-invasive imaging has become a research hotspot. This review focuses on the application of different imaging methods to predict the gene mutation status in CRC.
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    • References (30)
  • [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[J]. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394−424. doi: 10.3322/caac.21492
    [2]
    SUNDAR R, HONG D S, KOPETZ S, et al. Targeting BRAF-mutant colorectal cancer: Progress in combination strategies[J]. Cancer Discovery, 2017, 7(6): 558−560. doi: 10.1158/2159-8290.CD-17-0087
    [3]
    DAI D, WANG Y, ZHU L, et al. Prognostic value of KRAS mutation status in colorectal cancer patients: A population-based competing risk analysis[J]. PeerJ, 2020, 8(6): e9149.
    [4]
    DERBEL O, WANG Q, DESSEIGNE F, et al. Impact of KRAS, BRAF and PI3 KCA mutations in rectal carcinomas treated with neoadjuvant radiochemotherapy and surgery[J]. BMC Cancer, 2013, 13: 200. doi: 10.1186/1471-2407-13-200
    [5]
    BENSON A B, VENOOK A P, AL-HAWARY M M, et al. Colon cancer, version 2. 2021, NCCN clinical practice guidelines in oncology[J]. Journal of National Comprehensive Cancer Network, 2021, 19(3): 329−359. doi: 10.6004/jnccn.2021.0012
    [6]
    SCLAFANI F, CHAU I, CUNNINGHAM D, et al. KRAS and BRAF mutations in circulating tumour DNA from locally advanced rectal cancer[J]. Scientific Reports, 2018, 8(1): 1445. doi: 10.1038/s41598-018-19212-5
    [7]
    TABERNERO J, LENZ H J, SIENA S, et al. Analysis of circulating DNA and protein biomarkers to predict the clinical activity of regorafenib and assess prognosis in patients with metastatic colorectal cancer: A retrospective, exploratory analysis of the CORRECT trial[J]. The Lancet Oncology, 2015, 16(8): 937−948. doi: 10.1016/S1470-2045(15)00138-2
    [8]
    SONG C, SHEN B, DONG Z, et al. Diameter of superior rectal vein-CT predictor of KRAS mutation in rectal carcinoma[J]. Cancer Management and Research, 2020, 12: 10919−10928. doi: 10.2147/CMAR.S270727
    [9]
    GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: Images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563−577. doi: 10.1148/radiol.2015151169
    [10]
    LI Y, ERESEN A, SHANGGUAN J, et al. Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning[J]. Journal of Cancer Research and Clinical Oncology, 2020, 146(12): 3165−3174. doi: 10.1007/s00432-020-03354-z
    [11]
    YANG L, DONG D, FANG M, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?[J]. European Radiology, 2018, 28(5): 2058−2067. doi: 10.1007/s00330-017-5146-8
    [12]
    HOSNY A, PARMAR C, QUACKENBUSH J, et al. Artificial intelligence in radiology[J]. Nature Reviews Cancer, 2018, 18(8): 500−510. doi: 10.1038/s41568-018-0016-5
    [13]
    HE K, LIU X, LI M, et al. Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging[J]. BMC Medical Imaging, 2020, 20(1): 59. doi: 10.1186/s12880-020-00457-4
    [14]
    SHI R, CHEN W, YANG B, et al. Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features[J]. American Journal of Cancer Research, 2020, 10(12): 4513−4526.
    [15]
    赵常红, 郝粉娥, 刘挨师. 胰腺癌定量双能CT碘图与CT灌注参数相关性研究[J]. 放射学实践, 2018,33(6): 578−592.

    ZHAO C H, HAO F E, LIU A S. Correlation between quantitative dual-energy CT iodine maps and CT perfusion parameters in patients with pancreatic carcinoma[J]. Radiologic Practice, 2018, 33(6): 578−592. (in Chinese).
    [16]
    CAO Y, ZHANG G, BAO H, et al. Development of a dual-energy spectral CT based nomogram for the preoperative discrimination of mutated and wild-type KRAS in patients with colorectal cancer[J]. Clinical Imaging, 2021, 69: 205−212. doi: 10.1016/j.clinimag.2020.08.023
    [17]
    ZHOU X, YI Y, LIU Z, et al. Radiomics-based pretherapeutic prediction of non-response to neoadjuvant therapy in locally advanced rectal cancer[J]. Annals of Surgical Oncology, 2019, 26(6): 1676−1684. doi: 10.1245/s10434-019-07300-3
    [18]
    LIANG M, CAI Z, ZHANG H, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis[J]. Academic Radiology, 2019, 26(11): 1495−1504. doi: 10.1016/j.acra.2018.12.019
    [19]
    XU Y, XU Q, MA Y, et al. Characterizing MRI features of rectal cancers with different KRAS status[J]. BMC Cancer, 2019, 19(1): 1111. doi: 10.1186/s12885-019-6341-6
    [20]
    OH J E, KIM M J, LEE J, et al. Magnetic Resonance-based texture analysis differentiating KRAS mutation status in rectal cancer[J]. Cancer Research and Treatment, 2020, 52(1): 51−59. doi: 10.4143/crt.2019.050
    [21]
    CHAN H P, SAMALA R K, HADJIISKI L M, et al. Deep learning in medical image analysis[J]. Advances in Experimental Medicine and Biology, 2020, 1213: 3−21.
    [22]
    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]. Computer Methods and Programs in Biomed, 2021, 209: 106311. doi: 10.1016/j.cmpb.2021.106311
    [23]
    ZHANG G, CHEN L, LIU A, et al. Comparable performance of deep learning-based to manual-based tumor segmentation in KRAS/NRAS/BRAF mutation prediction with MR-based radiomics in rectal cancer[J]. Frontiers in Oncology, 2021, 11: 696706. doi: 10.3389/fonc.2021.696706
    [24]
    孙丹琦, 王灵华, 李广政, 等. 纹理分析及功能磁共振成像预测直肠癌KRAS基因突变的可行性研究[J]. 临床放射学杂志, 2021,40(5): 924−929.

    SUN D Q, WANG L H, LI G Z, et al. To predict KRAS mutation in rectal cancer patients with texture analysis and functional MRI[J]. Journal of Clinical Radiology, 2021, 40(5): 924−929. (in Chinese).
    [25]
    CUI Y, CUI X, YANG X, et al. Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings[J]. Journal of Magnetic Resonance Imaging, 2019, 50(3): 930−939. doi: 10.1002/jmri.26653
    [26]
    CAICEDO C, GARCIA-VELLOSO M J, LOZANO M D, et al. Role of 18F-FDG PET in prediction of KRAS and EGFR mutation status in patients with advanced non-small-cell lung cancer[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2014, 41(11): 2058−2065. doi: 10.1007/s00259-014-2833-4
    [27]
    LOVINFOSSE P, KOOPMANSCH B, LAMBERT F, et al. 18F-FDG PET/CT imaging in rectal cancer: Relationship with the RAS mutational status[J]. The British Journal of Radiology, 2016, 89(1063): 20160212. doi: 10.1259/bjr.20160212
    [28]
    MAO W, ZHOU J, ZHANG H, et al. Relationship between KRAS mutations and dual time point 18F-FDG PET/CT imaging in colorectal liver metastases[J]. Abdominal Radiology (NY), 2019, 44(6): 2059−2066. doi: 10.1007/s00261-018-1740-8
    [29]
    LEE J H, KANG J, BAIK S H, et al. Relationship between 18F-fluorodeoxyglucose uptake and V-Ki-Ras2 kirsten rat sarcoma viral oncogene homolog mutation in colorectal cancer patients: Variability depending on C-reactive protein level[J]. Medicine, 2016, 95(1): e2236. doi: 10.1097/MD.0000000000002236
    [30]
    KIM S J, PAK K, KIM K. Diagnostic performance of 18F-FDG PET/CT for prediction of KRAS mutation in colorectal cancer patients: A systematic review and meta-analysis[J]. Abdominal Radiology (NY), 2019, 44(5): 1703−1711. doi: 10.1007/s00261-018-01891-3
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