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结直肠癌基因突变状态预测的影像学研究进展

贾建业 丁聪 周围 柏根基

贾建业, 丁聪, 周围, 等. 结直肠癌基因突变状态预测的影像学研究进展[J]. CT理论与应用研究, 2023, 32(1): 147-152. DOI: 10.15953/j.ctta.2022.028
引用本文: 贾建业, 丁聪, 周围, 等. 结直肠癌基因突变状态预测的影像学研究进展[J]. CT理论与应用研究, 2023, 32(1): 147-152. DOI: 10.15953/j.ctta.2022.028
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)

结直肠癌基因突变状态预测的影像学研究进展

doi: 10.15953/j.ctta.2022.028
基金项目: 北京医卫健康公益基金(肝脏特异性(Gd-EOB-DTPA)增强MR成像“一站式”评估肝脏解剖与储备功能的应用研究(B20240ES))。
详细信息
    作者简介:

    贾建业:男,南京医科大学附属淮安市第一人民医院研究生,主要从事腹部磁共振研究,E-mail:844027685@qq.com

    柏根基:男,南京医科大学附属淮安市第一人民医院主任医师,主要从事腹部及肌骨磁共振研究,E-mail:hybgj0451@163.com

    通讯作者:

    男,南京医科大学附属淮安市第一人民医院主任医师,主要从事腹部及肌骨磁共振研究,E-mail:hybgj0451@163.com

  • 中图分类号: R  814;R  445

Advances in Research on Image-based Prediction of Colorectal in Cancer Gene Mutation Status

  • 摘要: 随着临床医师对于结直肠癌(CRC)患者个性化诊疗策略的进一步需求,CRC患者在确诊或病变转移时进行突变基因谱检测显得尤为重要,通过非侵入性的影像学检查分析肿瘤生物学特性,对CRC患者的遗传信息进行有效预测已成为该领域的研究热点。本文围绕不同的影像学方法预测CRC基因突变状态的应用进行综述。

     

  • [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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出版历程
  • 收稿日期:  2022-02-24
  • 修回日期:  2022-04-11
  • 录用日期:  2022-04-12
  • 网络出版日期:  2022-04-28
  • 刊出日期:  2023-01-31

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