留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码
x

基于MRI的瘤周影像组学在肿瘤研究中的应用进展

吴佩琪 刘于宝 陈祉妍 蔡海桃 毛小明

吴佩琪, 刘于宝, 陈祉妍, 蔡海桃, 毛小明. 基于MRI的瘤周影像组学在肿瘤研究中的应用进展[J]. 分子影像学杂志, 2023, 46(1): 164-169. doi: 10.12122/j.issn.1674-4500.2023.01.32
引用本文: 吴佩琪, 刘于宝, 陈祉妍, 蔡海桃, 毛小明. 基于MRI的瘤周影像组学在肿瘤研究中的应用进展[J]. 分子影像学杂志, 2023, 46(1): 164-169. doi: 10.12122/j.issn.1674-4500.2023.01.32
WU Peiqi, LIU Yubao, CHEN Zhiyan, CAI Haitao, MAO Xiaoming. Application progress of MRI-based peritumoral radiomics in tumor research[J]. Journal of Molecular Imaging, 2023, 46(1): 164-169. doi: 10.12122/j.issn.1674-4500.2023.01.32
Citation: WU Peiqi, LIU Yubao, CHEN Zhiyan, CAI Haitao, MAO Xiaoming. Application progress of MRI-based peritumoral radiomics in tumor research[J]. Journal of Molecular Imaging, 2023, 46(1): 164-169. doi: 10.12122/j.issn.1674-4500.2023.01.32

基于MRI的瘤周影像组学在肿瘤研究中的应用进展

doi: 10.12122/j.issn.1674-4500.2023.01.32
基金项目: 

国家高性能医疗器械创新中心开放基金面上项目 NMED2021MS-01-003

广东省医学科学技术研究基金项目 B2022071

深圳市科技计划项目 JCYJ20210324132809023

深圳市科技计划项目 JCYJ2021032412540301

深圳市盐田区软科学研究及社会公益性项目 YTWS20200204

详细信息
    作者简介:

    吴佩琪,在读博士研究生,主治医师,E-mail: peiqi_wu1992@163.com

    通讯作者:

    刘于宝,博士生导师,主任医师,E-mail: ybliu28@163.com

Application progress of MRI-based peritumoral radiomics in tumor research

  • 摘要: 影像组学可定量挖掘和分析医学图像的深层次信息,目前基于MRI的影像组学已广泛应用于肿瘤研究,但大多数研究忽视了瘤周影像组学特征,而瘤周影像组学特征可能蕴含肿瘤微环境相关信息。因此,越来越多的研究开始将MRI瘤周影像组学特征纳入到肿瘤研究中,在肿瘤的鉴别诊断、分子分型、转移预测、疗效评估、复发和预后预测等方面取得了一定的进展。本文就基于MRI的瘤周影像组学在肿瘤研究中的应用进展进行综述。

     

  • [1] Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022[J]. CA A Cancer J Clinicians, 2022, 72(1): 7-33. doi: 10.3322/caac.21708
    [2] Global Burden of Disease 2019 Cancer Collaboration, Kocarnik JM, Compton K, et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019[J]. JAMA Oncol, 2022, 8(3): 420-44. doi: 10.1001/jamaoncol.2021.6987
    [3] Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-57.
    [4] Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology[J]. Nat Rev Clin Oncol, 2022, 19(2): 132-46. doi: 10.1038/s41571-021-00560-7
    [5] 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): 749-62. doi: 10.1038/nrclinonc.2017.141
    [6] Ibrahim A, Primakov S, Beuque M, et al. Radiomics for precision medicine: current challenges, future prospects, and the proposal of a new framework[J]. Methods, 2021, 188: 20-9. doi: 10.1016/j.ymeth.2020.05.022
    [7] Jha AK, Mithun S, Purandare NC, et al. Radiomics: a quantitative imaging biomarker in precision oncology[J]. Nucl Med Commun, 2022, 43(5): 483-93. doi: 10.1097/MNM.0000000000001543
    [8] Paget S, et al. The distribution of secondary growths in cancer of the breast[J]. Lancet, 1889, 133(3421): 571-3. doi: 10.1016/S0140-6736(00)49915-0
    [9] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing[J]. N Engl J Med, 2012, 366(10): 883-92. doi: 10.1056/NEJMoa1113205
    [10] Tekpli X, Lien T, Røssevold AH, et al. An independent poorprognosis subtype of breast cancer defined by a distinct tumor immune microenvironment[J]. Nat Commun, 2019, 10: 5499. doi: 10.1038/s41467-019-13329-5
    [11] Bennani-Baiti B, Pinker K, Zimmermann M, et al. Non-invasive assessment of hypoxia and neovascularization with MRI for identification of aggressive breast cancer[J]. Cancers, 2020, 12(8): 2024. doi: 10.3390/cancers12082024
    [12] Braman N, Prasanna P, Whitney J, et al. Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)-positive breast cancer[J]. JAMA Netw Open, 2019, 2(4): e192561. doi: 10.1001/jamanetworkopen.2019.2561
    [13] Wild AE, Weiderpass E, Stewart BW, et al. World Cancer Report: Cancer Research for Cancer Prevention[M]. Lyon: International Agency for Research on Cancer, 2020.
    [14] DeSantis CE, Ma JM, Gaudet MM, et al. Breast cancer statistics, 2019[J]. CA A Cancer J Clin, 2019, 69(6): 438-51. doi: 10.3322/caac.21583
    [15] Chen WQ, Zheng RS, Baade PD, et al. Cancer statistics in China, 2015[J]. CA A Cancer J Clin, 2016, 66(2): 115-32. doi: 10.3322/caac.21338
    [16] Xia CF, Dong XS, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants[J]. Chin Med J (Engl), 2022, 135(5): 584-90. doi: 10.1097/CM9.0000000000002108
    [17] Ding R, Xiao Y, Mo M, et al. Breast cancer screening and early diagnosis in Chinese women[J]. Cancer Biol Med, 2022, 19(4): 450-67. doi: 10.20892/j.issn.2095-3941.2021.0676
    [18] Zhou JJ, Zhang Y, Chang KT, et al. Diagnosis of benign and malignant breast lesions on DCE- MRI by using radiomics and deep learning with consideration of peritumor tissue[J]. J Magn Reson Imaging, 2020, 51(3): 798-809. doi: 10.1002/jmri.26981
    [19] Lee HJ, Nguyen AT, Ki SY, et al. Classification of MR- detected additional lesions in patients with breast cancer using a combination of radiomics analysis and machine learning[J]. Front Oncol, 2021, 11: 744460. doi: 10.3389/fonc.2021.744460
    [20] 李宝明. 基于影像组学的三阴性乳腺癌分子亚型预测[D]. 南京: 南京信息工程大学, 2020.
    [21] 陆欢, 葛敏, 王世威. 动态增强MRI瘤内与瘤周影像组学特征对三阴性乳腺癌的诊断价值研究[J]. 浙江医学, 2021, 43(15): 1647-51, 1710. https://www.cnki.com.cn/Article/CJFDTOTAL-ZJYE202115013.htm
    [22] Li CL, Song LR, Yin JD. Intratumoral and peritumoral radiomics based on functional parametric maps from breast DCE-MRI for prediction of HER-2 and ki-67 status[J]. J Magn Reson Imaging, 2021, 54(3): 703-14. doi: 10.1002/jmri.27651
    [23] Liu CL, Ding J, Spuhler K, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2019, 49(1): 131-40. doi: 10.1002/jmri.26224
    [24] Ding J, et al. Optimizing the peritumoral region size in radiomics analysis for sentinel lymph node status prediction in breast cancer[J]. Acad Radiol, 2022, 29: S223-8. doi: 10.1016/j.acra.2020.10.015
    [25] 周佳丽. 基于MRI影像组学分析对乳腺癌新辅助化疗病理缓解早期术前预测[D]. 杭州: 浙江中医药大学, 2019.
    [26] 王雷. 基于影像组学的乳腺癌新辅助化疗疗效预测[D]. 南京: 南京信息工程大学, 2021.
    [27] Jemal A, Ward EM, Johnson CJ, et al. Annual report to the nation on the status of cancer, 1975-2014, featuring survival[J]. J Natl Cancer Inst, 2017, 109(9): djx030.
    [28] Zhang R, Xu L, Wen X, et al. A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J]. Quant Imaging Med Surg, 2019, 9(9): 1503-15. doi: 10.21037/qims.2019.09.07
    [29] Chong HH, Yang L, Sheng RF, et al. Multi-scale and multiparametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma≤5 cm[J]. Eur Radiol, 2021, 31 (7): 4824-38. doi: 10.1007/s00330-020-07601-2
    [30] Chong HH, Gong YD, Pan XP, et al. Peritumoral dilation radiomics of gadoxetate disodium-enhanced MRI excellently predicts early recurrence of hepatocellular carcinoma without macrovascular invasion after hepatectomy[J]. J Hepatocell Carcinoma, 2021, 8: 545-63. doi: 10.2147/JHC.S309570
    [31] Yu YX, Fan YF, Wang XM, et al. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma[J]. Eur Radiol, 2022, 32(2): 959-70. doi: 10.1007/s00330-021-08250-9
    [32] Kim S, Shin J, Kim DY, et al. Radiomics on gadoxetic acidenhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma[J]. Clin Cancer Res, 2019, 25(13): 3847-55. doi: 10.1158/1078-0432.CCR-18-2861
    [33] Zhang L, Hu JM, Hou JY, et al. Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection[J]. Abdom Radiol, 2021, 46(8): 3845-54. doi: 10.1007/s00261-021-03034-7
    [34] Xiao D, Wang J, Wang X, et al. Distinguishing brain abscess from necrotic glioblastoma using MRI-based intranodular radiomic features and peritumoral edema/tumor volume ratio[J]. J Integr Neurosci, 2021, 20(3): 623. doi: 10.31083/j.jin2003066
    [35] Qian ZH, Zhang LL, Hu J, et al. Machine learning-based analysis of magnetic resonance radiomics for the classification of gliosarcoma and glioblastoma[J]. Front Oncol, 2021, 11: 699789. doi: 10.3389/fonc.2021.699789
    [36] Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, et al. Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis[J]. Eur Radiol, 2022, 32(6): 3705-15. doi: 10.1007/s00330-021-08498-1
    [37] Samani ZR, Parker D, Wolf R, et al. Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases[J]. Sci Rep, 2021, 11: 14469. doi: 10.1038/s41598-021-93804-6
    [38] Cheng JH, Liu J, Yue HL, et al. Prediction of glioma grade using intratumoral and peritumoral radiomic features from multiparametric MRI images[J]. IEEE/ACM Trans Comput Biol Bioinform, 2022, 19(2): 1084-95. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9239868
    [39] Sun XJ, Pang PP, Lou L, et al. Radiomic prediction models for the level of Ki-67 and p53 in glioma[J]. J Int Med Res, 2020, 48(5): 300060520914466.
    [40] Jiang CD, Kong ZR, Zhang YW, et al. Conventional magnetic resonance imaging- based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade Ⅱ and Ⅲ gliomas[J]. Neuroradiology, 2020, 62(7): 803-13. doi: 10.1007/s00234-020-02392-1
    [41] Sun K, et al. A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions[J]. Eur J Radiol, 2022, 149: 110187. doi: 10.1016/j.ejrad.2022.110187
    [42] Li N, Mo Y, Huang CC, et al. A clinical semantic and radiomics nomogram for predicting brain invasion in WHO grade Ⅱ meningioma based on tumor and tumor-to-brain interface features[J]. Front Oncol, 2021, 11: 752158. doi: 10.3389/fonc.2021.752158
    [43] Xiao DD, Zhao Z, Liu J, et al. Diagnosis of invasive meningioma based on brain-tumor interface radiomics features on brain MR images: a multicenter study[J]. Front Oncol, 2021, 11: 708040. doi: 10.3389/fonc.2021.708040
    [44] Wu QX, et al. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer[J]. Radiother Oncol, 2019, 138: 141-8. doi: 10.1016/j.radonc.2019.04.035
    [45] Shi JX, et al. MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: a multi-center study[J]. Magn Reson Imaging, 2022, 88: 1-8. doi: 10.1016/j.mri.2021.12.008
    [46] Takada A, Yokota H, Nemoto MW, et al. A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions[J]. Jpn J Radiol, 2020, 38(3): 265-73. doi: 10.1007/s11604-019-00917-0
    [47] Algohary A, Shiradkar R, Pahwa S, et al. Combination of peritumoral and intra-tumoral radiomic features on Bi-parametric MRI accurately stratifies prostate cancer risk: a multi- site study[J]. Cancers, 2020, 12(8): 2200. doi: 10.3390/cancers12082200
    [48] Bai HL, Xia W, Ji XF, et al. Multiparametric magnetic resonance imaging-based peritumoral radiomics for preoperative prediction of the presence of extracapsular extension with prostate cancer[J]. J Magn Reson Imaging, 2021, 54(4): 1222-30. doi: 10.1002/jmri.27678
    [49] Holbrook MD, Blocker SJ, Mowery YM, et al. MRI-based deep learning segmentation and radiomics of sarcoma in mice[J]. Tomography, 2020, 6(1): 23-33. doi: 10.18383/j.tom.2019.00021
  • 加载中
计量
  • 文章访问数:  225
  • HTML全文浏览量:  188
  • PDF下载量:  36
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-10-11
  • 网络出版日期:  2023-01-18
  • 刊出日期:  2023-01-20

目录

    /

    返回文章
    返回

    关于《分子影像学杂志》变更刊期通知

    各位专家、作者、读者:

    为了缩短出版时滞,促进科研成果的快速传播,我刊自2024年1月起,刊期由双月刊变更为月刊。本刊主要栏目有:基础研究、临床研究、技术方法、综述等。

    感谢各位专家、作者、读者长期以来对我刊的支持与厚爱!

    南方医科大学学报编辑部

    《分子影像学杂志》

    2023年12月27日