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

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

吴佩琪^{1, 2, 3,},
刘于宝^{2, 3, ,},
陈祉妍⁴,
蔡海桃⁵,
毛小明¹

1.
南方科技大学盐田医院（深圳市盐田区人民医院）放射科，广东深圳 518081
2.
南方医科大学深圳医院放射科，广东深圳 510086
3.
南方医科大学第三临床医学院，广东广州 510515
4.
暨南大学附属珠海医院（珠海市人民医院）放射科，广东广州 519000
5.
湖南中医药大学医学院，湖南长沙 410208

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

计量
- 文章访问数: 325
- HTML全文浏览量: 229
- PDF下载量: 46
- 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

Application progress of MRI-based peritumoral radiomics in tumor research

WU Peiqi^{1, 2, 3
,},
LIU Yubao^{2, 3
, ,},
CHEN Zhiyan⁴,
CAI Haitao⁵,
MAO Xiaoming¹

1.
Department of Radiology, Southern University of Science and Technology Yantian Hospital (Shenzhen Yantian District People's Hospital), Shenzhen 518081, China
2.
Department of Radiology, Shenzhen Hospital, Southern Medical University, Shenzhen 510086, China
3.
The Third School of Clinical Medicine, Southern Medical University, Shenzhen 510515, China
4.
Department of Radiology, Zhuhai Hospital Affiliated to Jinan University (Zhuhai People's Hospital), Guangzhou 519000, China
5.
School of Medicine, Hunan University of Chinese Medicine, Changsha 410208, China

摘要

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。
- 肿瘤 /
- 瘤周 /
- 影像组学 /
- 核磁共振成像 /
- 预测
Abstract: Radiomics can quantitatively mine and analyze the deep-level information of medical images. At present, MRI-based radiomics has been widely used in tumor research. Still, most studies have ignored the peritumoral radiomics features, which may contain information on the tumor microenvironment. Therefore, more and more studies have begun incorporating MRI peritumoral radiomics features into tumor research. Some progress has been made in tumor differential diagnosis, molecular typing, metastasis prediction, efficacy evaluation, recurrence, and prognosis prediction. This article reviews the application progress of MRI-based peritumoral radiomics in tumor research.
- tumor /
- peritumoral /
- radiomics /
- magnetic resonance imaging /
- prediction

HTML全文

参考文献(49)

[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