前列腺癌磁共振成像的放射组学： 目前已知的是什么？

封面

如何引用文章

全文:

详细

前列腺癌的诊断和治疗方法依赖于磁共振成像和组织学数据的结合。

这篇综述的目的是向读者介绍利用磁共振成像对前列腺癌进行现代诊断的基本方法，重点是数字医学图像的纹理分析。

纹理分析使使用数学方法评估图像像素之间的关系成为可能，这提供了额外的信息，主要是关于肿瘤内异质性的信息。一阶特征的纹理分析可能比高阶纹理特征具有更大的临床再现性。从扩散系数图中提取的纹理特征具有最大的临床意义。

未来的研究应侧重于整合机器学习技术，以促进纹理分析在临床实践中的应用。需要开发自动分割方法，以降低将正常组织纳入感兴趣区域的可能性，并加快分析结果的传递。为了测试纹理特征的诊断潜力，需要进行大规模的前瞻性研究。

作者简介

Pavel B. Gelezhe

Moscow Center for Diagnostics and Telemedicine; European Medical Center

Email: gelezhe.pavel@gmail.com
ORCID iD: 0000-0003-1072-2202
SPIN 代码: 4841-3234

MD, Cand. Sci. (Med.)

俄罗斯联邦, 24-1 Petrovka street, 127051, Moscow; Moscow

Ivan A. Blokhin

Moscow Center for Diagnostics and Telemedicine

Email: i.blokhin@npcmr.ru
ORCID iD: 0000-0002-2681-9378
SPIN 代码: 3306-1387
俄罗斯联邦, 24-1 Petrovka street, 127051, Moscow

Serafim S. Semenov

Moscow Center for Diagnostics and Telemedicine; Moscow Clinical Scientific Center named after A.S. Loginov

Email: s.semenov@npcmr.ru
ORCID iD: 0000-0003-2585-0864
SPIN 代码: 4790-0416

MD

俄罗斯联邦, 24-1 Petrovka street, 127051, Moscow; Moscow

Damiano Caruso

Sapienza University of Rome; Sant’Andrea University Hospital

编辑信件的主要联系方式.
Email: dcaruso85@gmail.com
ORCID iD: 0000-0001-9285-4764

MD, PhD, Department of Surgical and Medical Sciences and Translational Medicine, Radiology Unit

意大利, Rome; Rome

参考

  1. Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol. 2019;76(3):340–351. doi: 10.1016/j.eururo.2019.02.033
  2. Kasivisvanathan V, Rannikko AS, Borghi M, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med. 2018;378(19):1767–1777. doi: 10.1056/NEJMoa1801993
  3. Ahmed HU, El-Shater Bosaily A, Brown LC, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. The Lancet. 2017;389(10071):815–822. doi: 10.1016/S0140-6736(16)32401-1
  4. Purysko AS, Rosenkrantz AB, Barentsz JO, et al. PI-RADS version 2: a pictorial update. Radiographics. 2016;36(5):1354–1372. doi: 10.1148/rg.2016150234
  5. Patel N, Henry A, Scarsbrook A. The value of MR textural analysis in prostate cancer. Clin Radiol. 2019;74(11):876–885. doi: 10.1016/j.crad.2018.11.007
  6. Sala E, Mema E, Himoto Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2017;72(1):3–10. doi: 10.1016/j.crad.2016.09.013
  7. Gleason DF. Classification of prostatic carcinomas. Cancer Chemother Rep. 1966;50(3):125–128.
  8. Young JC, Jeong KK, Kim N, et al. Functional MR imaging of prostate cancer. Radiographics. 2007;27(1): 63–75. doi: 10.1148/rg.271065078
  9. Nketiah G, Elschot M, Kim E, et al. T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol. 2017;27(7):3050–3059. doi: 10.1007/s00330-016-4663-1
  10. Morone M, Bali MA, Tunariu N, et al. Whole-body MRI: current applications in oncology. AJR Am J Roentgenol. 2017;209(6):W336–W349. doi: 10.2214/AJR.17.17984
  11. Nowak J, Malzahn U, Baur AD, et al. The value of ADC, T2 signal intensity, and a combination of both parameters to assess Gleason score and primary Gleason grades in patients with known prostate cancer. Acta Radiol. 2016;57(1):107–114. doi: 10.1177/0284185114561915
  12. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278(2):563–577. doi: 10.1148/radiol.2015151169
  13. Summers RM. Texture analysis in radiology: Does the emperor have no clothes? Abdom Radiol. 2017;42(2):342–345. doi: 10.1007/s00261-016-0950-1
  14. Bleker J, Kwee TC, Dierckx RA, et al. Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer. Eur Radiol. 2020;30(3):1313–1324. doi: 10.1007/s00330-019-06488-y
  15. Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol. 2017;90(1070):20160642. doi: 10.1259/bjr.20160642
  16. Wibmer A, Hricak H, Gondo T, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol. 2015;25(10):2840–2850. doi: 10.1007/s00330-015-3701-8
  17. Losnegard A, Reisæter L, Halvorsen OJ, et al. Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediateand high-risk prostate cancer patients. Acta Radiol. 2020;61(11):1570–1579. doi: 10.1177/0284185120905066
  18. Larue RT, Defraene G, Ruysscher DD, et al. Quantitative radiomics studies for tissue characterization: A review of technology and methodological procedures. Br J Radiol. 2017;90(1070):20160665. doi: 10.1259/bjr.20160665
  19. Court LE, Fave X, Mackin D, et al. Computational resources for radiomics. Translational Cancer Research. 2016;5(4):340–348. doi: 10.21037/tcr.2016.06.17
  20. Laplacian of Gaussian Filter [Electronic resource]. Available from: https://academic.mu.edu/phys/matthysd/web226/Lab02.htm. Accessed: 21.11.2021.
  21. Fehr D, Veeraraghavan H, Wibmer A, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance. Proc Natl Acad Sci U S A. 2015;112(46):E6265–E6273. doi: 10.1073/pnas.1505935112
  22. Sidhu HS, Benigno S, Ganeshan B, et al. Textural analysis of multiparametric MRI detects transition zone prostate cancer. Eur Radiol. 2017;27(6):2348–2358. doi: 10.1007/s00330-016-4579-9
  23. Vignati A, Mazzetti S, Giannini V, et al. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol. 2015;60(7):2685–2701. doi: 10.1088/0031-9155/60/7/2685
  24. Sierra PS, Damodaran S, Jarrard D. Clinical and pathologic factors predicting reclassification in active surveillance cohorts. Int Braz J Urol. 2018;44(3):440. doi: 10.1590/S1677-5538.IBJU.2017.0320
  25. Murciano-Goroff YR, Wolfsberger LD, Parekh A, et al. Variability in MRI vs. ultrasound measures of prostate volume and its impact on treatment recommendations for favorable-risk prostate cancer patients: a case series. Radiat Oncol. 2014;9:200. doi: 10.1186/1748-717X-9-200
  26. Engels RR, Israël B, Padhani AR, et al. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 1: acquisition. Eur Urology. 2020;77(4):457–468. doi: 10.1016/j.eururo.2019.09.021
  27. Min X, Li M, Dong D, et al. Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: cross-validation of a machine learning method. Eur J Radiol. 2019;115:16–21. doi: 10.1016/j.ejrad.2019.03.010
  28. Westphalen AC, McCulloch CE, Anaokar JM, et al. Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology. 2020;296(1):76–84. doi: 10.1148/radiol.2020190646
  29. Xu L, Zhang G, Zhao L, et al. Radiomics based on multiparametric magnetic resonance imaging to predict extraprostatic extension of prostate cancer. Front Oncol. 2020;10:940. doi: 10.3389/fonc.2020.00940
  30. Kuess P, Andrzejewski P, Nilsson D, et al. Association between pathology and texture features of multi parametric MRI of the prostate. Phys Med Biol. 2017;62(19):7833–7854. doi: 10.1088/1361-6560/aa884d
  31. Riaz N, Afaq A, Akin O, et al. Pretreatment endorectal coil magnetic resonance imaging findings predict biochemical tumor control in prostate cancer patients treated with combination brachytherapy and external-beam radiotherapy. Int J Radiat Oncol Biol Phys. 2012;84(3):707–711. doi: 10.1016/j.ijrobp.2012.01.009
  32. Gnep K, Fargeas A, Gutiérrez-Carvajal RE, et al. Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging. 2017;45(1):103–117. doi: 10.1002/jmri.25335
  33. Ginsburg SB, Rusu M, Kurhanewicz J, et al. Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer. SPIE. 2014;9035:903509. doi: 10.1117/12.2043937
  34. Park SY, Kim CK, Park BK, et al. Prediction of biochemical recurrence following radical prostatectomy in men with prostate cancer by diffusion-weighted magnetic resonance imaging: Initial results. Eur Radiol. 2011;21(5):1111–1118. doi: 10.1007/s00330-010-1999-9
  35. Woo S, Kim SY, Cho JY, et al. Preoperative evaluation of prostate cancer aggressiveness: Using ADC and ADC ratio in determining gleason score. AJR Am J Roentgenol. 2016;207(1):114–120. doi: 10.2214/AJR.15.15894
  36. Incoronato M, Aiello M, Infante T, et al. Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 2017;18(4):805. doi: 10.3390/ijms18040805
  37. Jamshidi N, Margolis DJ, Raman S, et al. Multiregional radiogenomic assessment of prostate microenvironments with multiparametric MR imaging and DNA whole-exome sequencing of prostate glands with adenocarcinoma. Radiology. 2017;284(1):109–119. doi: 10.1148/radiol.2017162827
  38. Stoyanova R, Pollack A, Takhar M, et al. Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget. 2016;7(33):53362–53376. doi: 10.18632/oncotarget.10523

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 2. Segmentation and evaluation of entropy of the tumor focus of the transitional zone of the prostate gland.

下载 (317KB)
索引源数据
3. 图 1基于T2加权像的前列腺癌放射组学工作流程模型。

下载 (212KB)
索引源数据

版权所有 © Gelezhe P., Blokhin I., Semenov S., Caruso D., 2022

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

##common.cookie##