乳房造影检查在乳腺癌放射学中的作用

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乳房造影检查是目前筛查乳腺癌的唯一方法。尽管数字乳房x线照相术是检测乳腺癌的主要和最广泛可用的方法，但其在检测和评估肿瘤的肿瘤内异质性方面的有效性有限。由于组织样本或肿瘤体积小，穿刺活检不能反映整个肿瘤的组织学图片。由于这个原因，选择适当的治疗方法和确定预后变得复杂。在这种情况下，医学成像这样的非侵入性方法给出了肿瘤的更完整的画面，是有希望的»虚拟活检»，以及用于监测疾病的进展和对治疗的反应。

使用纹理分析的放射学允许您将图像视为一组数值特征，超越通常的定性视觉感知强度，并继续深入分析数字，像素数据，以提高差分诊断的准确性。放射基因组学方法是放射组学的自然延伸，侧重于根据肿瘤的放射表型确定基因的表达。该综述探讨了在乳腺癌的放射组学和放射基因组学中使用乳房造影照相术的可能性。

本文概述了PubMed，Medline，Springer，eLibrary数据库的文献，以及使用Google学术搜索找到的相关俄罗斯科学文章。将获得的相关信息组合，结构与分析，以研究乳房造影照相术在乳腺癌放射组学中的作用。

作者简介

Veronika G. Govorukhina

Lomonosov Moscow State University

Email: govorukhinaver@gmail.com
ORCID iD: 0000-0002-1611-9618
SPIN 代码: 7038-8580

MD

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

Serafim S. Semenov

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Healthcare of Moscow; Moscow Clinical Scientific Center n.a. A.S. Loginov, Moscow Healthcare Department

编辑信件的主要联系方式.
Email: s.semenov@npcmr.ru
ORCID iD: 0000-0003-2585-0864
SPIN 代码: 4790-0416

Engineer of Medical Informatics, Radiomics & Radiogenomics group, Radiology Clinical Resident, MD

俄罗斯联邦, Moscow; Moscow

Pavel B. Gelezhe

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care

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, Moscow

Vera V. Didenko

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care; City Clinical Oncology Hospital № 1

Email: didenko@npcmr.ru
ORCID iD: 0000-0001-9068-1273
SPIN 代码: 5033-8376

MD

俄罗斯联邦, Moscow; Moscow

Sergey P. Morozov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care

Email: morozov@npcmr.ru
ORCID iD: 0000-0001-6545-6170
SPIN 代码: 8542-1720

MD, Dr. Sci. (Med.), Professor

俄罗斯联邦, Moscow

Anna E. Andreychenko

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care

Email: a.andreychenko@npcmr.ru
ORCID iD: 0000-0001-6359-0763
SPIN 代码: 6625-4186

Cand. Sci. (Phys.-Math.)

俄罗斯联邦, Moscow

参考

  1. Kaprin AD, Starinsky VV, Petrova GV. Malignant neoplasms in Russia in 2018 (morbidity and mortality). Moscow : P.A. Herzen Moscow State Research Institute; 2019. 250 р. (In Russ).
  2. Kaprin AD, Starinsky VV. The state of oncological care for the population of Russia in 2019. Moscow : P.A. Herzen Moscow State Research Institute; 2020. 239 p. (In Russ).
  3. Grishina KA, Muzaffarova TA, Khailenko VA, Karpukhin AV. Molecular genetic markers of breast cancer. Tumors of the female reproductive system. 2016;12(3):36–42. (In Russ). doi: 10.17650/1994-4098-2016-12-3-36-42
  4. Wu M, Ma J. Association between imaging characteristics and different molecular subtypes of breast cancer. Acad Radiol. 2017;24(4):426–434. doi: 10.1016/j.acra.2016.11.012
  5. Stenina MB, Zhukova LG, Koroleva IA, et al. Practical recommendations for the drug treatment of breast cancer. Malig tumours. 2021;10(3). (In Russ).
  6. Pesapane F, Suter MB, Rotili A, et al. Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation? Med Oncol. 2020;37(4):29. doi: 10.1007/s12032-020-01353-1
  7. Januškevičienė I, Petrikaitė V. Heterogeneity of breast cancer: the importance of interaction between different tumor cell populations. Life Sci. 2019;(239):117009. doi: 10.1016/j.lfs.2019.117009
  8. Turashvili G, Brogi E. Tumor heterogeneity in breast cancer. Front Med. 2017;4:227. doi: 10.3389/fmed.2017.00227
  9. Zavyalova MV, Vtorushin SV, Tsyganov MM. Intra-tumor heterogeneity: nature and biological significance. Review. Biochemistry. 2013;78(11):1531–1549. (In Russ).
  10. Ma W, Zhao Y, Ji Y, et al. Breast cancer molecular subtype prediction by mammographic radiomic features. Acad Radiol. 2019;26(2):196–201. doi: 10.1016/j.acra.2018.01.023
  11. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. doi: 10.1038/ncomms5006
  12. Lin F, Wang Z, Zhang K, et al. Contrast-Enhanced spectral mammography-based radiomics nomogram for identifying benign and malignant breast lesions of Sub-1 cm. Front Oncol. 2020;10:573630. doi: 10.3389/fonc.2020.573630
  13. Li X, Qin G, He Q, et al. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification. Eur Radiol. 2020;30(2):778–788. doi: 10.1007/s00330-019-06457-5
  14. Nazari SS, Mukherjee P. An overview of mammographic density and its association with breast cancer. Breast Cancer. 2018;25(3):259–267. doi: 10.1007/s12282-018-0857-5
  15. Conti A, Duggento A, Indovina I, et al. Radiomics in breast cancer classification and prediction. Semin Cancer Biol. 2021;72:238–250. doi: 10.1016/j.semcancer.2020.04.002
  16. Hogg P, Kelly J, Mercer C. Digital mammography: A holistic approach. Springer; 2015. 309 p.
  17. Demircioglu O, Uluer M, Aribal E. How many of the biopsy decisions taken at inexperienced breast radiology units were correct? J Breast Heal. 2017;13(1):23–26. doi: 10.5152/tjbh.2016.2962
  18. Valdora F, Houssami N, Rossi F, et al. Rapid review: radiomics and breast cancer. Breast Cancer Res Treat. 2018;169(2):217–229. doi: 10.1007/s10549-018-4675-4
  19. Mayerhoefer ME, Materka A, Langset G, et al. Introduction to radiomics. J Nucl Med. 2020;61(4):488–495. doi: 10.2967/jnumed.118.222893
  20. Lambin P, Zindler J, Vanneste BG, et al. Decision support systems for personalized and participative radiation oncology. Adv Drug Deliv Rev. 2017;109:131–153. doi: 10.1016/j.addr.2016.01.006
  21. Wen YL, Leech M. Review of the role of radiomics in tumour risk classification and prognosis of cancer. Anticancer Research. 2020;40(7):3605–3618. doi: 10.21873/anticanres.14350
  22. 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
  23. Lambin P, Leijenaar RT, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–762. doi: 10.1038/nrclinonc.2017.141
  24. Vailati-Riboni M, Palombo V, Loor JJ. What are omics sciences? Periparturient diseases of dairy cows: a systems biology approach. 2017. Р. 1–7. doi: 10.1007/978-3-319-43033-1_1
  25. Porcu M, Solinas C, Mannelli L, et al. Radiomics and “radi-omics” in cancer immunotherapy: a guide for clinicians. Crit Rev Oncol Hematol. 2020;154:103068. doi: 10.1016/j.critrevonc.2020.103068
  26. Ognerubov NA, Shatov IA, Shatov AV. Radiogenomics and radiomics in the diagnostics of malignant tumours: a literary review. Tambov Univ Reports Ser Nat Tech Sci. 2017;22(6-2):1453–1460. doi: 10.20310/1810-0198-2017-22-6-1453-1460
  27. Nasief H, Hall W, Zhenget C, et al. Improving treatment response prediction for chemoradiation therapy of pancreatic cancer using a combination of delta-radiomics and the clinical biomarker CA19-9. Front Oncol. 2020;9:1464. doi: 10.3389/fonc.2019.01464
  28. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–446. doi: 10.1016/j.ejca.2011.11.036
  29. Kerns SL, Ostrer H, Rosenstein BS. Radiogenomics: Using genetics to identify cancer patients at risk for development of adverse effects following radiotherapy. Cancer Discovery. 2014;4(2):155–165. doi: 10.1158/2159-8290
  30. Neri E, Del Re M, Paiar F, et al. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging. 2018;9(6):915–924. doi: 10.1007/s13244-018-0657-7
  31. Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36. doi: 10.1186/s41747-018-0068-z
  32. Kumar V, Gu Y, Basu S, et al. Radiomics: The process and the challenges. Magn Reson Imaging. 2012;30(9):1234–1248. doi: 10.1016/j.mri.2012.06.010
  33. Sala E, Mema E, Himoto Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clinical Radiology. 2017;72(1):3–10. doi: 10.1016/j.crad.2016.09.013
  34. Lee SH, Park H, Ko ES. Radiomics in breast imaging from techniques to clinical applications: A review. Korean J Radiol. 2020;21(7):779–792. doi: 10.3348/kjr.2019.0855
  35. Tagliafico AS, Piana M, Schenone D, et al. Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 2020;(49):74–80. doi: 10.1016/j.breast.2019.10.018
  36. Li H, Giger ML, Huo Z, et al. Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: Effect of ROI size and location. Med Phys. 2004;31(3):549–555. doi: 10.1118/1.1644514
  37. Holbrook MD, Blocker SJ, Mowery YM, et al. Mri-based deep learning segmentation and radiomics of sarcoma in mice. Tomography. 2020;6(1):23–33. doi: 10.18383/j.tom.2019.00021
  38. Pratt W. Digital Image Processing. Moscow : World; 1982. 480 p.
  39. Santos JM, Oliveira BC, de Araujo-Filho J, et al. State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations. Abdom Radiol. 2020;45(2):342–353. doi: 10.1007/s00261-019-02299-3
  40. Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Medica. 2017;(38):122–139. doi: 10.1016/j.ejmp.2017.05.071
  41. Verma M, Raman B, Murala S. Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing. 2015;165:255–269. doi: 10.1016/j.neucom.2015.03.015
  42. Tunali I, Hall LO, Napel S, et al. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Med Phys. 2019;46(11):5075–5085. doi: 10.1002/mp.13808
  43. Nasief H, Zheng C, Schott D, et al. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precis Oncol. 2019;3(1):25. doi: 10.1038/s41698-019-0096-z
  44. Cui Y, Li Y, Xing D, et al. Improving the prediction of benign or malignant breast masses using a combination of image biomarkers and clinical parameters. Front Oncol. 2021;11:629321. doi: 10.3389/fonc.2021.629321
  45. Mao N, Yin P, Wang Q, et al. Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol. 2019;16(4 Pt A):485–491. doi: 10.1016/j.jacr.2018.09.041
  46. Fanizzi A, TM, Losurdo L, et al. A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis. BMC Bioinformatics. 2020;21(Suppl 2):91. doi: 10.1186/s12859-020-3358-4
  47. Stelzer PD, Steding O, Raudner MW, et al. Combined texture analysis and machine learning in suspicious calcifications detected by mammography: Potential to avoid unnecessary stereotactical biopsies. Eur J Radiol. 2020;132:109309. doi: 10.1016/j.ejrad.2020.109309
  48. Karahaliou A, Skiadopoulos S, Boniatis I, et al. Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis. Br J Radiol. 2007;80(956):648–656. doi: 10.1259/bjr/30415751
  49. Li H, Mendel KR, Lan L, et al. Digital mammography in breast cancer: Additive value of radiomics of breast parenchyma. Radiology. 2019;291(1):15–20. doi: 10.1148/radiol.2019181113
  50. Parekh VS, Jacobs MA. MPRAD: A multiparametric radiomics framework. arXiv. 2018.
  51. Rozhkova NI, Bozhenko VK, Burdina II, et al. Radiogenomics of breast cancer as new vector of interdisciplinary integration of radiation and molecular biological technologies (literature review). Med Alph. 2020;(20):21–29. doi: 10.33667/2078-5631-2020-20-21-29
  52. Wang Z, Lin F, Ma H, et al. Contrast-Enhanced spectral mammography-based radiomics nomogram for the prediction of neoadjuvant chemotherapy-insensitive breast cancers. Front Oncol. 2021;11:605230. doi: 10.3389/fonc.2021.605230
  53. Zhang HX, Sun ZQ, Cheng YG, et al. A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer. J Xray Sci Technol. 2019;27(3):485–492. doi: 10.3233/XST-180488
  54. Morozov SP, Vladzimirsky AV, Klyashtornyy VG, et al. Clinical acceptance of software based on artificial intelligence technologies (Radiology). Moscow: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies; 2019. 45 р.
  55. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–1316. doi: 10.1097/JTO.0b013e3181ec173d
  56. Rahbar H, McDonald ES, Lee JM, et al. How can advanced imaging be used to mitigate potential breast cancer overdiagnosis? Academic Radiology. 2016;23(6):768–773. doi: 10.1016/j.acra.2016.02.008
  57. Pinker K. Beyond breast density: Radiomic phenotypes enhance assessment of breast cancer risk. Radiology. 2019;290(1):50–51. doi: 10.1148/radiol.2018182296
  58. Sun W, Tseng TL, Qian W, et al. Using multiscale texture and density features for near-term breast cancer risk analysis. Med Phys. 2015;42(6):2853–2862. doi: 10.1118/1.4919772
  59. Kontos D, Winham SJ, Oustimov А, et al. Radiomic phenotypes of mammographic parenchymal complexity: Toward augmenting breast density in breast cancer risk assessment. Radiology. 2019;290(1):41–49. doi: 10.1148/radiol.2018180179
  60. Yang J, Wang T, Yang L, et al. Preoperative prediction of axillary lymph node metastasis in breast cancer using mammography-based radiomics method. Sci Rep. 2019;9(1):4429. doi: 10.1038/s41598-019-40831-z
  61. Zhao B, Tan Y, Tsai WY, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6:23428. doi: 10.1038/srep23428
  62. Lu L, Ehmke RC, Schwartz LH, Zhao B. et al. Assessing agreement between radiomic features computed for multiple CT imaging settings. PLoS One. 2016;11(12):e0166550. doi: 10.1371/journal.pone.0166550
  63. Velazquez ER, Parmar C, Jermoumi M, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep. 2013;3:3529. doi: 10.1038/srep03529
  64. Qiu Q, Duan J, Gong G, et al. Reproducibility of radiomic features with GrowCut and GraphCut semiautomatic tumor segmentation in hepatocellular carcinoma. Transl Cancer Res. 2017;6(5). doi: 10.21037/tcr.2017.09.47
  65. Qiu Q, Duan J, Duan Z, et al. Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: Impact of tumor segmentation variability. Quant Imaging Med Surg. 2019;9(3):453–464. doi: 10.21037/qims.2019.03.02
  66. Hunter LA, Krafft S, Stingo F, et al. High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images. Med Phys. 2013;40(12):121916. doi: 10.1118/1.4829514
  67. O’Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14(3):169–186. doi: 10.1038/nrclinonc.2016.162
  68. Chalkidou A, O’Doherty M, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 2015;10(5):e0124165. doi: 10.1371/journal.pone.0124165
  69. Mann RM, Kuhl CK, Moy L. Contrast-enhanced MRI for breast cancer screening. J Magn Reson Imaging. 2019;50(2):377–390. doi: 10.1002/jmri.26654
  70. Parekh VS, Jacobs MA. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. NPJ Breast Cancer. 2017;3:43. doi: 10.1038/s41523-017-0045-3
  71. Whitney HM, Taylor NS, Drukker K, et al. Additive benefit of radiomics over size alone in the distinction between benign lesions and luminal a cancers on a large clinical breast MRI dataset. Acad Radiol. 2019;26(2):202–209. doi: 10.1016/j.acra.2018.04.019
  72. Crivelli P, Ledda RE, Parascandolo N, et al. A new challenge for radiologists: radiomics in breast cancer. BioMed Research International. 2018;2018:6120703. doi: 10.1155/2018/6120703
  73. Bickelhaupt S, Paech D, Kickingereder P, et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging. 2017;46(2):604–616. doi: 10.1002/jmri.25606
  74. Han L, Zhu Y, Liu Z, et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol. 2019;29(7):3820–3829. doi: 10.1007/s00330-018-5981-2
  75. Dong Y, Feng Q, Yang W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol. 2018;28(2):582–591. doi: 10.1007/s00330-017-5005-7
  76. Ma W, Ji Y, Qi L, et al. Breast cancer Ki67 expression prediction by DCE-MRI radiomics features. Clin Radiol. 2018;73(10):909.e1–909.e5. doi: 10.1016/j.crad.2018.05.027
  77. Jagadish K, Sheela GM, Naidu BP, et al. Big data analytics and radiomics to discover diagnostics and therapeutics for gastric cancer. In: Recent Advancements in Biomarkers and Early Detection of Gastrointestinal Cancers. 2020. Р. 213–219. doi: 10.1007/978-981-15-4431-6_12
  78. Scheckenbach K. Radiomics: Big data instead of biopsies in the future? Laryngorhinootologie. 2018;97(S 01):S114–S141. doi: 10.1055/s-0043-121964
  79. European Society of Radiology (ESR). Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights Imaging. 2015;6(2):141–155. doi: 10.1007/s13244-015-0394-0.

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2. 图 1图显示了放射组学的典型步骤。 获得医学图像后（1），它们被手动或自动分割（2）。 使用特殊软件或编程语言模块 (3) 从分割的感兴趣区域中提取一阶和更高阶的放射学特征。 接下来，对获得的纹理特征中最重要的进行分析和选择。最后阶段，基于分析的放射组学数据，构建分类或预测的各种临床和诊断模型（4）。

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3. 图 2一阶和二阶直方图特征的比较。 分割图像 (a) 的两个不同的原始 ROI 包含相同数量的浅灰色、深灰色和黑色像素。基于某些阴影的像素数（一阶直方图特征）的亮度直方图是相同的（b）。 这些特征不反映像素的相对位置。 邻接矩阵（二阶直方图特征）反映了图像的异质性（c）。 随后，数学算法根据所获得的强度直方图和灰度邻接矩阵计算一组放射组学特征以进行分析和建模。

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版权所有 © Govorukhina V.G., Semenov S.S., Gelezhe P.B., Didenko V.V., Morozov S.P., Andreychenko A.E., 2021

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