The role of mammography in breast cancer radiomics

Cover Page

Cite item

Full Text

Abstract

Mammography is still the only screening method for breast cancer. Although digital mammography is the most common and widely used method for detecting breast cancer, it is ineffective at detecting and assessing intratumoral heterogeneity. Due to the small size of the tissue sample or tumor, biopsies often fail to represent the entire tumor. For this reason, selecting a treatment and determining a patient’s prognosis becomes difficult. In this case, medical imaging is a noninvasive approach that can provide a more comprehensive view of the entire tumor, act as a “virtual biopsy,” and be useful for monitoring disease progression and response to therapy.

Radiomics with texture analysis allows you to look at an image as a group of numerical data, moving beyond the usual visual perception and into a deeper analysis of digital, pixel data to improve the accuracy of differential diagnosis. Radiogenomics is a natural extension of radiomics that focuses on determining gene expression based on radiologic tumor phenotype. The purpose of this review is to evaluate the role of mammography in breast cancer radiomics and radiogenomics.

The article presents a literature review of relevant Russian scientific articles found in databases such as PubMed, Medline, Springer, eLibrary, and Google Scholar. The information obtained was then pooled, structured, and analyzed to examine the role of mammography in breast cancer screening radiomics.

About the authors

Veronika G. Govorukhina

Lomonosov Moscow State University

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

MD

Russian Federation, 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

Author for correspondence.
Email: s.semenov@npcmr.ru
ORCID iD: 0000-0003-2585-0864
SPIN-code: 4790-0416

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

Russian Federation, 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-code: 4841-3234

MD, Cand. Sci. (Med.)

Russian Federation, 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-code: 5033-8376

MD

Russian Federation, 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-code: 8542-1720

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

Russian Federation, 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-code: 6625-4186

Cand. Sci. (Phys.-Math.)

Russian Federation, Moscow

References

  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.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. The diagram illustrates the typical stages in radiomics. After obtaining medical images (1), they are manually or automatically segmented (2). Using special software or programming language modules, radiomic signs of the first and higher orders are extracted from segmented regions of interest (3). Next, the analysis and selection of the most significant textural signs obtained are carried out. Finally, based on the analyzed radiomic data, various clinical and diagnostic models of classification or prediction are constructed (4)

Download (167KB)
Indexing metadata
3. Fig. 2. Comparison of histogram signs of the first and second orders. The two different initial regions of interest of the segmented image (a) comprise an equal number of pixels in light gray, dark gray, and black shades. Brightness histograms based on the number of pixels of certain shades (histogram signs of the first order) are the same (b). These signs do not indicate the mutual arrangement of the pixels. Adjacency matrices (second-order histogram signs) reflect the heterogeneity of images (c). In the future, mathematical algorithms derived from the obtained histograms of intensities and adjacency matrices of the gray level will be used to calculate a variety of radiomic signs for analysis and modeling

Download (231KB)
Indexing metadata

Copyright (c) 2021 Govorukhina V.G., Semenov S.S., Gelezhe P.B., Didenko V.V., Morozov S.P., Andreychenko A.E.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies