Top

Breast Cancer Research and Treatment

Published in:

01-06-2018 | Review

Rapid review: radiomics and breast cancer

Authors: Francesca Valdora, Nehmat Houssami, Federica Rossi, Massimo Calabrese, Alberto Stefano Tagliafico

Published in: Breast Cancer Research and Treatment | Issue 2/2018

Abstract

Purpose

To perform a rapid review of the recent literature on radiomics and breast cancer (BC).

Methods

A rapid review, a streamlined approach to systematically identify and summarize emerging studies was done (updated 27 September 2017). Clinical studies eligible for inclusion were those that evaluated BC using a radiomics approach and provided data on BC diagnosis (detection or characterization) or BC prognosis (response to therapy, morbidity, mortality), or provided data on technical challenges (software application: open source, repeatability of results). Descriptive statistics, results, and radiomics quality score (RQS) are presented.

Results

N = 17 retrospective studies, all published after 2015, provided BC-related radiomics data on 3928 patients evaluated with a radiomics approach. Most studies were done for diagnosis and/or characterization (65%, 11/17) or to aid in prognosis (41%, 7/17). The mean number of radiomics features considered was 100. Mean RQS score was 11.88 ± 5.8 (maximum value 36). The RQS criteria related to validation, gold standard, potential clinical utility, cost analysis, and open science data had the lowest scores. The majority of studies n = 16/17 (94%) provided correlation with histological outcomes and staging variables or biomarkers. Only 4/17 (23%) studies provided evidence of correlation with genomic data. Magnetic resonance imaging (MRI) was used in most studies n = 14/17 (82%); however, ultrasound (US), mammography, or positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose integrated with computed tomography (18F FDG PET/CT) was also used. Much heterogeneity was found for software usage.

Conclusions

The study of radiomics in BC patients is a new and emerging translational research topic. Radiomics in BC is frequently done to potentially improve diagnosis and characterization, mostly using MRI. Substantial quality limitations were found; high-quality prospective and reproducible studies are needed to further potential application.

Available only for authorised users

Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, Forman D, Bray F (2013) Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer 49:1374–1403. https://doi.org/10.1016/j.ejca.2012.12.027 CrossRefPubMed

Bland KI (2017) The breast: comprehensive management of benign and malignant disease. Saunders, Philadelphia

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Stiphout RV, Granton P et al (2012) Radiomics: extracting more information from medical images using advance feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentral

Li H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA et al (2016) Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer 2:16012CrossRefPubMedPubMedCentral

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMedPubMedCentral

Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 28:1191–1206. https://doi.org/10.1093/annonc/mdx034 CrossRefPubMed

Khangura S, Konnyu K, Cushman R, Grimshaw J, Moher D (2012) Evidence summaries: the evolution of a rapid review approach. Syst Rev 1:10CrossRefPubMedPubMedCentral

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169 CrossRefPubMed

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, van Timmeren J et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. https://doi.org/10.1038/nrclinonc.2017.141

10.

Guo Y, Hu Y, Qiao M, Wang Y, Yu J, Li J, Chang C (2017) Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma. Clin Breast Cancer. https://doi.org/10.1016/j.clbc.2017.08.002

11.

Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, Lian Z, Liu J, Luo X, Pei S, Mo X, Huang W, Liang C, Zhang B, Zhang S (2017) Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol. https://doi.org/10.1007/s00330-017-5005-7

12.

Antunovic L, Gallivanone F, Sollini M, Sagona A, Invento A, Manfrinato G, Kirienko M, Tinterri C, Chiti A, Castiglioni I (2017) [18F]FDG PET/CT features for the molecular characterization of primary breast tumors. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-017-3770-9

13.

Antropova N, Huynh BQ, Giger ML (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys. https://doi.org/10.1002/mp.12453 PubMed

14.

Chan HM, van der Velden BHM, Loo CE, Gilhuijs KGA (2017) Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI: a feasibility study. Phys Med Biol 62:6467–6485. https://doi.org/10.1088/1361-6560/aa7dc5 CrossRefPubMed

15.

Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 18(19):57. https://doi.org/10.1186/s13058-017-0846-1 CrossRef

16.

Ha S, Park S, Bang JI, Kim EK, Lee HY (2017) Metabolic radiomics for pretreatment 18F-FDG PET/CT to characterize locally advanced breast cancer: histopathologic characteristics, response to neoadjuvant chemotherapy, and prognosis. Sci Rep 7:1556. https://doi.org/10.1038/s41598-017-01524-7 CrossRefPubMedPubMedCentral

17.

Bickelhaupt S, Paech D, Kickingereder P, Steudle Lederer W, Daniel H, Götz M, Gählert N, Tichy D, Wiesenfarth M, Laun FB, Maier-Hein KH, Schlemmer HP, Bonekamp D (2017) 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 46:604–616. https://doi.org/10.1002/jmri.25606 CrossRefPubMed

18.

Prasanna Prateek, Tiwari Pallavi, Madabhushi Anant (2016) Co-occurrence of local anisotropic gradient orientations (CoLlAGe): a new radiomics descriptor. Sci Rep 6:37241. https://doi.org/10.1038/srep37241 CrossRefPubMedPubMedCentral

19.

Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML (2016) MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology 281:382–391CrossRefPubMedPubMedCentral

20.

Obeid JP, Stoyanova R, Kwon D, Patel M, Padgett K, Slingerland J, Takita C, Alperin N, Yepes M (2017) Zeidan YH (2017) Multiparametric evaluation of preoperative MRI in early stage breast cancer: prognostic impact of peri-tumoral fat. Clin Transl Oncol 19:211–218. https://doi.org/10.1007/s12094-016-1526-9 CrossRefPubMed

21.

Fan M, Wu G, Cheng H, Zhang J, Shao G, Li L (2017) Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients. Eur J Radiol 94:140–147. https://doi.org/10.1016/j.ejrad.2017.06.019 CrossRefPubMed

22.

Fan M, Li H, Wang S, Zheng B, Zhang J, Li L (2017) Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS ONE 12(2):e0171683. https://doi.org/10.1371/journal.pone.0171683 CrossRefPubMedPubMedCentral

23.

Zhu Y, Li H, Guo W, Drukker K, Lan L, Giger ML, Ji Y (2015) Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma. Sci Rep 7(5):17787. https://doi.org/10.1038/srep17787

24.

Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, Morris E, Burnside E, Whitman G, Giger ML, Ji Y, Tcga Breast Phenotype Research Group8 (2015) Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging. 2:041007. https://doi.org/10.1117/1.jmi.2.4.041007 CrossRef

25.

Wang J, Kato F, Oyama-Manabe N, Li R, Cui Y, Tha KK, Yamashita H, Kudo K, Shirato H (2015) Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: a pilot radiomics study. PLoS ONE 10(11):e0143308. https://doi.org/10.1371/journal.pone.0143308 CrossRefPubMedPubMedCentral

26.

Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A (2017) Erratum to: intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19(1):80. https://doi.org/10.1186/s13058-017-0862-1 CrossRefPubMedPubMedCentral

27.

Marin Z, Batchelder KA, Toner BC, Guimond L, Gerasimova-Chechkina E, Harrow AR, Arneodo A, Khalil A (2017) Mammographic evidence of microenvironment changes in tumorous breasts. Med Phys 44:1324–1336. https://doi.org/10.1002/mp.12120 CrossRefPubMed

28.

Parekh V, Jacobs MA (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 1:207–226. https://doi.org/10.1080/23808993.2016.1164013 CrossRefPubMedPubMedCentral

29.

Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423CrossRef

30.

Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621CrossRef

31.

Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4:172–179CrossRef

32.

Napel S, Giger M (2015) Radiomics and imaging genomics: quantitative imaging for precision medicine. J Med Imaging 2:041001. https://doi.org/10.1117/1.JMI.2.4.041001 CrossRef

33.

Jaffe C (2012) Imaging and genomics: is there a synergy? Radiology 264:329–331CrossRefPubMed

34.

http://www.radiomics.world/. Accessed 18 May 2017

35.

Collins G, Reitsma J, Altman D et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. Ann Intern Med 162:55–63CrossRefPubMed

36.

Houssami N, Lee CI, Buist DSM, Tao D (2017) Artificial intelligence for breast cancer screening: opportunity or hype? Breast 36:31–33. https://doi.org/10.1016/j.breast.2017.09.003 CrossRefPubMed

37.

Parekh VS, Jacobs MA (2017) Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. NPJ Breast Cancer 3:43. https://doi.org/10.1038/s41523-017-0045-3 CrossRefPubMedPubMedCentral

38.

Papp L, Poetsch N, Grahovac M et al (2017) Glioma survival prediction with the combined analysis of in vivo 11C-MET-PET, ex vivo and patient features by supervised machine learning. J Nucl Med. https://doi.org/10.2967/jnumed.117.202267 PubMed

39.

Garapati SS, Hadjiiski L, Cha KH et al (2017) Urinary bladder cancer staging in CT urography using machine learning. Med Phys 44:5814–5823. https://doi.org/10.1002/mp.12510 CrossRefPubMed

40.

Zhang B, He X, Ouyang F et al (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 403:21–27. https://doi.org/10.1016/j.canlet.2017.06.004 CrossRefPubMed

41.

Rahbar H, McDonald ES, Lee JM, Partridge SC, Lee CI (2016) How can advanced imaging be used to mitigate potential breast cancer overdiagnosis? Acad Radiol. 23:768–773. https://doi.org/10.1016/j.acra.2016.02.008 CrossRefPubMedPubMedCentral

Title: Rapid review: radiomics and breast cancer
Authors: Francesca Valdora
Nehmat Houssami
Federica Rossi
Massimo Calabrese
Alberto Stefano Tagliafico
Publication date: 01-06-2018
Publisher: Springer US
Published in: Breast Cancer Research and Treatment / Issue 2/2018
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI: https://doi.org/10.1007/s10549-018-4675-4

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 2/2018

Intrathecal trastuzumab in the management of HER2+ breast leptomeningeal disease: a single institution experience

The heterogeneity of cancer

A novel patient-derived xenograft model for claudin-low triple-negative breast cancer

Cross-reactivity between MUC1 antigen and MCA: false elevation of serum CA 15-3 level in pregnant and lactating women by Ma695–Ma552-based assay

The frequency of missed breast cancers in women participating in a high-risk MRI screening program

Systematical analysis of lncRNA–mRNA competing endogenous RNA network in breast cancer subtypes

Keynote webinar | Spotlight on antibody–drug conjugates in cancer