Perfusion MR Imaging of Breast Cancer: Insights Using “Habitat Imaging”

医学 乳腺癌 梅德林 癌症 肿瘤科 内科学 生物 生物化学
作者
Robert J. Gillies,Yoganand Balagurunathan
出处
期刊:Radiology 卷期号:288 (1): 36-37 被引量:11
标识
DOI:10.1148/radiol.2018180271
摘要

HomeRadiologyVol. 288, No. 1 PreviousNext Reviews and CommentaryEditorialPerfusion MR Imaging of Breast Cancer: Insights Using “Habitat Imaging”Robert J. Gillies , Yoganand BalagurunathanRobert J. Gillies , Yoganand BalagurunathanAuthor AffiliationsFrom the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia St, SRB-4, Tampa, FL 33612.Address correspondence to R.J.G. (e-mail: [email protected]).Robert J. Gillies Yoganand BalagurunathanPublished Online:May 1 2018https://doi.org/10.1148/radiol.2018180271MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Hanahan D, Weinberg RA, Hallmarks of cancer: the next generation, Cell 2011; 144(5):646–674. Crossref, Medline, Google Scholar2. Reuben A, Spencer CN, Prieto PA, et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. NPJ Genom Med 2017;2. 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Defining the biological basis of radiomic phenotypes in lung cancer. eLife 2017;6:e23421. Crossref, Medline, Google Scholar9. Esserman LJ, Berry DA, DeMichele A, et al. Pathologic complete response predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL—CALGB 150007/150012, ACRIN 6657. J Clin Oncol 2012;30(26):3242–3249. Crossref, Medline, Google Scholar10. Lloyd MC, Cunningham JJ, Bui MM, Gillies RJ, Brown JS, Gatenby RA. Darwinian dynamics of intratumoral heterogeneity: not solely random mutations but also variable environmental selection forces. Cancer Res 2016;76(11):3136–3144. Crossref, Medline, Google Scholar11. Ong MBH. “We’re gonna free the data,” Sharpless pledges at NCI Town Hall. Cancer Lett 2017;43(46):4–16. Google ScholarArticle HistoryReceived January 31, 2018; revision requested February 5; final version accepted February 6.Published online: May 01 2018Published in print: July 2018 FiguresReferencesRelatedDetailsCited ByDiffusion MRI of the BreastRitseMann2023Breast MRI: Where are we currently standing?HaralabosBougias, NikolaosStogiannos2022 | Journal of Medical Imaging and Radiation Sciences, Vol. 53, No. 2High-dimensional role of AI and machine learning in cancer researchEnricoCapobianco2022 | British Journal of Cancer, Vol. 126, No. 4Habitat Analysis of Breast Cancer-Enhanced MRI Reflects BRCA1 Mutation Determined by ImmunohistochemistryTianmingDu, HaidongZhao, ChenLi2022 | BioMed Research International, Vol. 2022The potential of predictive and prognostic breast MRI (P2-bMRI)MatthiasDietzel, Rubina ManuelaTrimboli, MorenoZanardo, RüdigerSchultz-Wendtland, MichaelUder, PaolaClauser, FrancescoSardanelli, Pascal A. T.Baltzer2022 | European Radiology Experimental, Vol. 6, No. 1Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine LearningVishwa S.Parekh, Jay J.Pillai, Katarzyna J.Macura, Peter S.LaViolette, Michael A.Jacobs2022 | Cancers, Vol. 14, No. 6Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community settingAngela M.Jarrett, Anum S.Kazerouni, ChengyueWu, JohnVirostko, Anna G.Sorace, Julie C.DiCarlo, David A.Hormuth, David A.Ekrut, DebraPatt, BooneGoodgame, SarahAvery, Thomas E.Yankeelov2021 | Nature Protocols, Vol. 16, No. 11How to develop a meaningful radiomic signature for clinical use in oncologic patientsNikolaosPapanikolaou, CelsoMatos, Dow MuKoh2020 | Cancer Imaging, Vol. 20, No. 1Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse ModelsBruna V.Jardim-Perassi, SuningHuang, WilliamDominguez-Viqueira, JanPoleszczuk, Mikalai M.Budzevich, Mahmoud A.Abdalah, Smitha R.Pillai, EpifanioRuiz, Marilyn M.Bui, Debora A.P.C.Zuccari, Robert J.Gillies, Gary V.Martinez2019 | Cancer Research, Vol. 79, No. 15Accompanying This ArticleIntratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant ChemotherapyMay 1 2018RadiologyRecommended Articles Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant ChemotherapyRadiology2018Volume: 288Issue: 1pp. 26-35Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor–Positive Breast Cancer: A Multicohort StudyRadiology2021Volume: 302Issue: 3pp. 516-524Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival OutcomesRadiology2016Volume: 282Issue: 3pp. 665-675Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast CancerRadiology2017Volume: 285Issue: 2pp. 401-413Pretreatment MR Imaging Features of Triple-Negative Breast Cancer: Association with Response to Neoadjuvant Chemotherapy and Recurrence-Free SurvivalRadiology2016Volume: 281Issue: 2pp. 392-400See More RSNA Education Exhibits Breast Cancer Recurrence: How To Predict The Predictable?Digital Posters2021High Risk Breast Cancer Screening  Digital Posters2020Diffusion-Weighted Imaging (DWI) and ADC Features of Triple Negative Breast Cancer (TNBC) Pre and Post Neoadjuvant ChemotherapyDigital Posters2019 RSNA Case Collection Malignancy on abbreviated screening breast MRIRSNA Case Collection2020Male invasive Ductal Carcinoma RSNA Case Collection2022Inflammatory breast cancerRSNA Case Collection2020 Vol. 288, No. 1 Metrics Altmetric Score PDF download
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