Literature DB >> 28885890

Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features.

Genevieve A Woodard1, Kimberly M Ray1, Bonnie N Joe1, Elissa R Price1.   

Abstract

Purpose To evaluate the association between Breast Imaging Reporting and Data System (BI-RADS) mammographic and magnetic resonance (MR) imaging features and breast cancer recurrence risk in patients with estrogen receptor-positive breast cancer who underwent the Oncotype DX assay. Materials and Methods In this institutional review board-approved and HIPAA-compliant protocol, 408 patients diagnosed with invasive breast cancer between 2004 and 2013 who underwent the Oncotype DX assay were identified. Mammographic and MR imaging features were retrospectively collected according to the BI-RADS lexicon. Linear regression assessed the association between imaging features and Oncotype DX test recurrence score (ODxRS), and post hoc pairwise comparisons assessed ODxRS means by using imaging features. Results Mammographic breast density was inversely associated with ODxRS (P ≤ .05). Average ODxRS for density category A was 24.4 and that for density category D was 16.5 (P < .02). Both indistinct mass margins and fine linear branching calcifications at mammography were significantly associated with higher ODxRS (P < .01 and P < .03, respectively). Masses with indistinct margins had an average ODxRS of 31.3, which significantly differed from the ODxRS of 18.5 for all other mass margins (P < .01). The average ODxRS for fine linear branching calcifications was 29.6, whereas the ODxRS for all other suspicious calcification morphologies was 19.4 (P < .03). Average ODxRS was significantly higher for irregular mass margins at MR imaging compared with spiculated mass margins (24.0 vs 17.6; P < .02). The presence of nonmass enhancement at MR imaging was associated with lower ODxRS than was its absence (16.4 vs 19.9; P < .05). Conclusion The BI-RADS features of mammographic breast density, calcification morphology, mass margins at mammography and MR imaging, and nonmass enhancement at MR imaging have the potential to serve as imaging biomarkers of breast cancer recurrence risk. Further prospective studies involving larger patient cohorts are needed to validate these preliminary findings. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28885890     DOI: 10.1148/radiol.2017162333

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  21 in total

1.  Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations.

Authors:  Natally Horvat; Harini Veeraraghavan; Raphael A Pelossof; Maria Clara Fernandes; Arshi Arora; Monika Khan; Michael Marco; Chin-Tung Cheng; Mithat Gonen; Jennifer S Golia Pernicka; Marc J Gollub; Julio Garcia-Aguillar; Iva Petkovska
Journal:  Eur J Radiol       Date:  2019-02-18       Impact factor: 3.528

Review 2.  Radiomics: an Introductory Guide to What It May Foretell.

Authors:  Stephanie Nougaret; Hichem Tibermacine; Marion Tardieu; Evis Sala
Journal:  Curr Oncol Rep       Date:  2019-06-25       Impact factor: 5.075

3.  Mammography radiomics features at diagnosis and progression-free survival among patients with breast cancer.

Authors:  Chuanxu Luo; Shuang Zhao; Cheng Peng; Chengshi Wang; Kejia Hu; Xiaorong Zhong; Ting Luo; Juan Huang; Donghao Lu
Journal:  Br J Cancer       Date:  2022-09-01       Impact factor: 9.075

Review 4.  The Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental Screening.

Authors:  Paula B Gordon
Journal:  Curr Oncol       Date:  2022-05-17       Impact factor: 3.109

Review 5.  Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review).

Authors:  Eleftherios Trivizakis; Georgios Z Papadakis; Ioannis Souglakos; Nikolaos Papanikolaou; Lefteris Koumakis; Demetrios A Spandidos; Aristidis Tsatsakis; Apostolos H Karantanas; Kostas Marias
Journal:  Int J Oncol       Date:  2020-05-11       Impact factor: 5.650

Review 6.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

7.  Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas.

Authors:  Xing Liu; Yiming Li; Zhiyan Sun; Shaowu Li; Kai Wang; Xing Fan; Yuqing Liu; Lei Wang; Yinyan Wang; Tao Jiang
Journal:  Cancer Med       Date:  2018-08-16       Impact factor: 4.452

Review 8.  Imaging and the completion of the omics paradigm in breast cancer.

Authors:  D Leithner; J V Horvat; R E Ochoa-Albiztegui; S Thakur; G Wengert; E A Morris; T H Helbich; K Pinker
Journal:  Radiologe       Date:  2018-11       Impact factor: 0.635

9.  Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer.

Authors:  Jiao Li; Weimei Ma; Xinhua Jiang; Chunyan Cui; Hongli Wang; Jiewen Chen; Runcong Nie; Yaopan Wu; Li Li
Journal:  J Cancer       Date:  2019-01-29       Impact factor: 4.207

10.  Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study.

Authors:  Yukiko Tokuda; Masahiro Yanagawa; Kaori Minamitani; Yasuto Naoi; Shinzaburo Noguchi; Noriyuki Tomiyama
Journal:  Medicine (Baltimore)       Date:  2020-04       Impact factor: 1.817

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