Literature DB >> 27039221

Three-Dimensional Quantitative Validation of Breast Magnetic Resonance Imaging Background Parenchymal Enhancement Assessments.

Richard Ha1, Eralda Mema2, Xiaotao Guo3, Victoria Mango2, Elise Desperito2, Jason Ha2, Ralph Wynn2, Binsheng Zhao3.   

Abstract

The magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and its clinical significance as a biomarker of breast cancer risk has been proposed based on qualitative studies. Previous BPE quantification studies lack appropriate correlation with BPE qualitative assessments. The purpose of this study is to validate our three-dimensional BPE quantification method with standardized BPE qualitative cases. An Institutional Review Board-approved study reviewed 500 consecutive magnetic resonance imaging cases (from January 2013-December 2014) using a strict inclusion criteria and 120 cases that best represented each of the BPE qualitative categories (minimal or mild or moderate or marked) were selected. Blinded to the qualitative data, fibroglandular tissue contours of precontrast and postcontrast images were delineated using an in-house, proprietary segmentation algorithm. Metrics of BPE were calculated including %BPE ([ratio of BPE volume to fibroglandular tissue volume] × 100) at multiple threshold levels to determine the optimal cutoff point for BPE quantification that best correlated with the reference BPE qualitative cases. The highest positive correlation was present at ×1.5 precontrast average signal intensity threshold level (r = 0.84, P < 0.001). At this level, the BPE qualitative assessment of minimal, mild, moderate, and marked correlated with the mean quantitative %BPE of 14.1% (95% CI: 10.9-17.2), 26.1% (95% CI: 22.8-29.3), 45.9% (95% CI: 40.2-51.7), and 74.0% (95% CI: 68.6-79.5), respectively. A one-way analysis of variance with post-hoc analysis showed that at ×1.5 precontrast average signal intensity level, the quantitative %BPE measurements best differentiated the four reference BPE qualitative groups (F [3,117] = 106.8, P < 0.001). Our three-dimensional BPE quantification methodology was validated using the reference BPE qualitative cases and could become an invaluable clinical tool to more accurately assess breast cancer risk and to test chemoprevention strategies.
Copyright © 2016 Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 27039221     DOI: 10.1067/j.cpradiol.2016.02.003

Source DB:  PubMed          Journal:  Curr Probl Diagn Radiol        ISSN: 0363-0188


  5 in total

1.  Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.

Authors:  Richard Ha; Peter Chang; Eralda Mema; Simukayi Mutasa; Jenika Karcich; Ralph T Wynn; Michael Z Liu; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-02       Impact factor: 4.056

Review 2.  Background parenchymal enhancement on breast MRI: A comprehensive review.

Authors:  Geraldine J Liao; Leah C Henze Bancroft; Roberta M Strigel; Rhea D Chitalia; Despina Kontos; Linda Moy; Savannah C Partridge; Habib Rahbar
Journal:  J Magn Reson Imaging       Date:  2019-04-19       Impact factor: 4.813

3.  Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist.

Authors:  Sarah Eskreis-Winkler; Elizabeth J Sutton; Donna D'Alessio; Katherine Gallagher; Nicole Saphier; Joseph Stember; Danny F Martinez; Elizabeth A Morris; Katja Pinker
Journal:  J Magn Reson Imaging       Date:  2022-02-15       Impact factor: 5.119

4.  The progesterone-receptor modulator, ulipristal acetate, drastically lowers breast cell proliferation.

Authors:  Carolyn L Westhoff; Hua Guo; Zhong Wang; Hanina Hibshoosh; Margaret Polaneczky; Malcolm C Pike; Richard Ha
Journal:  Breast Cancer Res Treat       Date:  2022-01-11       Impact factor: 4.624

5.  Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

Authors:  Laurent Dercle; Matthew Fronheiser; Lin Lu; Shuyan Du; Wendy Hayes; David K Leung; Amit Roy; Julia Wilkerson; Pingzhen Guo; Antonio T Fojo; Lawrence H Schwartz; Binsheng Zhao
Journal:  Clin Cancer Res       Date:  2020-03-20       Impact factor: 13.801

  5 in total

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