Literature DB >> 25758675

MRI phenotype of breast cancer: Kinetic assessment for molecular subtypes.

Eric Blaschke1, Hiroyuki Abe1.   

Abstract

PURPOSE: To evaluate the dynamic contrast-enhanced magnetic resonance imaging (MRI) kinetic characteristics of newly diagnosed breast cancer molecular subtypes.
MATERIALS AND METHODS: Breast MRI examinations of 112 patients with newly diagnosed breast cancer were reviewed. Cases of newly diagnosed invasive ductal carcinoma were sorted by molecular subtype (28 TN, 11 HER2 +, 73 Lum A/B) and MRI field strength, with lesion segmentation and kinetic analyses performed on a dedicated workstation. For kinetic assessment, 50% and 100% thresholds were employed for display of medium and rapid uptake. Kinetic profiles in terms of percent volume for six kinetic types (medium-persistent, medium-plateau, medium-washout, fast-persistent, fast-plateau, fast-washout) relative to the whole volume of the lesion were obtained. Statistical analysis of the kinetic profiles was performed using Welch's t-test.
RESULTS: Percent volume of HER2-positive lesions with >100% uptake at early phase on 3T strength MRI exams was significantly greater compared with luminal A/B (93.8 ± 0.92 vs. 77.3 ± 7.2; P < 0.01) and triple negative (93.8 ± 0.92 vs. 81.3 ± 8.2; P < 0.05) subtypes. The >50% early phase uptake for HER2+ lesions was also higher than Lum A/B (99.1 ± 0.73 vs. 93.6 ± 3.05; P < 0.01) at 3T. In the 1.5T subgroup the percent volume of HER2+ tumors with >50% and >100% early phase uptake trended higher than Lum A/B lesions without reaching significance.
CONCLUSION: The percent volume of HER2-positive tumors demonstrating rapid early contrast uptake is significantly increased compared to other molecular subtypes.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  HER2; breast cancer; dynamic contrast MRI; luminal A/B; triple negative

Mesh:

Substances:

Year:  2015        PMID: 25758675     DOI: 10.1002/jmri.24884

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  32 in total

1.  Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging.

Authors:  Tianwen Xie; Qiufeng Zhao; Caixia Fu; Qianming Bai; Xiaoyan Zhou; Lihua Li; Robert Grimm; Li Liu; Yajia Gu; Weijun Peng
Journal:  Eur Radiol       Date:  2018-11-06       Impact factor: 5.315

2.  Role of DCE-MR in predicting breast cancer subtypes.

Authors:  Marco Macchini; Martina Ponziani; Andrea Prochowski Iamurri; Mirco Pistelli; Mariagrazia De Lisa; Rossana Berardi; Gian Marco Giuseppetti
Journal:  Radiol Med       Date:  2018-06-05       Impact factor: 3.469

3.  Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results.

Authors:  Maria Adele Marino; Katja Pinker; Doris Leithner; Janice Sung; Daly Avendano; Elizabeth A Morris; Maxine Jochelson
Journal:  Mol Imaging Biol       Date:  2020-06       Impact factor: 3.488

4.  Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.

Authors:  Maciej A Mazurowski; Ashirbani Saha; Michael R Harowicz; Elizabeth Hope Cain; Jeffrey R Marks; P Kelly Marcom
Journal:  J Magn Reson Imaging       Date:  2019-01-22       Impact factor: 4.813

5.  A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.

Authors:  Ashirbani Saha; Michael R Harowicz; Weiyao Wang; Maciej A Mazurowski
Journal:  J Cancer Res Clin Oncol       Date:  2018-02-09       Impact factor: 4.553

6.  Deep learning for identifying radiogenomic associations in breast cancer.

Authors:  Zhe Zhu; Ehab Albadawy; Ashirbani Saha; Jun Zhang; Michael R Harowicz; Maciej A Mazurowski
Journal:  Comput Biol Med       Date:  2019-04-25       Impact factor: 4.589

7.  Role of MRI in the staging of breast cancer patients: does histological type and molecular subtype matter?

Authors:  Almir G V Bitencourt; Nara P Pereira; Luciana K L França; Caroline B Silva; Jociana Paludo; Hugo L S Paiva; Luciana Graziano; Camila S Guatelli; Juliana A Souza; Elvira F Marques
Journal:  Br J Radiol       Date:  2015-09-16       Impact factor: 3.039

Review 8.  Background, current role, and potential applications of radiogenomics.

Authors:  Katja Pinker; Fuki Shitano; Evis Sala; Richard K Do; Robert J Young; Andreas G Wibmer; Hedvig Hricak; Elizabeth J Sutton; Elizabeth A Morris
Journal:  J Magn Reson Imaging       Date:  2017-11-02       Impact factor: 4.813

9.  Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

Authors:  Richard Ha; Simukayi Mutasa; Jenika Karcich; Nishant Gupta; Eduardo Pascual Van Sant; John Nemer; Mary Sun; Peter Chang; Michael Z Liu; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

10.  Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.

Authors:  Elizabeth S Burnside; Karen Drukker; Hui Li; Ermelinda Bonaccio; Margarita Zuley; Marie Ganott; Jose M Net; Elizabeth J Sutton; Kathleen R Brandt; Gary J Whitman; Suzanne D Conzen; Li Lan; Yuan Ji; Yitan Zhu; Carl C Jaffe; Erich P Huang; John B Freymann; Justin S Kirby; Elizabeth A Morris; Maryellen L Giger
Journal:  Cancer       Date:  2015-11-30       Impact factor: 6.860

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