Literature DB >> 23040001

Magnetic resonance imaging features in triple-negative breast cancer: comparison with luminal and HER2-overexpressing tumors.

Melania Costantini1, Paolo Belli, Daniela Distefano, Enida Bufi, Marialuisa Di Matteo, Pierluigi Rinaldi, Michela Giuliani, Gianluigi Petrone, Stefano Magno, Lorenzo Bonomo.   

Abstract

BACKGROUND: It has been ascertained that triple-negative (TN) breast cancer is characterized by an aggressive clinical course and a poor prognosis. The purpose of our study was to compare the magnetic resonance imaging (MRI) features of the 3 major different breast cancer subtypes (TN, luminal, and human epidermal growth factor receptor 2 [HER2]-overexpressing) and to suggest the criteria that might predict TN phenotype.
MATERIALS AND METHODS: From October 2007 to April 2011, we studied 77 patients with histologically confirmed TN breast cancer who underwent breast MRI. We randomly included 148 patients with non-TN breast cancer (110 luminal and 38 HER-overexpressing) as a control group. We evaluated the clinicopathologic data, the MRI morphologic and kinetic features, the signal intensity on T2-weighted images, and the apparent diffusion coefficient (ADC).
RESULTS: Our results confirmed that TN tumors are more aggressive, are usually diagnosed at a younger age compared with the other study groups, and show benign morphologic features with MRI. Backward stepwise logistic regression identified some parameters as independent predictors of TN-type lesions: age, size, shape, presence of edema, and infiltrative characteristics. The receiver operating characteristic (ROC) curve, built with 4 of 5 these factors as criteria to predict TN status, showed a 0.664 area under the curve (AUC) value (sensitivity 58.4%, specificity 73.2%). The inclusion of the fifth criterion showed a 0.699 AUC value (sensitivity, 49.4%; specificity, 89.4%).
CONCLUSION: We identified the clinicoradiologic parameters that are independent predictors of TN breast lesions, which might be helpful for earlier prediction of the TN status of a breast lesion.
Copyright © 2012 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 23040001     DOI: 10.1016/j.clbc.2012.07.002

Source DB:  PubMed          Journal:  Clin Breast Cancer        ISSN: 1526-8209            Impact factor:   3.225


  21 in total

1.  Breast cancer molecular subtype classifier that incorporates MRI features.

Authors:  Elizabeth J Sutton; Brittany Z Dashevsky; Jung Hun Oh; Harini Veeraraghavan; Aditya P Apte; Sunitha B Thakur; Elizabeth A Morris; Joseph O Deasy
Journal:  J Magn Reson Imaging       Date:  2016-01-12       Impact factor: 4.813

2.  Correlation between electrical conductivity and apparent diffusion coefficient in breast cancer: effect of necrosis on magnetic resonance imaging.

Authors:  Soo-Yeon Kim; Jaewook Shin; Dong-Hyun Kim; Eun-Kyung Kim; Hee Jung Moon; Jung Hyun Yoon; Jai Kyung You; Min Jung Kim
Journal:  Eur Radiol       Date:  2018-03-06       Impact factor: 5.315

3.  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

4.  Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis.

Authors:  Hans-Jonas Meyer; Andreas Wienke; Alexey Surov
Journal:  Breast Care (Basel)       Date:  2021-02-23       Impact factor: 2.860

5.  Quantitative apparent diffusion coefficient measurement obtained by 3.0Tesla MRI as a potential noninvasive marker of tumor aggressiveness in breast cancer.

Authors:  Manuela Durando; Lucas Gennaro; Gene Y Cho; Dilip D Giri; Merlin M Gnanasigamani; Sujata Patil; Elizabeth J Sutton; Joseph O Deasy; Elizabeth A Morris; Sunitha B Thakur
Journal:  Eur J Radiol       Date:  2016-06-28       Impact factor: 3.528

6.  Heterogeneity of triple-negative breast cancer: mammographic, US, and MR imaging features according to androgen receptor expression.

Authors:  Min Sun Bae; So Yeon Park; Sung Eun Song; Won Hwa Kim; Su Hyun Lee; Wonshik Han; In-Ae Park; Dong-Young Noh; Woo Kyung Moon
Journal:  Eur Radiol       Date:  2014-09-16       Impact factor: 5.315

Review 7.  Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.

Authors:  Jia Wu; Aaron T Mayer; Ruijiang Li
Journal:  Semin Cancer Biol       Date:  2020-12-05       Impact factor: 17.012

8.  Correlation between apparent diffusion coefficients and HER2 status in gastric cancers: pilot study.

Authors:  Jian He; Hua Shi; Zhuping Zhou; Jun Chen; Wenxian Guan; Hao Wang; Haiping Yu; Song Liu; Zhengyang Zhou; Xiaofeng Yang; Tian Liu
Journal:  BMC Cancer       Date:  2015-10-20       Impact factor: 4.430

9.  Diffusion-Weighted Imaging of Breast Cancer: Correlation of the Apparent Diffusion Coefficient Value with Pathologic Prognostic Factors.

Authors:  Şehnaz Tezcan; Nihal Uslu; Funda Ulu Öztürk; Eda Yılmaz Akçay; Tugan Tezcaner
Journal:  Eur J Breast Health       Date:  2019-10-01

10.  Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype.

Authors:  Jose M Net; Gary J Whitman; Elizabteh Morris; Kathleen R Brandt; Elizabeth S Burnside; Maryellen L Giger; Marie Ganott; Elizabeth J Sutton; Margarita L Zuley; Arvind Rao
Journal:  Curr Probl Diagn Radiol       Date:  2018-08-23
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.