Literature DB >> 30150038

Imaging Phenotypes in Women at High Risk for Breast Cancer on Mammography, Ultrasound, and Magnetic Resonance Imaging Using the Fifth Edition of the Breast Imaging Reporting and Data System.

Maria Adele Marino1, Christopher C Riedl2, Maria Bernathova3, Clemens Bernhart4, Pascal A T Baltzer5, Thomas H Helbich6, Katja Pinker7.   

Abstract

OBJECTIVE: To assess imaging phenotypes of familial breast cancer on mammography (MG), ultrasound (US), and magnetic resonance imaging (MRI) using the fifth edition of the BI-RADS; to investigate inter-observer agreement and to correlate imaging phenotypes with risk status, histopathology, and molecular subtypes derived by immunohistochemical surrogate.
MATERIALS AND METHODS: Forty-nine women (BRCA-1/2 mutation carriers and women with >20% lifetime risk) were diagnosed with breast cancer within our high-risk screening program. BI-RADS MG, US, and MRI imaging descriptors were correlated with risk status, histopathology, and molecular subtypes derived by immunohistochemical surrogate. Inter-rater agreement for BI-RADS MG, US, and MRI categories was assessed.
RESULTS: Fifty-two breast cancers were diagnosed and 98% were detectable in at least one modality. MRI detected more cancers (P < 0.001). No lesion had benign morphology on BI-RADS. BRCA-1 had triple-negative and high-grade tumors in the posterior part and in the upper-outer quadrant (P ≤ 0.01); positive-family-history patients had intermediate-grade neoplasms (P < 0.01) in the middle part (P = 0.04) and in the upper-outer quadrants (P = 0.05). There was moderate inter-rater agreement for the assigned BI-RADS assessment for MG (k = 0.554) and MRI (k = 0.512) and substantial inter-rater agreement for US (k = 0.741).
CONCLUSIONS: Imaging phenotypes of familial breast cancers with BI-RADS are malignant in all imaging modalities. Risk status seems to influence cancer location.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  BRCA1 Protein; BRCA2 Protein; Breast Neoplasms; Magnetic resonance imaging; Mammography

Mesh:

Substances:

Year:  2018        PMID: 30150038     DOI: 10.1016/j.ejrad.2018.07.026

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  7 in total

1.  Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.

Authors:  Hao-Lin Yin; Yu Jiang; Zihan Xu; Hui-Hui Jia; Guang-Wu Lin
Journal:  J Cancer Res Clin Oncol       Date:  2022-06-30       Impact factor: 4.553

2.  Are Mutation Carrier Patients Different from Non-Carrier Patients? Genetic, Pathology, and US Features of Patients with Breast Cancer.

Authors:  Roxana Maria Pintican; Angelica Chiorean; Magdalena Duma; Diana Feier; Madalina Szep; Dan Eniu; Iulian Goidescu; Sorin Dudea
Journal:  Cancers (Basel)       Date:  2022-06-02       Impact factor: 6.575

3.  Diagnostic value of radiomics and machine learning with dynamic contrast-enhanced magnetic resonance imaging for patients with atypical ductal hyperplasia in predicting malignant upgrade.

Authors:  Roberto Lo Gullo; Kerri Vincenti; Carolina Rossi Saccarelli; Peter Gibbs; Michael J Fox; Isaac Daimiel; Danny F Martinez; Maxine S Jochelson; Elizabeth A Morris; Jeffrey S Reiner; Katja Pinker
Journal:  Breast Cancer Res Treat       Date:  2021-01-20       Impact factor: 4.872

4.  The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams.

Authors:  Ruxandra Iulia Milos; Francesca Pipan; Anastasia Kalovidouri; Paola Clauser; Panagiotis Kapetas; Maria Bernathova; Thomas H Helbich; Pascal A T Baltzer
Journal:  Eur Radiol       Date:  2020-06-06       Impact factor: 5.315

5.  A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value.

Authors:  Lingsong Meng; Xin Zhao; Lin Lu; Qingna Xing; Kaiyu Wang; Yafei Guo; Honglei Shang; Yan Chen; Mengyue Huang; Yongbing Sun; Xiaoan Zhang
Journal:  Front Oncol       Date:  2021-12-02       Impact factor: 6.244

6.  Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis.

Authors:  Isaac Daimiel Naranjo; Peter Gibbs; Jeffrey S Reiner; Roberto Lo Gullo; Caleb Sooknanan; Sunitha B Thakur; Maxine S Jochelson; Varadan Sevilimedu; Elizabeth A Morris; Pascal A T Baltzer; Thomas H Helbich; Katja Pinker
Journal:  Diagnostics (Basel)       Date:  2021-05-21

7.  Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results.

Authors:  Roberto Lo Gullo; Hannah Wen; Jeffrey S Reiner; Raza Hoda; Varadan Sevilimedu; Danny F Martinez; Sunitha B Thakur; Maxine S Jochelson; Peter Gibbs; Katja Pinker
Journal:  Cancers (Basel)       Date:  2021-12-14       Impact factor: 6.639

  7 in total

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