Literature DB >> 26374470

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

Almir G V Bitencourt1, Nara P Pereira1, Luciana K L França1, Caroline B Silva1, Jociana Paludo1, Hugo L S Paiva1, Luciana Graziano1, Camila S Guatelli1, Juliana A Souza1, Elvira F Marques1.   

Abstract

OBJECTIVE: To assess the role of MRI in the pre-operative staging of patients with different histological types and molecular subtypes of breast cancer, by the assessment of the dimensions of the main tumour and identification of multifocal and/or multicentric disease.
METHODS: The study included 160 females diagnosed with breast cancer who underwent breast MRI for pre-operative staging. The size of the primary tumour evaluated by MRI was compared with the pathology (gold standard) using the Pearson's correlation coefficient (r). The presence of multifocal and/or multicentric disease was also evaluated.
RESULTS: The mean age of patients was 52.6 years (range 30-81 years). Correlation between the largest dimension of the main tumour measured by MRI and pathology was worse for non-special type/invasive ductal carcinoma than for other histological types and was better for luminal A and triple-negative than for luminal B and Her-2 molecular subtypes. Multifocal and/or multicentric disease was present in 48 patients (30.0%), and it was more common in breast carcinomas classified as Her-2 molecular subtype. There was no statistically significant difference in the frequency of multifocal and/or multicentric tumours identified only by MRI in relation to histological type or molecular subtype.
CONCLUSION: The results of this retrospective study demonstrated that histological types and molecular subtypes might influence the MRI assessment of breast cancers, especially in the evaluation of tumour size. ADVANCES IN KNOWLEDGE: The real benefit of MRI for treatment planning in patients with breast cancer may be different according to the histological type and molecular subtype.

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Year:  2015        PMID: 26374470      PMCID: PMC4743462          DOI: 10.1259/bjr.20150458

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  26 in total

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2.  MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

Authors:  Almir G V Bitencourt; Peter Gibbs; Carolina Rossi Saccarelli; Isaac Daimiel; Roberto Lo Gullo; Michael J Fox; Sunitha Thakur; Katja Pinker; Elizabeth A Morris; Monica Morrow; Maxine S Jochelson
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  5 in total

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