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Predictive performance of BI-RADS magnetic resonance imaging
descriptors in the context of suspicious (category 4) findings
Autores: João Ricardo Maltez de Almeida, André Boechat Gomes, Thomas Pitangueiras Barros, Paulo Eduardo Fahel, Mário de Seixas Rocha
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Nos últimos anos, com o desenvolvimento de sequências ultrarrápidas, a ressonância magnética (RM) tem-se estabelecido como uma
ferramenta de diagnóstico por imagem de grande valor. Em virtude dos aperfeiçoamentos na velocidade de aquisição e na qualidade das
imagens, a RM é atualmente um método apropriado também para o estudo de doenças pulmonares. A principal vantagem da RM é sua
combinação única que permite avaliação morfológica e funcional em um mesmo exame de imagem. Neste artigo iremos revisar aspectos
técnicos e sugerir um protocolo para a realização de RM do tórax. Também serão descritas as três maiores indicações de RM do tórax:
estadiamento para neoplasia pulmonar, avaliação de doença vascular do pulmão e investigação de doenças pulmonares em pacientes
que não devem ser expostos à radiação ionizante.
Unitermos: Ressonância magnética; Pulmão; Tórax; Protocolo; Sequências.
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Objective: To determine the positive predictive value (PPV) and likelihood ratio for magnetic resonance imaging (MRI) characteristics of category 4 lesions, as described in the Breast Imaging Reporting and Data System (BI-RADS®) lexicon, as well as to test the predictive
performance of the descriptors using multivariate analysis and the area under the curve derived from a receiver operating characteristic
(ROC) curve.
Materials and Methods: This was a double-blind review study of 121 suspicious findings from 98 women examined between 2009 and
2013. The terminology was based on the 2013 edition of the BI-RADS.
Results: Of the 121 suspicious findings, 53 (43.8%) were proven to be malignant lesions, with no significant difference between mass
and non-mass enhancement (p = 0.846). The PPVs were highest for masses with a spiculated margin (71%) and round shape (63%),
whereas segmental distribution achieved a high PPV (80%) for non-mass enhancement. Kinetic analyses performed poorly, except for
type 3 curves applied to masses (PPV of 73%). Logistic regression models were significant for both patterns, although the results were
better for masses, particularly when kinetic assessments were included (p = 0.015; pseudo R2
= 0.48; area under the curve = 90%).
Conclusion: Some BI-RADS MRI descriptors have high PPV and good predictive performance—as demonstrated by ROC curve and
multivariate analysis—when applied to BI-RADS category 4 findings. This may allow future stratification of this category.
Keywords: Magnetic resonance imaging; Breast neoplasms; Predictive value of tests; Likelihood functions.
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