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J Magn Reson Imaging ; 50(1): 230-238, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-30589146

RESUMO

BACKGROUND: The gamma distribution (GD) model is based on the statistical distribution of the apparent diffusion coefficient (ADC) parameter. The GD model is expected to reflect the probability of the distribution of water molecule mobility in different regions of tissue, but also the intra- and extracellular diffusion and perfusion components (f1 , f2 , f3 fractions). PURPOSE: To assess the GD model in the characterization and diagnostic performance of breast lesions. STUDY TYPE: Prospective. POPULATION: In all, 48 females with 24 benign and 33 malignant breast lesions. FIELD STRENGTH/SEQUENCE: A diffusion-weighted sequence (b = 0-3000 s/mm2 ) with a 3 T scanner. ASSESSMENT: For each group of benign, malignant, invasive, and in situ breast lesions, the ADC was obtained. Also, θ and k parameters (scale and shape of the statistic distribution, respectively), f1 , f2 , and f3 fractions were obtained from fitting the GD model to diffusion data. STATISTICAL TESTS: Lesion types were compared regarding diffusion parameters using nonparametric statistics and receiver operating characteristic curve diagnostic performance. RESULTS: The majority of GD parameters (k, f1 , f2 , f3 fractions) showed significant differences between benign and malignant lesions, and between in situ and invasive lesions (f1 , f2 , f3 fractions) (P ≤ 0.001). The best diagnostic performances were obtained with ADC and f1 fraction in benign vs. malignant lesions (area under curve [AUC] = 0.923 and 0.913, sensitivity = 93.9% and 81.8%, specificity = 79.2% and 91.7%, accuracy = 87.7% and 86.0%, respectively). In invasive lesions vs. in situ lesions, the best diagnostic performance was obtained with f1 fraction, which outperformed ADC results (AUC = 0.978 and 0.941, and sensitivity = 91.3% for both parameters, specificity = 100.0% and 90.0%, accuracy = 93.9% and 90.9%, respectively). DATA CONCLUSION: This work shows that the GD model provides information in addition to the ADC parameter, suggesting its potential in the diagnosis of breast lesions. Level of Evidence 2: Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:230-238.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética , Processamento de Imagem Assistida por Computador , Adulto , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , Diagnóstico por Computador , Feminino , Humanos , Pessoa de Meia-Idade , Modelos Estatísticos , Reconhecimento Automatizado de Padrão , Perfusão , Probabilidade , Estudos Prospectivos , Sensibilidade e Especificidade , Água
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