Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros











Base de dados
Intervalo de ano de publicação
1.
J Ultrasound Med ; 28(3): 285-91, 2009 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-19244063

RESUMO

OBJECTIVE: The purpose of this study was to describe a new reporting system called the Gynecologic Imaging Reporting and Data System (GI-RADS) for reporting findings in adnexal masses based on transvaginal sonography. METHODS: A total of 171 women (mean age, 39 years; range, 16-77 years) suspected of having an adnexal mass were evaluated by transvaginal sonography before treatment. Pattern recognition analysis and color Doppler blood flow location were used for determining the presumptive diagnosis. Then the GI-RADS was used, with the following classifications: GI-RADS 1, definitively benign; GI-RADS 2, very probably benign; GI-RADS 3, probably benign; GI-RADS 4, probably malignant; and GI-RADS 5, very probably malignant. Patients with GI-RADS 1 and 2 tumors were treated expectantly. All GI-RADS 3, 4, and 5 tumors were removed surgically, and a definitive histologic diagnosis was obtained. The GI-RADS classification was compared with final histologic diagnosis. RESULTS: A total of 187 masses were evaluated. The prevalence rate for malignant tumors was 13.4%. Overall GI-RADS classification rates were as follows: GI-RADS 1, 4 cases (2.1%); GI-RADS 2, 52 cases (27.8%); GI-RADS 3, 90 cases (48.1%); GI-RADS 4, 13 cases (7%); and GI-RADS 5, 28 cases (15%). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92%, 97%, 85%, 99%, and 96%, respectively. CONCLUSIONS: Our proposed reporting system showed good diagnostic performance. It is simple and could facilitate communication between sonographers/sonologists and clinicians.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Ginecologia/métodos , Interpretação de Imagem Assistida por Computador/métodos , Armazenamento e Recuperação da Informação/métodos , Neoplasias Ovarianas/diagnóstico por imagem , Reconhecimento Automatizado de Padrão/métodos , Sistemas de Informação em Radiologia , Adolescente , Adulto , Inteligência Artificial , Feminino , Humanos , Aumento da Imagem/métodos , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Ultrassonografia , Adulto Jovem
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA