Your browser doesn't support javascript.
loading
Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography
Korean Journal of Radiology ; : 749-758, 2019.
Artigo em Inglês | WPRIM | ID: wpr-741457
ABSTRACT

OBJECTIVE:

To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). MATERIALS AND

METHODS:

B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared.

RESULTS:

When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD 72.8–92.5% vs. 82.1–93.1%; p < 0.001), accuracy (77.9–88.9% vs. 86.2–90.9%; p = 0.038), and positive predictive value (PPV) (60.2–83.3% vs. 70.4–85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3–88.8% vs. 86.3–95.0%; p = 0.120) and negative predictive value (91.4–93.5% vs. 92.9–97.3%; p = 0.259).

CONCLUSION:

Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.
Assuntos

Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Assunto principal: Mama / Sistemas de Informação / Estudos Prospectivos / Estudos Retrospectivos / Ultrassonografia Mamária / Ultrassonografia / Sensibilidade e Especificidade / Diagnóstico / Conjunto de Dados / Aprendizagem Tipo de estudo: Estudo diagnóstico / Estudo observacional / Estudo prognóstico Limite: Humanos Idioma: Inglês Revista: Korean Journal of Radiology Ano de publicação: 2019 Tipo de documento: Artigo

Similares

MEDLINE

...
LILACS

LIS

Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Assunto principal: Mama / Sistemas de Informação / Estudos Prospectivos / Estudos Retrospectivos / Ultrassonografia Mamária / Ultrassonografia / Sensibilidade e Especificidade / Diagnóstico / Conjunto de Dados / Aprendizagem Tipo de estudo: Estudo diagnóstico / Estudo observacional / Estudo prognóstico Limite: Humanos Idioma: Inglês Revista: Korean Journal of Radiology Ano de publicação: 2019 Tipo de documento: Artigo