Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning.
Nat Biomed Eng
; 5(6): 522-532, 2021 06.
Article
em En
| MEDLINE
| ID: mdl-33875840
ABSTRACT
The clinical application of breast ultrasound for the assessment of cancer risk and of deep learning for the classification of breast-ultrasound images has been hindered by inter-grader variability and high false positive rates and by deep-learning models that do not follow Breast Imaging Reporting and Data System (BI-RADS) standards, lack explainability features and have not been tested prospectively. Here, we show that an explainable deep-learning system trained on 10,815 multimodal breast-ultrasound images of 721 biopsy-confirmed lesions from 634 patients across two hospitals and prospectively tested on 912 additional images of 152 lesions from 141 patients predicts BI-RADS scores for breast cancer as accurately as experienced radiologists, with areas under the receiver operating curve of 0.922 (95% confidence interval (CI) = 0.868-0.959) for bimodal images and 0.955 (95% CI = 0.909-0.982) for multimodal images. Multimodal multiview breast-ultrasound images augmented with heatmaps for malignancy risk predicted via deep learning may facilitate the adoption of ultrasound imaging in screening mammography workflows.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Mamografia
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Interpretação de Imagem Assistida por Computador
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Ultrassonografia
/
Aprendizado Profundo
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Etiology_studies
/
Guideline
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
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Female
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Humans
/
Middle aged
Idioma:
En
Revista:
Nat Biomed Eng
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos