Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.
EBioMedicine
; 56: 102777, 2020 Jun.
Article
em En
| MEDLINE
| ID: mdl-32485640
ABSTRACT
BACKGROUND:
The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. MATERIALS ANDMETHODS:
This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 41 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively.FINDINGS:
The AUC of ModelLBC for FLLs was 0.924 (95% CI 0.889-0.959) in the EV cohort. The diagnostic sensitivity and specificity of ModelLBC were superior to 15-year skilled radiologists (86.5% vs 76.1%, p = 0.0084 and 85.5% vs 76.9%, p = 0.0051, respectively). Accuracy of ModelLBC was comparable to that of contrast enhanced CT (both 84.7%) but inferior to contrast enhanced MRI (87.9%) for lesions detected by US.INTERPRETATION:
DCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Interpretação de Imagem Radiográfica Assistida por Computador
/
Neoplasias Hepáticas
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article