Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.
Ultraschall Med
; 45(4): 412-417, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-38593859
ABSTRACT
PURPOSE:
To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. MATERIALS ANDMETHODS:
We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC).RESULTS:
The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001).CONCLUSION:
AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Neoplasias da Mama
/
Inteligência Artificial
/
Ultrassonografia Mamária
Limite:
Adult
/
Aged
/
Female
/
Humans
/
Middle aged
Idioma:
En
Revista:
Ultraschall Med
Ano de publicação:
2024
Tipo de documento:
Article