Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.
Nat Med
; 26(1): 52-58, 2020 01.
Article
em En
| MEDLINE
| ID: mdl-31907460
ABSTRACT
Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise Espectral Raman
/
Sistemas Computacionais
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Neoplasias Encefálicas
/
Monitorização Intraoperatória
/
Redes Neurais de Computação
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Nat Med
Assunto da revista:
BIOLOGIA MOLECULAR
/
MEDICINA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos