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Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

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.

journal_name

Nat Med

journal_title

Nature medicine

authors

Hollon TC,Pandian B,Adapa AR,Urias E,Save AV,Khalsa SSS,Eichberg DG,D'Amico RS,Farooq ZU,Lewis S,Petridis PD,Marie T,Shah AH,Garton HJL,Maher CO,Heth JA,McKean EL,Sullivan SE,Hervey-Jumper SL,Patil PG,Thompson BG

doi

10.1038/s41591-019-0715-9

subject

Has Abstract

pub_date

2020-01-01 00:00:00

pages

52-58

issue

1

eissn

1078-8956

issn

1546-170X

pii

10.1038/s41591-019-0715-9

journal_volume

26

pub_type

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