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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.

SUBMITTER: Hollon TC 

PROVIDER: S-EPMC6960329 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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

Hollon Todd C TC   Pandian Balaji B   Adapa Arjun R AR   Urias Esteban E   Save Akshay V AV   Khalsa Siri Sahib S SSS   Eichberg Daniel G DG   D'Amico Randy S RS   Farooq Zia U ZU   Lewis Spencer S   Petridis Petros D PD   Marie Tamara T   Shah Ashish H AH   Garton Hugh J L HJL   Maher Cormac O CO   Heth Jason A JA   McKean Erin L EL   Sullivan Stephen E SE   Hervey-Jumper Shawn L SL   Patil Parag G PG   Thompson B Gregory BG   Sagher Oren O   McKhann Guy M GM   Komotar Ricardo J RJ   Ivan Michael E ME   Snuderl Matija M   Otten Marc L ML   Johnson Timothy D TD   Sisti Michael B MB   Bruce Jeffrey N JN   Muraszko Karin M KM   Trautman Jay J   Freudiger Christian W CW   Canoll Peter P   Lee Honglak H   Camelo-Piragua Sandra S   Orringer Daniel A DA  

Nature medicine 20200106 1


Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery<sup>1</sup>. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive<sup>2,3</sup>. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce<sup>4</sup>. In the present study, we report a parallel workflow that combines stimulated R  ...[more]

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