Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

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Abstract

Intraoperative histology is essential for surgical guidance and decision-making. However, frozen-sectioned hematoxylin and eosin (H&E) staining suffers from degraded accuracy, whereas the gold-standard formalin-fixed and paraffin-embedded (FFPE) H&E is too lengthy for intraoperative use. Stimulated Raman scattering (SRS) microscopy has shown rapid histology of brain tissue with lipid/protein contrast but is challenging to yield images identical to nucleic acid–/protein-based FFPE stains interpretable to pathologists. Here, we report the development of a semi-supervised stimulated Raman CycleGAN model to convert fresh-tissue SRS images to H&E stains using unpaired training data. Within 3 minutes, stimulated Raman virtual histology (SRVH) results that matched perfectly with true H&E could be generated. A blind validation indicated that board-certified neuropathologists are able to differentiate histologic subtypes of human glioma on SRVH but hardly on conventional SRS images. SRVH may provide intraoperative diagnosis superior to frozen H&E in both speed and accuracy, extendable to other types of solid tumors.

Abstract

Label-free and slide-free virtual histology of brain tissue by stimulated Raman scattering achieves gold-standard H&E quality.

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Most cited references 37

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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Navneet Narula, Theodore Sakellaropoulos, Paolo Ocampo … (2018)

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

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Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy.

Christian W. Freudiger, Wei Min, Brian Saar … (2008)

Label-free chemical contrast is highly desirable in biomedical imaging. Spontaneous Raman microscopy provides specific vibrational signatures of chemical bonds, but is often hindered by low sensitivity. Here we report a three-dimensional multiphoton vibrational imaging technique based on stimulated Raman scattering (SRS). The sensitivity of SRS imaging is significantly greater than that of spontaneous Raman microscopy, which is achieved by implementing high-frequency (megahertz) phase-sensitive detection. SRS microscopy has a major advantage over previous coherent Raman techniques in that it offers background-free and readily interpretable chemical contrast. We show a variety of biomedical applications, such as differentiating distributions of omega-3 fatty acids and saturated lipids in living cells, imaging of brain and skin tissues based on intrinsic lipid contrast, and monitoring drug delivery through the epidermis.

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Generative Adversarial Networks: An Overview

Antonia Creswell, Tom White, Vincent Dumoulin … (2018)

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Author and article information

Contributors

Zhijie Liu:

ORCID: https://orcid.org/0000-0002-9775-9026

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Lingchao Chen: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Haixia Cheng:

ORCID: https://orcid.org/0000-0002-1601-8673

Role: ConceptualizationRole: Formal analysisRole: ResourcesRole: Validation

Jianpeng Ao: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing - original draft

Ji Xiong: Role: Investigation

Xing Liu:

ORCID: https://orcid.org/0000-0002-5786-2838

Role: Validation

Yaxin Chen:

ORCID: https://orcid.org/0000-0003-1470-4001

Role: InvestigationRole: VisualizationRole: Writing - review & editing

Ying Mao:

ORCID: https://orcid.org/0000-0001-8055-115X

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Minbiao Ji:

ORCID: https://orcid.org/0000-0002-9066-4008

Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: 29 March 2024

Publication date (Electronic, pub): 27 March 2024

Volume: 10

Issue: 13

Electronic Location Identifier: eadn3426

Affiliations

[ ¹ ]State Key Laboratory of Surface Physics and Department of Physics, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Human Phenome Institute, Fudan University, Shanghai 200433, China.

[ ² ]Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China.

[ ³ ]National Center for Neurological Disorders, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200040, China.

[ ⁴ ]Department of Pathology, Huashan Hospital, Fudan University, Shanghai 200040, China.

[ ⁵ ]Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China.

Author notes

[* ]Corresponding author. Email: minbiaoj@ 123456fudan.edu.cn

[†]

These authors contributed equally to this work.

[‡]

Present address: Department of Electrical and Computer Engineering, Boston University, Boston, MA 02155, USA.

Author information

Zhijie Liu https://orcid.org/0000-0002-9775-9026

Haixia Cheng https://orcid.org/0000-0002-1601-8673

Xing Liu https://orcid.org/0000-0002-5786-2838

Yaxin Chen https://orcid.org/0000-0003-1470-4001

Ying Mao https://orcid.org/0000-0001-8055-115X

Minbiao Ji https://orcid.org/0000-0002-9066-4008

Article

Publisher ID: adn3426

DOI: 10.1126/sciadv.adn3426

PMC ID: 10971418

PubMed ID: 38536925

SO-VID: 80f996ae-0ec0-41f3-a84d-9436771bd62f

Copyright © Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

History

Date received : 05 December 2023

Date accepted : 22 February 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100018904, Beijing Xisike Clinical Oncology Research Foundation;

Award ID: Y-zai2021/qn-0204

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 61975033

Funded by: National key R&D program of China;

Award ID: 2021YFF0502900

Funded by: Municipal Natural Science Foundation of Shanghai;

Award ID: 23dz2260100

Funded by: Shanghai Municipal Science and Technology Project;

Award ID: 22Y11907500

Funded by: Shanghai Municipal Science and Technology Major Project;

Award ID: 2018SHZDZX01

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Copyeditor Vivian Hernandez

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Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

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2021 Research and Innovation Open Distance and eLearning Virtual Conference

Most cited references 37

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy.

Generative Adversarial Networks: An Overview

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Cited by 3

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