Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
09 06 2021
Historique:
received: 07 12 2020
accepted: 10 05 2021
entrez: 10 6 2021
pubmed: 11 6 2021
medline: 3 11 2021
Statut: epublish

Résumé

In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection.

Identifiants

pubmed: 34108566
doi: 10.1038/s41598-021-91648-8
pii: 10.1038/s41598-021-91648-8
pmc: PMC8190264
doi:

Types de publication

Clinical Trial Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

12162

Subventions

Organisme : NCI NIH HHS
ID : R01 CA227136
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS079697
Pays : United States
Organisme : NINDS NIH HHS
ID : 1K08NS110919-01
Pays : United States

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Auteurs

Melike Pekmezci (M)

Department of Pathology, University of California, San Francisco, CA, USA.

Ramin A Morshed (RA)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.

Pranathi Chunduru (P)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.

Balaji Pandian (B)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.
Invenio Imaging, Inc, Santa Clara, CA, USA.

Jacob Young (J)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.

Javier E Villanueva-Meyer (JE)

Department of Radiology, University of California, San Francisco, CA, USA.

Tarik Tihan (T)

Department of Pathology, University of California, San Francisco, CA, USA.

Emily A Sloan (EA)

Department of Pathology, University of California, San Francisco, CA, USA.

Manish K Aghi (MK)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.

Annette M Molinaro (AM)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.

Mitchel S Berger (MS)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA.

Shawn L Hervey-Jumper (SL)

Department of Neurological Surgery, University of California, 505 Parnassus Ave., Rm. M-779, San Francisco, CA, 94143-0112, USA. shawn.hervey-jumper@ucsf.edu.

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Classifications MeSH