Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography.
Journal
Biomedical optics express
ISSN: 2156-7085
Titre abrégé: Biomed Opt Express
Pays: United States
ID NLM: 101540630
Informations de publication
Date de publication:
01 May 2021
01 May 2021
Historique:
received:
22
02
2021
revised:
10
04
2021
accepted:
12
04
2021
entrez:
25
6
2021
pubmed:
26
6
2021
medline:
26
6
2021
Statut:
epublish
Résumé
We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT). A total of 720 PS-OCT images from 72 sites of 41 patients with H&E histology-confirmed diagnoses as the gold standard were employed in this study. The differentiation model is trained by the features extracted from both one standard OCT-based metric (i.e., intensity) and four PS-OCT-based metrics (i.e., phase difference between two channels (
Identifiants
pubmed: 34168912
doi: 10.1364/BOE.423026
pii: 423026
pmc: PMC8194620
doi:
Types de publication
Journal Article
Langues
eng
Pagination
3021-3036Informations de copyright
© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
Déclaration de conflit d'intérêts
S.A.B. is co-founder of Diagnostic Photonics, which is licensing intellectual property from the University of Illinois at Urbana-Champaign related to Interferometric Synthetic Aperture Microscopy for intraoperative imaging of cancer. All other authors declare that there are no conflicts of interest related to this article.
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