Kiran S Yemul1, Adam M Zysk1, Andrea L Richardson2,3, Krishnarao V Tangella4, Lisa K Jacobs3. 1. 1 Diagnostic Photonics, Inc, Chicago, IL, USA. 2. 2 Sibley Memorial Hospital, Washington, DC, USA. 3. 3 Johns Hopkins Hospital, Baltimore, MD, USA. 4. 4 Christie Clinic, University of Illinois College of Medicine, Urbana-Champaign, IL, USA.
Abstract
PURPOSE: Initial studies have shown that optical coherence tomography (OCT) is an effective margin-evaluation tool for breast-conserving surgery, but methods for the interpretation of breast OCT images have not been directly studied. In this work, breast pathologies were assessed with a handheld OCT probe. OCT images and corresponding histology were used to develop guidelines for the identification of breast tissue features in OCT images. METHODS: Mastectomy and breast-conserving surgery specimens from 26 women were imaged with a handheld OCT probe. During standard pathology specimen dissection, representative 1-cm × 1-cm tissue regions were grossly identified, assessed with OCT, inked for orientation and image-matching purposes, and processed. Histology slides corresponding to the OCT image region were digitally photographed. OCT and histology images from the same region were paired by selecting the best structural matches. RESULTS: In total, 2880 OCT images were acquired from 26 breast specimens (from 26 patients) and 48 matching OCT-histology image pairs were identified. These matched image pairs illustrate tissue types including adipose tissue, dense fibrosis, fibroadipose tissue, blood vessels, regular and hyperplastic ducts and lobules, cysts, cyst, fibroadenoma, invasive ductal carcinoma, invasive lobular carcinoma, ductal carcinoma in situ, calcifications, and biopsy cavities. Differentiation between pathologies was achieved by considering feature boundaries, interior appearance, posterior shadowing or enhancement, and overall morphologic patterns. CONCLUSIONS: This is the first work to systematically catalog the critical features of breast OCT images. The results indicate that OCT can be used to identify and distinguish between benign and malignant features in human breast tissue.
PURPOSE: Initial studies have shown that optical coherence tomography (OCT) is an effective margin-evaluation tool for breast-conserving surgery, but methods for the interpretation of breast OCT images have not been directly studied. In this work, breast pathologies were assessed with a handheld OCT probe. OCT images and corresponding histology were used to develop guidelines for the identification of breast tissue features in OCT images. METHODS: Mastectomy and breast-conserving surgery specimens from 26 women were imaged with a handheld OCT probe. During standard pathology specimen dissection, representative 1-cm × 1-cm tissue regions were grossly identified, assessed with OCT, inked for orientation and image-matching purposes, and processed. Histology slides corresponding to the OCT image region were digitally photographed. OCT and histology images from the same region were paired by selecting the best structural matches. RESULTS: In total, 2880 OCT images were acquired from 26 breast specimens (from 26 patients) and 48 matching OCT-histology image pairs were identified. These matched image pairs illustrate tissue types including adipose tissue, dense fibrosis, fibroadipose tissue, blood vessels, regular and hyperplastic ducts and lobules, cysts, cyst, fibroadenoma, invasive ductal carcinoma, invasive lobular carcinoma, ductal carcinoma in situ, calcifications, and biopsy cavities. Differentiation between pathologies was achieved by considering feature boundaries, interior appearance, posterior shadowing or enhancement, and overall morphologic patterns. CONCLUSIONS: This is the first work to systematically catalog the critical features of breast OCT images. The results indicate that OCT can be used to identify and distinguish between benign and malignant features in human breast tissue.
Entities:
Keywords:
breast surgery; image-guided surgery; surgical oncology
Authors: Carolina Fabelo; Laura E Selmic; Pin-Cheh Huang; Jonathan P Samuelson; Jennifer K Reagan; Alexandra Kalamaras; Vincent Wavreille; Guillermo L Monroy; Marina Marjanovic; Stephen A Boppart Journal: Vet Comp Oncol Date: 2020-07-26 Impact factor: 2.613