Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography

Dan Zhu; Jianfeng Wang; Marina Marjanovic; Eric J Chaney; Kimberly A Cradock; Anna M Higham; Zheng G Liu; Zhishan Gao; Stephen A Boppart

doi:10.1364/BOE.423026

Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography

Biomed Opt Express. 2021 Apr 29;12(5):3021-3036. doi: 10.1364/BOE.423026. eCollection 2021 May 1.

Authors

Dan Zhu^{1

2

3}, Jianfeng Wang^{1

3}, Marina Marjanovic^{1

4}, Eric J Chaney¹, Kimberly A Cradock^{5

6}, Anna M Higham^{5

6}, Zheng G Liu^{6

7}, Zhishan Gao², Stephen A Boppart^{1

4

6

8

9}

Affiliations

¹ Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
² School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
³ These authors contributed equally to this work.
⁴ Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
⁵ Department of Surgery, Carle Foundation Hospital, Urbana, Illinois 61801, USA.
⁶ Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, USA.
⁷ Department of Pathology, Carle Foundation Hospital, Urbana, Illinois 61801, USA.
⁸ Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
⁹ Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

Abstract

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 (PD), phase retardation (PR), local phase retardation (LPR), and degree of polarization uniformity (DOPU)). Further optimized by forward searching and validated by leave-one-site-out-cross-validation (LOSOCV) method, the best feature subset was acquired with the highest overall accuracy of 93.5% for the model. Furthermore, to show the superiority of our differentiation model based on PS-OCT images over standard OCT images, the best model trained by intensity-only features (usually obtained by standard OCT systems) was also obtained with an overall accuracy of 82.9%, demonstrating the significance of the polarization information in breast tissue differentiation. The high performance of our differentiation model suggests the potential of using PS-OCT for intraoperative human breast tissue differentiation during the surgical resection of breast cancer.