{"doi":"10.1364/boe.423026","title":"Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography","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.","journal":"Biomedical Optics Express","year":2021,"id":163326,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":42,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8995,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":682355,"name":"Jianfeng Wang","orcid":"0000-0003-3358-181X","position":1,"is_corresponding":false},{"id":383804,"name":"Marina Marjanović","orcid":"0000-0002-1213-882X","position":2,"is_corresponding":false},{"id":417934,"name":"Eric J. Chaney","orcid":"0000-0002-0966-5450","position":3,"is_corresponding":false},{"id":682356,"name":"Kimberly A. Cradock","orcid":"0000-0002-7366-820X","position":4,"is_corresponding":false},{"id":682357,"name":"Anna M. Higham","orcid":"0000-0002-8938-9862","position":5,"is_corresponding":false},{"id":682972,"name":"Zheng G. Liu","orcid":null,"position":6,"is_corresponding":false},{"id":366687,"name":"Zhishan Gao","orcid":"0000-0002-9712-4696","position":7,"is_corresponding":false},{"id":366688,"name":"Stephen A. Boppart","orcid":"0000-0002-9386-5630","position":8,"is_corresponding":false},{"id":39350,"name":"Dan Zhu","orcid":"0000-0003-0485-9456","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-18T23:45:17.971865Z","pmid":"34168912","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}