ArticleBreast cancer research : BCR2015
Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues.
Article in Breast cancer research : BCR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Entropy-Regularized Attention for Explainable Histological Classification with Convolutional and Hybrid Models.Entropy (Basel, Switzerland) · 2025Article
- Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries.BME frontiers · 2023Article
- Evaluation of an Integrated Spectroscopy and Classification Platform for Point-of-Care Core Needle Biopsy Assessment: Performance Characteristics from Ex Vivo Renal Mass Biopsies.Journal of vascular and interventional radiology : JVIR · 2022Article
- Is Real-Time Microscopy on the Horizon? A Brief Review of the Potential Future Directions in Clinical Breast Tumor Microscopy Implementation.Virchows Archiv : an international journal of pathology · 2022Review
- Methylene Blue and Proflavine as Intraarterial Marker for Functional Perforazome-Comparative Study.Journal of personalized medicine · 2021Article
- Comparative Study Regarding the Properties of Methylene Blue and Proflavine and Their Optimal Concentrations for In Vitro and In Vivo Applications.Diagnostics (Basel, Switzerland) · 2020Review
- Types of advanced optical microscopy techniques for breast cancer research: a review.Lasers in medical science · 2018Review
- Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes.Biomedical optics express · 2016Article
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Authors and funding
8 authors.
Funding
Abstract
introductionPathologists currently diagnose breast lesions through histologic assessment, which requires fixation and tissue preparation. The diagnostic criteria used to classify breast lesions are qualitative and subjective, and inter-observer discordance has been shown to be a significant challenge in the diagnosis of selected breast lesions, particularly for borderline proliferative lesions. Thus, there is an opportunity to develop tools to rapidly visualize and quantitatively interpret breast tissue morphology for a variety of clinical applications.
methodsToward this end, we acquired images of freshly excised breast tissue specimens from a total of 34 patients using confocal fluorescence microscopy and proflavine as a topical stain. We developed computerized algorithms to segment and quantify nuclear and ductal parameters that characterize breast architectural features. A total of 33 parameters were evaluated and used as input to develop a decision tree model to classify benign and malignant breast tissue. Benign features were classified in tissue specimens acquired from 30 patients and malignant features were classified in specimens from 22 patients.
resultsThe decision tree model that achieved the highest accuracy for distinguishing between benign and malignant breast features used the following parameters: standard deviation of inter-nuclear distance and number of duct lumens. The model achieved 81 % sensitivity and 93 % specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90 %. The model classified IDC and DCIS with 92 % and 96 % accuracy, respectively. The cross-validated model achieved 75 % sensitivity and 93 % specificity and an overall accuracy of 88 %.
conclusionsThese results suggest that proflavine staining and confocal fluorescence microscopy combined with image analysis strategies to segment morphological features could potentially be used to quantitatively diagnose freshly obtained breast tissue at the point of care without the need for tissue preparation.
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