ArticleDiagnostics (Basel, Switzerland)2023
Breast Cancer Diagnosis Using Extended-Wavelength-Diffuse Reflectance Spectroscopy (EW-DRS)-Proof of Concept in Ex Vivo Breast Specimens Using Machine Learning.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06857669 (Breast Cancer Diagnosis Using Optical Techniques), which is not on this map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Breast Cancer Diagnosis Using Optical Techniques
Who cites it
7 citing papers in PubMed.
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- Detection of margin positivity in breast cancer using fluorescence and diffuse reflectance imaging.Journal of Taibah University Medical Sciences · 2026Article
- Time-Resolved Fluorescence and Diffuse Reflectance (TRF-DR) Spectroscopy for Heterogeneous Breast Tissue Classification and Tumor Margin Assessment.Journal of biophotonics · 2026Article
- Diffuse reflectance spectroscopy for enhanced diagnostic precision in breast cancer.Journal of translational medicine · 2025Article
- Evaluating Normalization Methods for Robust Spectral Performance Assessments of Hyperspectral Imaging Cameras.Biosensors · 2025Article
- Detection of breast cancer using machine learning on time-series diffuse optical transillumination data.Journal of biomedical optics · 2024Article
- Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450-1550 nm) has an advantage over the standard wavelength range (450-900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS can discriminate between malignant and healthy breast tissue, with improved outcomes using an extended wavelength. It is also possible to construct a simple analytical model to improve the diagnostic performance of the DRS technique.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.