ArticleJournal of biophotonics2013
Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications.
Article in Journal of biophotonics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 36 citations in OpenAlex.
- Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature.Exploration (Beijing, China) · 2023Review
- Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.Frontiers in microbiology · 2023Article
- Raman spectroscopy as a non-invasive diagnostic technique for endometriosis.Scientific reports · 2019Article
- Rapid identification of staphylococci by Raman spectroscopy.Scientific reports · 2017Article
- Raman spectroscopic sensing of carbonate intercalation in breast microcalcifications at stereotactic biopsy.Scientific reports · 2015Article
- Application of Raman spectroscopy to identify microcalcifications and underlying breast lesions at stereotactic core needle biopsy.Cancer research · 2013Article
- Raman imaging at biological interfaces: applications in breast cancer diagnosis.Molecular cancer · 2013Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. Here, we develop and compare different approaches for developing Raman classification algorithms to diagnose invasive and in situ breast cancer, fibrocystic change and fibroadenoma that can be associated with microcalcifications. In this study, Raman spectra were acquired from tissue cores obtained from fresh breast biopsies and analyzed using a constituent-based breast model. Diagnostic algorithms based on the breast model fit coefficients were devised using logistic regression, C4.5 decision tree classification, k-nearest neighbor (k -NN) and support vector machine (SVM) analysis, and subjected to leave-one-out cross validation. The best performing algorithm was based on SVM analysis (with radial basis function), which yielded a positive predictive value of 100% and negative predictive value of 96% for cancer diagnosis. Importantly, these results demonstrate that Raman spectroscopy provides adequate diagnostic information for lesion discrimination even in the presence of microcalcifications, which to the best of our knowledge has not been previously reported.
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Registered trials
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