ArticleScientific reports2021
Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Raman Spectroscopy in Cellular and Tissue Aging Research.Aging cell · 2025Review
- Recent advances in applications of artificial intelligence-assisted Raman spectroscopy in diagnosis of cancers.Frontiers in molecular biosciences · 2025Review
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Identification of extracellular vesicles from their Raman spectra via self-supervised learning.Scientific reports · 2024Article
- Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.Frontiers in oncology · 2024Article
- Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review.International journal of molecular sciences · 2023Review
- Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra.Applied spectroscopy · 2023Article
- Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise.Journal of clinical medicine · 2023Article
- The emerging applications and advancements of Raman spectroscopy in pediatric cancers.Frontiers in oncology · 2023Review
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Recent Advances in Raman Spectral Imaging in Cell Diagnosis and Gene Expression Prediction.Genes · 2022Review
- Label-free discrimination of tumorigenesis stages using in vitro prostate cancer bone metastasis model by Raman imaging.Scientific reports · 2022Article
- Review
- Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of ClinicalFrontiers in microbiology · 2021Article
- Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2-94.6% accuracy, 89.8-91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600-1800 cm
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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.