ArticleScientific reports2023
An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Accuracy of AI-based raman spectroscopy in the diagnosis of gastric cancer: a systematic review and meta-analysis.Lasers in medical science · 2026Pooled it
- The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.BMC medical informatics and decision making · 2025Pooled it
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- Portable Multispectral Optoelectronic System for Thyroid Cancer Detection.Sensors (Basel, Switzerland) · 2026Article
- In vivo Raman spectroscopy for real-time biochemical assessment of tissue pathology and physiology.Nature protocols · 2026Review
- Label-free molecular profiling of cancer using Raman spectroscopy: from fundamentals to clinical applications.Frontiers in oncology · 2026Review
- Article
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- Explainable AI-Based Feature Selection Approaches for Raman Spectroscopy.Diagnostics (Basel, Switzerland) · 2025Article
- Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument.Biosensors · 2025Article
- Raman Spectroscopy in Cellular and Tissue Aging Research.Aging cell · 2025Review
- Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.Biosensors · 2024Article
- Combined SERS-Raman screening of HER2-overexpressing or silenced breast cancer cell lines.Journal of nanobiotechnology · 2024Article
- Explainable artificial intelligence and microbiome data for food geographical origin: the Mozzarella di Bufala Campana PDO Case of Study.Frontiers in microbiology · 2024Article
- Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification.Frontiers in microbiology · 2024Article
- Unraveling the microbiome-metabolome nexus: a comprehensive study protocol for personalized management of Behçet's disease using explainable artificial intelligence.Frontiers in microbiology · 2024Article
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.
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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 Socratic graph.