ArticleThe Analyst2023
Metric-based analysis of FTIR data to discriminate tissue types in oral cancer.
Article in The Analyst, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Artificial Intelligence-Assisted Infrared Spectroscopy and Chemometrics for Enhanced Histopathology Screening of Micro- and Macrocancer Lesions.Analytical chemistry · 2026Article
- Mid-infrared microscopy for label-free digital staining: initial clinical assessment.Frontiers in bioengineering and biotechnology · 2026Article
- Review
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy.Molecules (Basel, Switzerland) · 2024Article
- Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics.Journal of personalized medicine · 2023Article
- Tissue discrimination in head and neck cancer using image fusion of IR and optical microscopy.The Analyst · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.
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What OpenQuestion holds
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.