ArticleMetabolomics : Official journal of the Metabolomic Society2020
Malignancy prediction among tissues from Oral SCC patients including neck invasions: a
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 20 citations in OpenAlex.
- Assessment and interpretation of the status of surgical margins following resective surgery for head and neck squamous cancer: a narrative review.World journal of surgical oncology · 2025Review
- Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.The Laryngoscope · 2025Article
- Biological biomarkers of oral cancer.Periodontology 2000 · 2024Review
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Study of the metabolic alterations in patulin-induced neoplastic transformation in normal intestinal cells.Toxicology research · 2021Article
- Metabolic landscape of oral squamous cell carcinoma.Metabolomics : Official journal of the Metabolomic Society · 2020Review
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOral cancer is a sixth commonly occurring cancer globally. The use of tobacco and alcohol consumption are being considered as the major risk factors for oral cancer. The metabolic profiling of tissue specimens for developing carcinogenic perturbations will allow better prognosis.
objectivesTo profile and generate precise
methodsStatistical models of
resultsForty-eight metabolites including lipids have been quantified in tumor and adjacent tissues. All metabolites other than lipids were found to be upregulated in malignant tissues except for ambiguous glucose. All of three prediction models have successfully identified malignancy status among blinded set of 64 tissues from 12 OSCC patients with an accuracy of above 90%.
conclusionThe efficiency of the models in malignancy prediction based on tumor induced metabolic perturbations supported by histopathological validation may revolutionize the OSCC assessment. Further, the results may enable machine learning to trace tumor induced altered metabolic pathways for better pattern recognition. Thus, it complements the newly developed REIMS-MS iKnife real time precession during surgery.
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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.