ArticleCancer cytopathology2020
Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions.
Article in Cancer cytopathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- CD44-SNA1 integrated cytopathology for delineation of high grade dysplastic and neoplastic oral lesions.PloS one · 2023Pooled it
- Diagnostic tests for oral cancer and potentially malignant disorders in patients presenting with clinically evident lesions.The Cochrane database of systematic reviews · 2021Pooled it
- Article
- Deep learning single-cell analysis for cytologic evaluation of oral potentially malignant disorders.Scientific reports · 2026Article
- Current Trends in Diagnosis and Early Monitoring of Oral Cavity Cancer: Techniques and Biomarkers.Cancers · 2026Review
- Emerging Diagnostic Strategies for Oral Cancer and Oral Potentially Malignant Disorders: A PRISMA-Guided Scoping Review.Diagnostics (Basel, Switzerland) · 2026Review
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
- A critique to the article "Pina PSS, Mendes AT, Correa L, Coracin FL, de Sousa SCOM. The role of exfoliative cytology in diagnosis of oral lesions. Clin Oral Investig. 2024 Dec 6;29(1):2. doi: 10.1007/s00784-024-06080-9. PMID: 39641812."Clinical oral investigations · 2025Article
- A bibliometric analysis reveals a dynamic growth in the use of artificial intelligence in oral cancer research over three decades.Discover oncology · 2025Article
- Cytopathological quantification of NORs using artificial intelligence to oral cancer screening.Brazilian oral research · 2025Article
- Factors influencing engagement in oral cancer prevention activities among dental students and professionals in Saudi Arabia.BMC oral health · 2024Article
- Embracing technological revolution: A panorama of machine learning in dentistry.Medicina oral, patologia oral y cirugia bucal · 2024Review
- A cytomics-on-a-chip platform and diagnostic model stratifies risk for oral lichenoid conditions.Oral surgery, oral medicine, oral pathology and oral radiology · 2024Article
- Beta-defensin index: A functional biomarker for oral cancer detection.Cell reports. Medicine · 2024Article
- A brief review of cytology in dentistry.British dental journal · 2024Review
- Simultaneous Quantitative SARS-CoV-2 Antigen and Host Antibody Detection and Pre-Screening Strategy at the Point of Care.Bioengineering (Basel, Switzerland) · 2023Article
- A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics.Sensors (Basel, Switzerland) · 2022Article
- Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundThe effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation.
methodsData were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting.
resultsCell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy).
conclusionsThese new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.
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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.