ArticleOncotarget2017
Quantitative prediction of oral cancer risk in patients with oral leukoplakia.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.PloS one · 2024Pooled it
- Non-Invasive Screening of AI-Assisted DNA Aneuploidy Cytology by Image Cytometry for the Early Diagnosis of Malignant Transformation of OPMDs.International dental journal · 2026Article
- Biomarker-based machine learning for malignant transformation in oral potentially malignant disorders: a scoping review.BMC oral health · 2026Article
- 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
- Next-generation AI framework for comprehensive oral leukoplakia evaluation and management.NPJ digital medicine · 2025Article
- Review
- Article
- Ploidy status analysis in small cell lung cancer cells and its use in cytopathological diagnosis.International journal of clinical and experimental pathology · 2025Article
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- Early identification of people at high risk of oral cancer-A review of existing risk prediction models.Journal of family medicine and primary care · 2024Review
- Chemopreventive effect of modified zeng-sheng-ping on oral squamous cell carcinoma by regulating tumor associated macrophages through targeting tnf alpha induced protein 6.BMC complementary medicine and therapies · 2024Article
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- 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
- Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023Article
- Risk prediction models for head and neck cancer: A rapid review.Laryngoscope investigative otolaryngology · 2022Review
- Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders.Cancers · 2021Article
- Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review.Cancers · 2021Article
- [DNA cytometry of exfoliated cells in the diagnosis of oral potential malignant disorders].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2019Article
- Variants in FAT1 and COL9A1 genes in male population with or without substance use to assess the risk factors for oral malignancy.PloS one · 2019Article
- Big Data in Head and Neck Cancer.Current treatment options in oncology · 2018Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.
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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.