ArticleCancers2021
Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.PloS one · 2024Pooled it
- Artificial Intelligence for Diagnosing Normal Anatomical Variants and Pathological Oral Mucosal Lesions: A Prospective Observational Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Image-Based Artificial Intelligence for Predicting Malignant Transformation of Oral Potentially Malignant Disorders: A Scoping Review.Journal of clinical medicine · 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 in Oral Cancer Diagnosis: Overcoming Challenges for Enhanced Outcomes.Current medicinal chemistry · 2026Review
- AI-driven prediction of progression to oral squamous cell carcinoma using a multiresolution pathology model.NPJ digital medicine · 2025Article
- Clinico-Pathological Assessment and Malignant Transformation in Patients with Oral Epithelial Dysplasia - A Follow-up Cohort Study.Head and neck pathology · 2025Article
- A bibliometric analysis reveals a dynamic growth in the use of artificial intelligence in oral cancer research over three decades.Discover oncology · 2025Article
- Predicting In-Hospital Mortality in Intensive Care Unit Patients Using Causal SurvivalNet With Serum Chloride and Other Causal Factors: Cross-Country Study.Journal of medical Internet research · 2025Article
- Article
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models.Frontiers in oncology · 2025Article
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.Scientific reports · 2024Article
- An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.Head and neck pathology · 2024Review
- Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network.Frontiers in endocrinology · 2024Article
- The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer.iScience · 2023Article
- Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.BMC medical research methodology · 2023Article
- Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration.Brain sciences · 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
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms-Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)-and one standard statistical method-Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index-0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.
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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.