ArticleCellular oncology (Dordrecht, Netherlands)2014
Automated classification of oral premalignant lesions using image cytometry and Random Forests-based algorithms.
Article in Cellular oncology (Dordrecht, Netherlands), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Predicting Malignant Transformation in Oral Epithelial Dysplasia: A Systematic Comparison of Artificial Intelligence-Based Risk Models and Pathologist-Based Microscopy.Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology · 2026Pooled it
- Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis.Asian Pacific journal of cancer prevention : APJCP · 2024Pooled it
- Use of Artificial Intelligence in the Early Diagnosis of Oral Cancer: Current Update, Limitations and Future Perspectives.Journal of dentistry (Shiraz, Iran) · 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
- Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- A deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma.Journal of pathology informatics · 2024Article
- OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification.Journal of imaging informatics in medicine · 2024Article
- An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.Head and neck pathology · 2024Review
- Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature.Journal of dentistry (Shiraz, Iran) · 2023Review
- Influence of Data Augmentation Strategies on the Segmentation of Oral Histological Images Using Fully Convolutional Neural Networks.Journal of digital imaging · 2023Article
- Automatic Segmentation of Bone Canals in Histological Images.Journal of digital imaging · 2021Article
- Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview.British journal of cancer · 2021Review
- Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.PloS one · 2021Article
- Article
- Identification of Malignancy-Associated Changes in Histologically Normal Tumor-Adjacent Epithelium of Patients with HPV-Positive Oropharyngeal Cancer.Analytical cellular pathology (Amsterdam) · 2018Article
- Large-scale DNA organization is a prognostic marker of breast cancer survival.Medical oncology (Northwood, London, England) · 2017Article
- A tablet-interfaced high-resolution microendoscope with automated image interpretation for real-time evaluation of esophageal squamous cell neoplasia.Gastrointestinal endoscopy · 2016Article
- Development and validation of an AI-based application for early detection and risk stratification of oral potentially malignant disorders.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
purposeA major challenge for the early diagnosis of oral cancer is the ability to differentiate oral premalignant lesions (OPL) at high risk of progressing into invasive squamous cell carcinoma (SCC) from those at low risk. Our group has previously used high-resolution image analysis algorithms to quantify the nuclear phenotypic changes occurring in OPLs. This approach, however, requires a manual selection of nuclei images. Here, we investigated a new, semi-automated algorithm to identify OPLs at high risk of progressing into invasive SCC from those at low risk using Random Forests, a tree-based ensemble classifier.
methodsWe trained a sequence of classifiers using morphometric data calculated on nuclei from 29 normal, 5 carcinoma in situ (CIS) and 28 SCC specimens. After automated discrimination of nuclei from other objects (i.e., debris, clusters, etc.), a nuclei classifier was trained to discriminate abnormal nuclei (8,841) from normal nuclei (5,762). We extracted voting scores from this trained classifier and created an automated nuclear phenotypic score (aNPS) to identify OPLs at high risk of progression.
resultsThe new algorithm showed a correct classification rate of 80% (80.6% sensitivity, 79.3% specificity) at the cellular level for the test set, and a correct classification rate of 75% (77.8% sensitivity, 71.4% specificity) at the tissue level with a negative predictive value of 76% and a positive predictive value of 74% for predicting progression among 71 OPLs, performed on par with the manual method in our previous study.
conclusionsWe conclude that the newly developed aNPS algorithm serves as a crucial asset in the implementation of high-resolution image analysis in routine clinical pathology practice to identify lesions that require molecular evaluation or more frequent follow-up.
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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.