ArticleScience translational medicine2019
Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases.
Article in Science translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 1 of them a synthesis that pooled it.
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Who cites it
84 citing papers in PubMed, 1 synthesis or guideline pooled it, 157 citations in OpenAlex.
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
- DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.Cell reports. Medicine · 2026Article
- Development and external validation of a machine learning-based Cox model for predicting in-hospital survival in classic heatstroke: a multicenter retrospective study.Scientific reports · 2026Article
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- DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification.Genome medicine · 2026Article
- CnQuant: high-resolution chromosomal copy number profiling for precision oncology in the clinics.Acta neuropathologica · 2026Article
- The Role of Molecular Testing in Head and Neck Squamous Cell Carcinoma of Unknown Primary.Head and neck pathology · 2026Review
- Comprehensive molecular analyses for diagnosis and treatment guidance in an adult neuroblastoma patient.The oncologist · 2026Article
- An AI Approach to Differentiating Lung Squamous Cell Carcinoma From Metastases of Other Origins.JAMA network open · 2026Article
- Simultaneous Head and Neck and Lung Cancers: Implications and Therapeutic Management Timing.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2026Observational
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- Personalized ctDNA analysis for detection of residual disease and recurrence in surgically treated HNSCC patients.NPJ precision oncology · 2026Article
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
- Review
- Extracting Histologic Features to Distinguish Primary and Metastatic Squamous Cell Carcinoma of the Lung.Pathology international · 2026Article
- RECAP-seq: restriction enzyme-based CpG-methylated fragment amplification for early cancer detection.Scientific reports · 2025Article
- Epigenetic-mediated positive feedback loop facilitates the progression of lung adenocarcinoma.iScience · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Mucinous cystic neoplasms of the pancreas and liver share a similar DNA methylation profile with mucinous ovarian tumors.The Journal of pathology · 2025Article
- crossNN is an explainable framework for cross-platform DNA methylation-based classification of tumors.Nature cancer · 2025Article
24 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.
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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.