scholarly journals Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation

2021 ◽  
Author(s):  
Maximilian Leitheiser ◽  
David Capper ◽  
Philipp Seegerer ◽  
Annika Lehmann ◽  
Ulrich Schüller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Comparisons of forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Six Machine Learning Models Based on Multi-Omics

2021 ◽  
Author(s):  
Liying Mo ◽  
Yuangang Su ◽  
Jianhui Yuan ◽  
Zhiwei Xiao ◽  
Ziyan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Survival Outcome ◽  
Learning Models ◽  
Machine Learning Methods ◽  
Machine Learning Models ◽  
Better Than

Abstract Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Results: For omics of HNSC, the results of the six models all showed that the performance of multi-omics was better than each single-omic alone. Results were presented which showed that the BN model played a good prediction performance (area under the curve [AUC] 0.8250) in HNSC multi-omics data. The other machine learning models RF (AUC = 0.8002), NN (AUC = 0.7200), and GLM (AUC = 0.7145) also showed high predictive performance except for DT(AUC = 0.5149) and SVM(AUC = 0.6981). And the results of a vitro qPCR were consistent with the Random forest algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the Bayesian network was the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.

scholarly journals Unique DNA Methylation Loci Distinguish Anatomic Site and HPV Status in Head and Neck Squamous Cell Carcinoma

2013 ◽  
Vol 19 (19) ◽  
pp. 5444-5455 ◽  
Author(s):  
Roberto A. Lleras ◽  
Richard V. Smith ◽  
Leslie R. Adrien ◽  
Nicolas F. Schlecht ◽  
Robert D. Burk ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Anatomic Site ◽  
Hpv Status

Abstract 4808: Detection of aberrant DNA methylation in saliva samples as a predictor of recurrence in head and neck squamous cell carcinoma patients

2011 ◽  
Author(s):  
Ana Carolina de Carvalho ◽  
Marianna M. Rettori ◽  
Danielle C. C. Maia ◽  
Andre L. Carvalho ◽  
Luiz P. Kowalski ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Aberrant Dna Methylation

scholarly journals DNA methylation in tumor-adjacent normal tissue in the course of head and neck squamous cell carcinoma as an epigenetic field for cancerization

2012 ◽  
Vol 10 (Suppl 3) ◽  
pp. A20
Author(s):  
Joanna Strzelczyk ◽  
Łukasz Krakowczyk ◽  
Anna Płachetka ◽  
Grażyna Izdebska-Straszak ◽  
Andrzej Wiczkowski
Keyword(s):  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Normal Tissue ◽  
Neck Squamous Cell Carcinoma ◽  
Adjacent Normal Tissue

scholarly journals Comprehensive Analysis of DNA Methylation in Head and Neck Squamous Cell Carcinoma Indicates Differences by Survival and Clinicopathologic Characteristics

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e54742 ◽  
Author(s):  
Justin A. Colacino ◽  
Dana C. Dolinoy ◽  
Sonia A. Duffy ◽  
Maureen A. Sartor ◽  
Douglas B. Chepeha ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Comprehensive Analysis ◽  
Neck Squamous Cell Carcinoma ◽  
Clinicopathologic Characteristics

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases

2019 ◽  
Vol 11 (509) ◽  
pp. eaaw8513 ◽  
Author(s):  
Philipp Jurmeister ◽  
Michael Bockmayr ◽  
Philipp Seegerer ◽  
Teresa Bockmayr ◽  
Denise Treue ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Pulmonary Metastases ◽  
Normal Lung ◽  
Support Vector

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.

scholarly journals DNA methylation biomarkers for head and neck squamous cell carcinoma

Epigenetics ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. 398-409 ◽  
Author(s):  
Chongchang Zhou ◽  
Meng Ye ◽  
Shumin Ni ◽  
Qun Li ◽  
Dong Ye ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma
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