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More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers
Signal detection and Bayesian inferential tools were applied to salivary biomarkers to improve screening accuracy and efficiency in detecting oral squamous cell carcinoma (OSCC). Potential cancer biomarkers are identified by significant differences in assay concentrations, receiver operating charact...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648090/ https://www.ncbi.nlm.nih.gov/pubmed/29085239 http://dx.doi.org/10.1177/1179299X17732007 |
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author | Menke, James Michael Ahsan, Md Shahidul Khoo, Suan Phaik |
author_facet | Menke, James Michael Ahsan, Md Shahidul Khoo, Suan Phaik |
author_sort | Menke, James Michael |
collection | PubMed |
description | Signal detection and Bayesian inferential tools were applied to salivary biomarkers to improve screening accuracy and efficiency in detecting oral squamous cell carcinoma (OSCC). Potential cancer biomarkers are identified by significant differences in assay concentrations, receiver operating characteristic areas under the curve (AUCs), sensitivity, and specificity. However, the end goal is to report to individual patients their risk of having disease given positive or negative test results. Likelihood ratios (LRs) and Bayes factors (BFs) estimate evidential support and compile biomarker information to optimize screening accuracy. In total, 26 of 77 biomarkers were mentioned as having been tested at least twice in 137 studies and published in 16 summary papers through 2014. Studies represented 10 212 OSCC and 25 645 healthy patients. The measure of biomarker and panel information value was number of biomarkers needed to approximate 100% positive predictive value (PPV). As few as 5 biomarkers could achieve nearly 100% PPV for a disease prevalence of 0.2% when biomarkers were ordered from highest to lowest LR. When sequentially interpreting biomarker tests, high specificity was more important than test sensitivity in achieving rapid convergence toward a high PPV. Biomarkers ranked from highest to lowest LR were more informative and easier to interpret than AUC or Youden index. The proposed method should be applied to more recently published biomarker data to test its screening value. |
format | Online Article Text |
id | pubmed-5648090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-56480902017-10-30 More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers Menke, James Michael Ahsan, Md Shahidul Khoo, Suan Phaik Biomark Cancer Technical Advance Signal detection and Bayesian inferential tools were applied to salivary biomarkers to improve screening accuracy and efficiency in detecting oral squamous cell carcinoma (OSCC). Potential cancer biomarkers are identified by significant differences in assay concentrations, receiver operating characteristic areas under the curve (AUCs), sensitivity, and specificity. However, the end goal is to report to individual patients their risk of having disease given positive or negative test results. Likelihood ratios (LRs) and Bayes factors (BFs) estimate evidential support and compile biomarker information to optimize screening accuracy. In total, 26 of 77 biomarkers were mentioned as having been tested at least twice in 137 studies and published in 16 summary papers through 2014. Studies represented 10 212 OSCC and 25 645 healthy patients. The measure of biomarker and panel information value was number of biomarkers needed to approximate 100% positive predictive value (PPV). As few as 5 biomarkers could achieve nearly 100% PPV for a disease prevalence of 0.2% when biomarkers were ordered from highest to lowest LR. When sequentially interpreting biomarker tests, high specificity was more important than test sensitivity in achieving rapid convergence toward a high PPV. Biomarkers ranked from highest to lowest LR were more informative and easier to interpret than AUC or Youden index. The proposed method should be applied to more recently published biomarker data to test its screening value. SAGE Publications 2017-10-17 /pmc/articles/PMC5648090/ /pubmed/29085239 http://dx.doi.org/10.1177/1179299X17732007 Text en © The Author(s) 2017 http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Technical Advance Menke, James Michael Ahsan, Md Shahidul Khoo, Suan Phaik More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers |
title | More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers |
title_full | More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers |
title_fullStr | More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers |
title_full_unstemmed | More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers |
title_short | More Accurate Oral Cancer Screening with Fewer Salivary Biomarkers |
title_sort | more accurate oral cancer screening with fewer salivary biomarkers |
topic | Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648090/ https://www.ncbi.nlm.nih.gov/pubmed/29085239 http://dx.doi.org/10.1177/1179299X17732007 |
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