Association between disease status and abnormal FISH marker (>2 copies per cell) for the top 4 markers (CEP3, <i>TP63</i>, CEP6, <i>MYC</i>).

*<p>Adjusted by multiple logistic regression for gender, age, center, current smoking status and histologic grade.</p

Percentage of lesions with abnormal FISH copy numbers according to case or control status (70 subjects).

*<p>Adjusted by multiple logistic regression for gender, age, center, and current smoking status.</p

Distribution of lung cancer cases and controls according to demographic and histology variables.

<p>Distribution of lung cancer cases and controls according to demographic and histology variables.</p

Receiver operating characteristic curves describing the diagnostic prediction accuracy of three models, demographics alone (plain line, area under the curve (AUC) 73.4%), in combination with cytology (dashed line, AUC 86.6%) or demographics in combination with cytology and 4 FISH biomarker candidates (dotted line, AUC 92.6%).

<p>The demographic information represents gender, age, pack years smoking history, and smoking status.</p

ROC curve performance of the 12-biomarker naïve Bayes NSCLC classifier by study site.

<p>ROC curve performance of the 12-biomarker naïve Bayes NSCLC classifier by study site.</p

Potential NSCLC biomarkers^§.

§<p>Measure of the relative importance of potential biomarkers selected with KS distance (KS), KS FDR-corrected q-value (q-value), frequency for naïve Bayes (NB Freq),</p

Heat map shows the magnitude of difference for each protein measured (columns) between subject populations for the comparison of NSCLC to controls (top row) and comparisons of cases or controls between study sites (bottom row).

<p>Top row: KS distances for NSCLC versus control distributions. Bottom row: mean KS distances for all 12 pair-wise comparisons, between the four sites, of case and control samples analyzed separately. Proteins were ordered by subtracting the NSCLC KS distance from the mean site KS distance. This revealed groups of NSCLC biomarkers (top right) contrasting with preanalytical markers (bottom left).</p

Classifier specificity by level of airflow obstruction.

§<p>Spirometric classification of airflow obstruction based on GOLD staging <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015003#pone.0015003-GOLD1" target="_blank">[60]</a>.</p

Performance of classifier in demographic subsets.

<p>Performance of classifier in demographic subsets.</p

Twelve biomarker classifier proteins^§.

§<p>Up or down regulation in NSCLC cases relative to controls.</p