Primary data Mundt et al 2013

Concentration levels of all biomarkers measured in the regarded article. Biomarkers (6 proteins and 1 sugar) were measured in pleural effusions using commercial ELISA based kits. Detailed information is available in the article

Exclusion and inclusion criteria for the studied populations.

<p>Flow chart showing patient exclusion and inclusion for the <b>A)</b> model generation dataset and <b>B)</b> the validation dataset.</p

Demographic data of the analysed datasets.

<p>Age (IQR = interquartile range) and patient sub-grouping in the model generation data set and validation dataset. The high proportion of female mesothelioma patients in the validation dataset is most likely due to environmental asbestos and erionite exposure, which is related to geographical distribution and is in concordance with a previous study <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0072030#pone.0072030-Metintas1" target="_blank">[54]</a>.</p

Validation of hyaluronan and N-ERC/mesothelin levels as well as the two-step model.

<p><b>A)</b> and <b>B)</b> Levels of hyaluronan and N-ERC/mesothelin, respectively, in the validation dataset. The dotted line represents cut-off values. <b>C)</b> Predicted risk values from the two-step predictive model. Cases with predicted risks>0.9 were considered positive (above shaded area). <b>D)</b> ROC curves generated from N-ERC/mesothelin (solid black line) and hyaluronan (dotted black line) as single markers or combined in the two-step model (dotted grey line).</p

Model generation and performance.

<p><b>A)</b> ROC curves showing sensitivity and specificity for individual biomarkers. <b>B)</b> Predicted risk values from two-step model based on hyaluronan and N-ERC/mesothelin. Cases with a predicted risk>0.9 were considered positive (above shaded area). <b>C)</b> ROC curve for the two-step model.</p

Biomarker expressions in diagnostic sub-groups in the model generation dataset.

<p>Levels of hyaluronan, N-ERC/mesothelin, C-ERC/mesothelin, osteopontin, syndecan-2, syndecan-1, and thioredoxin in patients with malignant pleural mesothelioma, other malignant pleural disease, or benign effusions. Values of 0 (i.e. below the detection limit) are tabulated for each group under the respective graph, as they cannot be shown on a logarithmic scale. Dotted line represents cut-off values. Horizontal lines represent medians. N_tot/biomarker<190 indicates the exclusion of patients from individual analyses due to insufficient material (e.g. thioredoxin, n = 186).</p

Odds ratios for biomarkers in the model generation dataset.

<p>Association of biomarkers with malignant mesothelioma and diagnostic performance characteristics. Interquartile odds ratios from logistic regression models are shown with associated p-values. Sensitivity and specificity are shown at a point which optimises both measurements, and the associated positive likelihood ratio is also shown. Odds ratios were calculated using log₁₀ transformed values.</p>*<p>Based on a one-tailed statistical analysis.</p

Schematic representation and model calibration of both datasets.

<p><b>A)</b> Schematic presentation of the two-step model and its performance on the model generation dataset. <b>B)</b> Schematic presentation of the two-step model and its performance on the validation dataset. In both <b>A)</b> and <b>B)</b>, after the logistic regression a predicted risk value>0.9 indicates additional mesothelioma cases compared to hyaluronan or N-ERC/mesothelin alone. <b>C)</b> and <b>D)</b> Calibration plots showing the agreement between observed outcomes (y-axis) and predictions (x-axis) in the model generation dataset and validation dataset respectively.</p

Achieving thoracic oncology data collection in Europe: a precursor study in 35 countries

Background: A minority of European countries have participated in international comparisons with high level data on lung cancer. However, the nature and extent of data collection across the continent is simply unknown, and without accurate data collection it is not possible to compare practice and set benchmarks to which lung cancer services can aspire. Methods: Using an established network of lung cancer specialists in 37 European countries, a survey was distributed in December 2014. The results relate to current practice in each country at the time, early 2015. The results were compiled and then verified with co-authors over the following months. Results: Thirty-five completed surveys were received which describe a range of current practice for lung cancer data collection. Thirty countries have data collection at the national level, but this is not so in Albania, Bosnia-Herzegovina, Italy, Spain and Switzerland. Data collection varied from paper records with no survival analysis, to well-established electronic databases with links to census data and survival analyses. Conclusion: Using a network of committed clinicians, we have gathered validated comparative data reporting an observed difference in data collection mechanisms across Europe. We have identified the need to develop a well-designed dataset, whilst acknowledging what is feasible within each country, and aspiring to collect high quality data for clinical research.</p