Optimal gross tumor volume definition in lung-sparing intensity modulated radiotherapy for pleural mesothelioma: an in silico study

<p><b>Background:</b> The gross tumor volume (GTV) definition for malignant pleural mesothelioma (MPM) is ill-defined. We therefore investigated which imaging modality is optimal: computed tomography (CT) with intravenous contrast (IVC), positron emission tomography-CT (PET/CT) or magnetic resonance imaging (MRI).</p> <p><b>Material and methods:</b> Sixteen consecutive patients with untreated stage I–IV MPM were included. Patients with prior pleurodesis were excluded. CT with IVC, 18FDG-PET/CT and MRI (T2 and contrast-enhanced T1) were obtained. CT was rigidly co-registered with PET/CT and with MRI. Three sets of pleural GTVs were defined: GTV_CT, GTV_CT+PET/CT and GTV_CT+MRI. Quantitative and qualitative evaluations of the contoured GTVs were performed.</p> <p><b>Results:</b> Compared to CT-based GTV definition, PET/CT identified additional tumor sites (defined as either separate nodules or greater extent of a known tumor) in 12/16 patients. Compared to either CT or PET/CT, MRI identified additional tumor sites in 15/16 patients (p = .7). The mean GTV_CT, GTV_CT+PET/CT and GTV_CT+MRI [±standard deviation (SD)] were 630.1 cm³ (±302.81), 640.23 cm³ (±302.83) and 660.8 cm³ (±290.8), respectively. Differences in mean volumes were not significant. The mean Jaccard Index was significantly lower in MRI-based contours versus all the others.</p> <p><b>Conclusion:</b> As MRI identified additional pleural disease sites in the majority of patients, it may play a role in optimal target volume definition.</p

Two Distinct Chronic Obstructive Pulmonary Disease (COPD) Phenotypes Are Associated with High Risk of Mortality

<div><h3>Rationale</h3><p>In COPD patients, mortality risk is influenced by age, severity of respiratory disease, and comorbidities. With an unbiased statistical approach we sought to identify clusters of COPD patients and to examine their mortality risk.</p> <h3>Methods</h3><p>Stable COPD subjects (n = 527) were classified using hierarchical cluster analysis of clinical, functional and imaging data. The relevance of this classification was validated using prospective follow-up of mortality.</p> <h3>Results</h3><p>The most relevant patient classification was that based on three clusters (phenotypes). Phenotype 1 included subjects at very low risk of mortality, who had mild respiratory disease and low rates of comorbidities. Phenotype 2 and 3 were at high risk of mortality. Phenotype 2 included younger subjects with severe airflow limitation, emphysema and hyperinflation, low body mass index, and low rates of cardiovascular comorbidities. Phenotype 3 included older subjects with less severe respiratory disease, but higher rates of obesity and cardiovascular comorbidities. Mortality was associated with the severity of airflow limitation in Phenotype 2 but not in Phenotype 3 subjects, and subjects in Phenotype 2 died at younger age.</p> <h3>Conclusions</h3><p>We identified three COPD phenotypes, including two phenotypes with high risk of mortality. Subjects within these phenotypes may require different therapeutic interventions to improve their outcome.</p> </div

Dendrogram illustrating the results of the cluster analysis in 527 COPD subjects.

<p>Subjects were classified using agglomerative hierarchical cluster analysis based on the main axes identified by principal component analysis (PCA) and multiple correspondence analyses (MCA, see Methods section). Each vertical line represents an individual subject and the length of vertical lines represents the degree of similarity between subjects. The horizontal lines identify possible cut-off for choosing the optimal number of clusters in the data. When choosing 3 clusters (upper line) the 3 groups (labelled 1 to 3) have differential mortality rates (0.5%, 20.6% and 14.3% for Phenotype 1, 2, and 3, respectively). When choosing 5 clusters (lower line, labelled 1′ to 5′), subjects in clusters 1′ and 2′ had comparable mortality rates (0.7% and 0%, respectively) and subjects in clusters 4′ and 5′ had similar mortality rates (14.3% in each group), suggesting that grouping in 5 phenotypes would not improve patient classification.</p

Mortality distribution by GOLD stage in Phenotype 2 and 3.

<p>At the end of the follow-up period, 20/97 (20.6%) and 29/203 (14.3%) subjects had died in Phenotype 2 and 3, respectively. Distribution of dead subjects by GOLD stage is expressed as % total number of death in each phenotype. The majority of Phenotype 2 subjects who died had very severe airflow limitation, whereas only 25% of Phenotype 3 subjects who died were in GOLD stage IV.</p

Description of the 527 COPD patients based on spirometric GOLD classification.

<p>BMI : body mass index; FEV1: forced expiratory volume in 1 sec, FVC: forced vital capacity, RV: residual volume, TLC: total lung capacity, TGV: thoracic gas volume, Raw: airway resistance, Sgaw: specific airway conductance, DLCO: diffusing capacity of the lung for carbon monoxide, KCO: ratio of DLCO to alveolar volume, mMRC: modified Medical Research Council Scale.</p>*<p>, % missing data: GOLD I 83%, GOLD II 28%.</p

Kaplan-Meier analysis of mortality between Phenotypes.

<p>Subjects in Phenotype 2 and 3 were at higher risk of mortality than subjects in Phenotype 1 (each comparison, <i>P</i><0.0001; log-rank test). However, no significant difference was observed between Phenotype 2 and 3, indicating that during the period of observation both group had comparable mortality.</p

Flow chart.

<p>Abbreviations: BMI: body mass index; mMRC: modified Medical Research Council; CCQ: clinical COPD questionnaire; TGV: thoracic gas volume and DLCO: diffusing capacity of the lung for carbon monoxide.</p

Cox model analysis of mortality between phenotypes.

<p>CI: confidence interval.</p

Description of the 527 COPD patients based on phenotypes identified by cluster analysis.

*<p>% missing data: Phenotype 1∶67%; Phenotype 2∶1%, Phenotype 3∶4%.</p><p>P values correspond to comparisons between the 3 phenotypes using Kruskal-Wallis or Chi-square tests, as appropriate.</p