The prognostic value of early onset, CT derived loss of muscle and adipose tissue during chemotherapy in metastatic non-small cell lung cancer

Objectives: To evaluate the relationship between early changes in muscle and adipose tissue during chemotherapy and overall survival (OS) in stage IV non-small cell lung cancer (NSCLC). Materials and methods: In this post-hoc analysis of the first line NVALT12 trial (NCT01171170) in stage IV NSCLC, skeletal muscle (SM), radiation attenuation (RA), subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) were assessed at the third lumbar level on CT-images obtained before initiation of chemotherapy and shortly after administration of the second cycle. The contribution of changes in different body compartments to overall survival was assessed. Results: CT scans of 111 patients were included. Analysis of body composition changes between the baseline and the follow-up scan, revealed that overall SM cross sectional area (CSA), radiation attenuation and SAT CSA decreased respectively by −1.2 ± 2.9 cm2 /m2 (p < 0.001), −0.7 ± 3.3 HU (p = 0.026) and −1.9 ± 8.7 cm2 /m2 (p = 0.026), while no significant changes in VAT tissue were observed. Longitudinally, median OS was significantly shorter among patients losing SM compared to patients with preserved SM (9.4 versus 14.2 months; HR 1.9, 95% CI: 1.23, 2.79, p = 0.003). Multivariate analyses showed that proportional loss of muscle mass was associated with poor OS (HR 0.949, 95% CI: 0.915, 0.985, p = 0.006) independent from important clinical prognostic factors including WHO-PS, gender, age and Charlson comorbidity index. Conclusion: Early loss of SM during first line chemotherapy is a poor prognostic factor in stage IV NSCLC patients. Future studies have to reveal whether early supportive intervention guided by initial CT muscle response to chemotherapy can influence the wasting process and related mortality ris

Chemotherapy for pulmonary large cell neuroendocrine carcinomas: Does the regimen matter?

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A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: A step toward individualized care and shared decision making

Background: Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models. Purpose: In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors. Materials and methods: Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: (1) after the first consultation of the patient, and (2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area Under the Curve (AUC) analysis. Results: A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH, respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models. Conclusion: The models substantially outperformed ROs' predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact studies

CD44 and OTP are strong prognostic markers for pulmonary carcinoids

Purpose: Pulmonary carcinoids are well-differentiated neuroendocrine tumors showing usually a favorable prognosis. However, there is a risk for late recurrence and/or distant metast