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Treatment of non-small-cell lung cancer (NSCLC) might take into account comorbidities as an important variable. The aim of this study was to generate a new simplified comorbidity score (SCS) and to determine whether or not it improves the possibility of predicting prognosis of NSCLC patients. A two-step methodology was used. Step 1: An SCS was developed and its prognostic value was compared with classical prognostic determinants in the outcome of 735 previously untreated NSCLC patients. Step 2: the SCS reliability as a prognostic determinant was tested in a different population of 136 prospectively accrued NSCLC patients with a formal comparison between SCS and the classical Charlson comorbidity index (CCI). Prognosis was analysed using both univariate and multivariate (Cox model) statistics. The SCS summarised the following variables: tobacco consumption, diabetes mellitus and renal insufficiency (respective weightings 7, 5 and 4), respiratory, neoplastic and cardiovascular comorbidities and alcoholism (weighting=1 for each item). In step 1, aside from classical variables such as age, stage of the disease and performance status, SCS was a statistically significant prognostic variable in univariate analyses. In the Cox model weight loss, stage grouping, performance status and SCS were independent determinants of a poor outcome. There was a trend towards statistical significance for age (P=0.08) and leucocytes count (P=0.06). In Step 2, both SCS and well-known prognostic variables were found as significant determinants in univariate analyses. There was a trend towards a negative prognostic effect for CCI. In multivariate analysis, stage grouping, performance status, histology, leucocytes, lymphocytes, lactate dehydrogenase, CYFRA 21-1 and SCS were independent determinants of a poor prognosis. CCI was removed from the Cox model. In conclusion, the SCS, constructed as an independent prognostic factor in a large NSCLC patient population, is validated in another prospective population and appears more informative than the CCI in predicting NSCLC patient outcome.

The purpose of this study was to determine the prognostic significance of a high pretreatment serum CYFRA 21-1 level (a cytokeratin 19 fragment) adjusted for the effects of well-known co-variables in non-small-cell lung cancer (NSCLC). This meta-analysis based on individual updated data gathered comprehensive databases from published or unpublished controlled studies dealing with the prognostic effect of serum CYFRA 21-1 level at presentation in NSCLC of any stage (nine institutions, 2063 patients). Multivariate regression was carried out with the Cox model. The proportional hazard assumption for each of the selected variables retained in the final model was originally checked by log minus log plots baseline hazard ratio. The follow-up ranged from 25 to 78 months. A total of 1616 events were recorded. In the multivariate analysis performed at the 1-year end point, a high pretreatment CYFRA 21-1 level was an unfavourable prognostic determinant in all centres except one (Hazard ratio (95% confidence interval): 1.88 (1.64-2.15), P

International audience; To evaluate, according to the histologic type and initial stage, the mean cost (MC) of managing patients with lung cancer and the costs of the different management phases. A Markov approach was used to model these costs, based on the management of a representative nation-wide sample of 428 patients with newly diagnosed lung cancer. The 18-month MC ranged from US$ 20 691 (95% CI: 5777-50 380 for diffuse non-small-cell lung cancer (NSCLC) to US$ 31 833 (95% CI: 15 866-64 455) for localised small-cell lung cancer (SCLC); first-line treatment costs ranged from 33.8% of MC for medically inoperable localised NSCLC to 74.6% for diffuse SCLC; second- or third-line treatment costs ranged from 7.8% of MC for surgically treated localised NSCLC to 32% for locally advanced NSCLC; and the cost of palliative care ranged from 9.1% of MC for locally advanced NSCLC to 39.9% for medically inoperable localised NSCLC. The cost of first-line chemotherapy and the percentage of actively treated patients impacted more on MC than did the cost of second- or third-line chemotherapy regimens or the cost of palliative care. In conclusion, this model provides a robust economic analysis of the cost of lung cancer management, and will be useful for assessing the economic consequences of future changes in patient management.

In lung cancer, DNA content abnormalities have been described as a heterogeneous spectrum of impaired tumour cell DNA histogram patterns. They are merged into the common term of aneuploidy and probably reflect a high genotypic instability. In non-small-cell lung cancer, the negative effect of aneuploidy has been a subject of controversy inasmuch as studies aimed at determining the survival–DNA content relationship have reported conflicting results. We made a meta-analysis of published studies aimed at determining the prognostic effect of aneuploidy in surgically resected non-small-cell lung cancer. 35 trials have been identified in the literature. A comprehensive collection of data has been constructed taking into account the following parameters: quality of specimen, DNA content assessment method, aneuploidy definition, histology and stage grouping, quality of surgical resection and demographic characteristics of the analysed population. Among the 4033 assessable patients, 2626 suffered from non-small-cell lung cancer with aneuploid DNA content (overall frequency of aneuploidy: 0.65; 95% CI: (0.64–0.67)). The DerSimonian and Laird method was used to estimate the size effects and the Peto and Yusuf method was used in order to generate the odds ratios (OR) of reduction in risk of death for patients affected by a nearly diploid (non-aneuploid) non-small-cell lung cancer. Survivals following surgical resection, from 1 to 5 years, were chosen as the end-points of our meta-analysis. Patients suffering from a nearly diploid tumour benefited from a significant reduction in risk of death at 1, 2, 3 and 4 years with respective OR: 0.51, 0.51, 0.45 and 0.67 (P< 10−4for each end-point). 5 years after resection, the reduction of death was of lesser magnitude: OR: 0.87 (P = 0.08). The test for overall statistical heterogeneity was conventionally significant (P< 0.01) for all 5 end-points, however. None of the recorded characteristics of the studies could explain this phenomenon precluding a subset analysis. Therefore, the DerSimonian and Laird method was applied inasmuch as this method allows a correction for heterogeneity. This method demonstrated an increase in survival at 1, 2, 3, 4 and 5 years for patients with diploid tumours with respective size effects of 0.11, 0.15, 0.20, 0.20 and 0.21 (value taking into account the correction for heterogeneity;P< 10−4for each end-point). Patients who benefit from a surgical resection for non-small-cell lung cancer with aneuploid DNA content prove to have a higher risk of death. This negative prognostic factor decreases the probability of survival by 11% at one year, a negative effect deteriorating up to 21% at 5 years following surgery. © 2001 Cancer Research Campaign http://www.bjcancer.com

High serum NSE and advanced tumour stage are well-known negative prognostic determinants of small cell lung cancer (SCLC) when observed at presentation. However, such variables are reversible disease indicators as they can change during the course of therapy. The relationship between risk of death and marker level and disease state during treatment of SCLC chemotherapy is not known. A total of 52 patients with SCLC were followed during cisplatin-based chemotherapy (the median number of tumour status and marker level assessments was 4). The time-homogeneous Markov model was used in order to analyse separately the prognostic significance of change in the state of the serum marker level (NSE, CYFRA 21-1, TPS) or the change in tumour status. In this model, transition rate intensities were analysed according to three different states: alive with low marker level (state 0), alive with high marker level (state 1) and dead (absorbing state). The model analysing NSE levels showed that the mean time to move out of state ‘high marker level’ was short (123 days). There was a 44% probability of the opposite reversible state ‘low marker level’ being reached, which demonstrated the reversible property of the state ‘high marker level’. The relative risk of death from this state ‘high marker level’ was about 2.24 times greater in comparison with that of state 0 ‘low marker level’ (Wald's test; P < 0.01). For patients in state ‘high marker level’ at time of sampling, the probability of death increased dramatically, a transition explaining the rapid decrease in the probability of remaining stationary at this state. However, a non-nil probability to change from state 1 ‘high marker level’ to the opposite transient level, state 0 ‘low marker level’, was observed suggesting that, however infrequently, patients in state 1 ‘high marker level’ might still return to state 0 ‘low marker level’. Almost similar conclusions can be drawn regarding the three-state model constructed using the tumour response status. For the two cytokeratin markers, the Markov model suggests the lack of a true reversible property of these variables as there was only a very weak probability of a patient returning to state ‘low marker level’ once having entered state ‘high marker level’. In conclusion, The Markov model suggests that the observation of an increase in serum NSE level or a lack of response of the disease at any time during follow-up (according to the homogeneous assumption) was strongly associated with a worse prognosis but that the reversion to a low mortality risk state remains possible. © 1999 Cancer Research Campaign