Determining the accurate outcome of patients with non-small cell lung cancer (NSCLC) and malignant pleural effusion (MPE) or malignant pleural pericardial effusion (MPCE) at the initial diagnosis remains a challenge. The aim of the present study was to develop an effective nomogram for individualized estimation of overall survival in these patients. Patients diagnosed between January 2010 and December 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Age, race, sex, grade, histology, laterality, stage and status of MPE or MPCE at initial diagnosis were included as covariates. Several survival models were created and the performance of each was evaluated. The most effective model was then validated by internal bootstrap resampling and by using an independent external cohort. A nomogram was created based on this survival model and the predictive accuracy of the nomogram was evaluated by calibration plots. Data from 10,268 patients with lung cancer with MPE or MPCE at initial diagnosis were collected. The multivariate analysis with a lognormal model suggested that age, race, sex, histology, stage and status of MPE or MPCE at initial diagnosis were significant independent factors to predict survival. A nomogram was constructed based on the lognormal survival model, which showed the best performance. The concordance index of the survival model in the SEER cohort was 0.736. Both internal and external validation showed an acceptable level of agreement between the nomogram-predicted survival probability and actual survival. The nomogram of the present study based on a large cohort from the SEER database may improve prognostic prediction of patients with NSCLC with MPE or MPCE at initial diagnosis, and allow physicians to make appropriate decisions for disease management of their patients.
Objective
Thymidine kinase 1 (TK1) is a key enzyme in the pyrimidine salvage pathway. Increased TK1 concentration correlates with cell division. TK1 is an emerging biomarker in cancer diagnosis; however, its effectiveness in diagnosis and management for malignant pleural effusion (MPE) is unclear. We evaluated the diagnostic efficiency and prognostic value of pleural effusion TK1 (pTK1) concentration for MPE.
Methods
From 2013 to 2017, 210 pleural effusion samples were collected from 160 patients diagnosed with MPE and 50 patients diagnosed with benign pleural effusion (BPE). TK1 concentrations in pleural effusion were measured by chemiluminescence dot blot assays. The median follow‐up was 12 months. We constructed a receiver‐operating characteristic (ROC) curve to find the optimal cutoff value for MPE diagnosis. The hazard ratios were estimated using a multivariable Cox proportional hazard model. A nomogram was drawn to illustrate the prognostic characteristics of MPE.
Results
The TK1 concentration in pleural effusion was significantly higher in MPE than BPE (P < 0.001), and patients with MPE could be distinguished by an optimal cutoff value of 3.10 pmol/L with a sensitivity of 0.894 and a specificity of 0.800. The multivariate analysis suggested that pTK1 concentration was an independent predictor of survival in patients with MPE.
Conclusions
The diagnostic and prognostic prediction of MPE may be improved by measuring pTK1 concentration and utilizing a multivariate nomogram.
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