Limits...
Combining clinical, pathological, and demographic factors refines prognosis of lung cancer: a population-based study.

Putila J, Remick SC, Guo NL - PLoS ONE (2011)

Bottom Line: Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses.Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors.

View Article: PubMed Central - PubMed

Affiliation: Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, West Virginia, United States of America.

ABSTRACT

Background: In the treatment of lung cancer, an accurate estimation of patient clinical outcome is essential for choosing an appropriate course of therapy. It is important to develop a prognostic stratification model which combines clinical, pathological and demographic factors for individualized clinical decision making.

Methodology/principal findings: A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. A model was developed by estimating a Cox proportional hazards model on 500 bootstrapped samples. Two models, one using stage alone and another comprehensive model using additional covariates, were constructed. The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R(2), and Brier Scores in the whole patient population as well as in specific treatment modalities. Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses. Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.

Conclusion/significance: These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors. The comprehensive model significantly improves individualized prognosis upon AJCC tumor staging and is robust across a range of treatment modalities, the spectrum of patient risk, and in novel patient cohorts.

Show MeSH

Related in: MedlinePlus

Prediction of survival at 60 months for the AJCC 3rd and 6th Editions (top) and 30 months for the cases converted to the AJCC 7th Edition (bottom) for both lung adenocarcinoma (left) and squamous cell lung cancer (right) using ROC curves.P<0.05 indicates that the full model is significantly more accurate in predicting disease-specific survival than tumor stage.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3045456&req=5

pone-0017493-g001: Prediction of survival at 60 months for the AJCC 3rd and 6th Editions (top) and 30 months for the cases converted to the AJCC 7th Edition (bottom) for both lung adenocarcinoma (left) and squamous cell lung cancer (right) using ROC curves.P<0.05 indicates that the full model is significantly more accurate in predicting disease-specific survival than tumor stage.

Mentions: A total of 150,158 lung adenocarcinoma patients staged with the 3rd and 6th AJCC Editions met the criteria for inclusion. Harrell's c statistic was calculated for both the model using stage alone and the comprehensive model using additional covariates. The comprehensive model had a higher C statistic (0.732) compared to the stage only model (0.694), as well as showing better prediction of 5-year survival after the initial treatment in ROC curves (P<0.0001, Fig. 1A). A similar improvement was seen for Nagelkerke's R2 (0.294 vs. 0.253) and Brier score (0.134 vs. 0.143).


Combining clinical, pathological, and demographic factors refines prognosis of lung cancer: a population-based study.

Putila J, Remick SC, Guo NL - PLoS ONE (2011)

Prediction of survival at 60 months for the AJCC 3rd and 6th Editions (top) and 30 months for the cases converted to the AJCC 7th Edition (bottom) for both lung adenocarcinoma (left) and squamous cell lung cancer (right) using ROC curves.P<0.05 indicates that the full model is significantly more accurate in predicting disease-specific survival than tumor stage.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3045456&req=5

pone-0017493-g001: Prediction of survival at 60 months for the AJCC 3rd and 6th Editions (top) and 30 months for the cases converted to the AJCC 7th Edition (bottom) for both lung adenocarcinoma (left) and squamous cell lung cancer (right) using ROC curves.P<0.05 indicates that the full model is significantly more accurate in predicting disease-specific survival than tumor stage.
Mentions: A total of 150,158 lung adenocarcinoma patients staged with the 3rd and 6th AJCC Editions met the criteria for inclusion. Harrell's c statistic was calculated for both the model using stage alone and the comprehensive model using additional covariates. The comprehensive model had a higher C statistic (0.732) compared to the stage only model (0.694), as well as showing better prediction of 5-year survival after the initial treatment in ROC curves (P<0.0001, Fig. 1A). A similar improvement was seen for Nagelkerke's R2 (0.294 vs. 0.253) and Brier score (0.134 vs. 0.143).

Bottom Line: Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses.Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors.

View Article: PubMed Central - PubMed

Affiliation: Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, West Virginia, United States of America.

ABSTRACT

Background: In the treatment of lung cancer, an accurate estimation of patient clinical outcome is essential for choosing an appropriate course of therapy. It is important to develop a prognostic stratification model which combines clinical, pathological and demographic factors for individualized clinical decision making.

Methodology/principal findings: A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. A model was developed by estimating a Cox proportional hazards model on 500 bootstrapped samples. Two models, one using stage alone and another comprehensive model using additional covariates, were constructed. The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R(2), and Brier Scores in the whole patient population as well as in specific treatment modalities. Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses. Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.

Conclusion/significance: These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors. The comprehensive model significantly improves individualized prognosis upon AJCC tumor staging and is robust across a range of treatment modalities, the spectrum of patient risk, and in novel patient cohorts.

Show MeSH
Related in: MedlinePlus