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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.

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Related in: MedlinePlus

Results of survival analyses performed on patient cohorts from the Director's Challenge Study and the Mary Babb Randolph Cancer Center at West Virginia University.
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pone-0017493-g008: Results of survival analyses performed on patient cohorts from the Director's Challenge Study and the Mary Babb Randolph Cancer Center at West Virginia University.

Mentions: In the adenocarcinoma cohort from MBRCC, the comprehensive model was able to improve prognostication for the low-risk group (33 versus 24 months, P = 0.0170) and borderline significant for the high-risk groups (2.2 versus 2.8 months, P = 0.058). The addition of pathological and demographic factors could not significantly improve prognostication in the squamous cell carcinoma patients from the same set (P>0.05). In the Director's Challenge cohort which contained only adenocarcinomas, the comprehensive model was able to improve prognostication for the low-risk (42.6 versus 36.2 months) and the high-risk group (2.2 versus 9.2 months), although the results were not significant (P>0.05). These results are illustrated in Fig. 8.


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

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

Results of survival analyses performed on patient cohorts from the Director's Challenge Study and the Mary Babb Randolph Cancer Center at West Virginia University.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0017493-g008: Results of survival analyses performed on patient cohorts from the Director's Challenge Study and the Mary Babb Randolph Cancer Center at West Virginia University.
Mentions: In the adenocarcinoma cohort from MBRCC, the comprehensive model was able to improve prognostication for the low-risk group (33 versus 24 months, P = 0.0170) and borderline significant for the high-risk groups (2.2 versus 2.8 months, P = 0.058). The addition of pathological and demographic factors could not significantly improve prognostication in the squamous cell carcinoma patients from the same set (P>0.05). In the Director's Challenge cohort which contained only adenocarcinomas, the comprehensive model was able to improve prognostication for the low-risk (42.6 versus 36.2 months) and the high-risk group (2.2 versus 9.2 months), although the results were not significant (P>0.05). These results are illustrated in Fig. 8.

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