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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 analysis on squamous cell lung cancer patients converted to AJCC 7th Edition.a) Histogram of Hazard Scores obtained from the comprehensive model. b) Probability of death from lung cancer prior to 24 months based on Hazard Scores calculated using the comprehensive model. c) Kaplan-Meier survival plots for low-, intermediate-, and high-risk groups determined by the comprehensive model (blue) and AJCC staging alone (orange). d.) Average survival of each group in months, with log-rank P-values shown. e) Kaplan-Meier survival plots for each risk group in patients who received surgery without radiation. f.) Average survival for risk groups in patients who received surgery without radiation. L: low-risk; Int: intermediate-risk; H: high-risk defined by the full model. Stage only model contains patient with stage 1, 2, 3a, 3b and 4.
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pone-0017493-g005: Results of survival analysis on squamous cell lung cancer patients converted to AJCC 7th Edition.a) Histogram of Hazard Scores obtained from the comprehensive model. b) Probability of death from lung cancer prior to 24 months based on Hazard Scores calculated using the comprehensive model. c) Kaplan-Meier survival plots for low-, intermediate-, and high-risk groups determined by the comprehensive model (blue) and AJCC staging alone (orange). d.) Average survival of each group in months, with log-rank P-values shown. e) Kaplan-Meier survival plots for each risk group in patients who received surgery without radiation. f.) Average survival for risk groups in patients who received surgery without radiation. L: low-risk; Int: intermediate-risk; H: high-risk defined by the full model. Stage only model contains patient with stage 1, 2, 3a, 3b and 4.

Mentions: In patients receiving surgery without radiation, the comprehensive model predicted an average survival of 15.7 months for the low-risk group versus 15.2 months for stage I (log-rank P = 0.0114). The average survival of the high-risk group did not differ significantly from that of stage IIIB/IV (P = 0.8764), due in part to the small sample size and short follow-up, although the comprehensive model showed a non-significant improvement of 5.0 versus 7.8 months. These results are summarized in Fig. 5. In patients treated with radiation without surgery or radiation with surgery, prognostic categorization was improved only in the high-risk group, with an average survival of 2.1 versus 3.2 months and 2.4 versus 6.1 months, respectively, compared to stage alone (log-rank P = 0.0136; results not shown).


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 analysis on squamous cell lung cancer patients converted to AJCC 7th Edition.a) Histogram of Hazard Scores obtained from the comprehensive model. b) Probability of death from lung cancer prior to 24 months based on Hazard Scores calculated using the comprehensive model. c) Kaplan-Meier survival plots for low-, intermediate-, and high-risk groups determined by the comprehensive model (blue) and AJCC staging alone (orange). d.) Average survival of each group in months, with log-rank P-values shown. e) Kaplan-Meier survival plots for each risk group in patients who received surgery without radiation. f.) Average survival for risk groups in patients who received surgery without radiation. L: low-risk; Int: intermediate-risk; H: high-risk defined by the full model. Stage only model contains patient with stage 1, 2, 3a, 3b and 4.
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Related In: Results  -  Collection

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

pone-0017493-g005: Results of survival analysis on squamous cell lung cancer patients converted to AJCC 7th Edition.a) Histogram of Hazard Scores obtained from the comprehensive model. b) Probability of death from lung cancer prior to 24 months based on Hazard Scores calculated using the comprehensive model. c) Kaplan-Meier survival plots for low-, intermediate-, and high-risk groups determined by the comprehensive model (blue) and AJCC staging alone (orange). d.) Average survival of each group in months, with log-rank P-values shown. e) Kaplan-Meier survival plots for each risk group in patients who received surgery without radiation. f.) Average survival for risk groups in patients who received surgery without radiation. L: low-risk; Int: intermediate-risk; H: high-risk defined by the full model. Stage only model contains patient with stage 1, 2, 3a, 3b and 4.
Mentions: In patients receiving surgery without radiation, the comprehensive model predicted an average survival of 15.7 months for the low-risk group versus 15.2 months for stage I (log-rank P = 0.0114). The average survival of the high-risk group did not differ significantly from that of stage IIIB/IV (P = 0.8764), due in part to the small sample size and short follow-up, although the comprehensive model showed a non-significant improvement of 5.0 versus 7.8 months. These results are summarized in Fig. 5. In patients treated with radiation without surgery or radiation with surgery, prognostic categorization was improved only in the high-risk group, with an average survival of 2.1 versus 3.2 months and 2.4 versus 6.1 months, respectively, compared to stage alone (log-rank P = 0.0136; results not shown).

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