title: Combining COPD with clinical, pathological and demographic information refines prognosis and treatment response prediction of non-small cell lung cancer.
abstract: BACKGROUND
  Accurate assessment of a patient's risk of recurrence and treatment response is an important prerequisite of personalized therapy in lung cancer. This study extends a previously described non-small cell lung cancer prognostic model by the addition of chemotherapy and co-morbidities through the use of linked SEER-Medicare data.
  
  
  RESULTS
  Data on 34,203 lung adenocarcinoma and 26,967 squamous cell lung carcinoma patients were used to determine the contribution of Chronic Obstructive Pulmonary Disease (COPD) to prognostication in 30 treatment combinations. A Cox model including COPD was estimated on 1,000 bootstrap samples, with the resulting model assessed on ROC, Brier Score, Harrell's C, and Nagelkerke's R2 metrics in order to evaluate improvements in prognostication over a model without COPD. The addition of COPD to the model incorporating cancer stage, age, gender, race, and tumor grade was shown to improve prognostication in multiple patient groups. For lung adenocarcinoma patients, there was an improvement on the prognostication in the overall patient population and in patients without receiving chemotherapy, including those receiving surgery only. For squamous cell carcinoma, an improvement on prognostication was seen in both the overall patient population and in patients receiving multiple types of chemotherapy. COPD condition was able to stratify patients receiving the same treatments into significantly (log-rank p<0.05) different prognostic groups, independent of cancer stage.
  
  
  CONCLUSIONS
  Combining patient information on COPD, cancer stage, age, gender, race, and tumor grade could improve prognostication and prediction of treatment response in individual non-small cell lung cancer patients. This model enables refined prognosis and estimation of clinical outcome of comprehensive treatment regimens, providing a useful tool for personalized clinical decision-making.
authors: Department of Environmental and Occupational Health Sciences, School of Public Health, Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, West Virginia, United States of America.
authors: Joseph
authors: J
authors: Putila
authors: Department of Environmental and Occupational Health Sciences, School of Public Health, Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, West Virginia, United States of America.
authors: Nancy Lan
authors: NL
authors: Guo
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology / Q000628:therapy; D015897:Comorbidity; D016208:Databases, Factual; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology / Q000628:therapy; D008297:Male; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D011379:Prognosis; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications / Q000453:epidemiology*; D012307:Risk Factors; D018426:SEER Program; D016896:Treatment Outcome