title: An individual risk prediction model for lung cancer based on a study in a Chinese population.
abstract: OBJECTIVE
  Early detection and diagnosis remains an effective yet challenging approach to improve the clinical outcome of patients with cancer. Low-dose computed tomography screening has been suggested to improve the diagnosis of lung cancer in high-risk individuals. To make screening more efficient, it is necessary to identify individuals who are at high risk.
  
  
  METHODS
  We conducted a case-control study to develop a predictive model for identification of such high-risk individuals. Clinical data from 705 lung cancer patients and 988 population-based controls were used for the development and evaluation of the model. Associations between environmental variants and lung cancer risk were analyzed with a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic curve and the optimal operating point.
  
  
  RESULTS
  Our results indicate that lung cancer risk factors included older age, male gender, lower education level, family history of cancer, history of chronic obstructive pulmonary disease, lower body mass index, smoking cigarettes, a diet with less seafood, vegetables, fruits, dairy products, soybean products and nuts, a diet rich in meat, and exposure to pesticides and cooking emissions. The area under the curve was 0.8851 and the optimal operating point was obtained. With a cutoff of 0.35, the false positive rate, true positive rate, and Youden index were 0.21, 0.87, and 0.66, respectively.
  
  
  CONCLUSIONS
  The risk prediction model for lung cancer developed in this study could discriminate high-risk from low-risk individuals.
authors.affiliation: Cancer Center, First Hospital of Jilin University, Changchun, Jilin Province - China.
authors.forename: Xu
authors.initials: X
authors.lastname: Wang
authors.forename: Kewei
authors.initials: K
authors.lastname: Ma
authors.forename: Jiuwei
authors.initials: J
authors.lastname: Cui
authors.forename: Xiao
authors.initials: X
authors.lastname: Chen
authors.forename: Lina
authors.initials: L
authors.lastname: Jin
authors.forename: Wei
authors.initials: W
authors.lastname: Li
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D019540:Area Under Curve; D044466:Asian People / Q000706:statistics & numerical data*; D015992:Body Mass Index; D002274:Carcinogens, Environmental / Q000633:toxicity; D016022:Case-Control Studies; D002681:China / Q000453:epidemiology; D004522:Educational Status; D004781:Environmental Exposure / Q000009:adverse effects / Q000706:statistics & numerical data; D004785:Environmental Pollutants / Q000633:toxicity; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000209:etiology*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications / Q000453:epidemiology; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking / Q000009:adverse effects / Q000453:epidemiology
chemical_list: D002274:Carcinogens, Environmental; D004785:Environmental Pollutants