title: Development and validation of clinical diagnostic models for the probability of malignancy in solitary pulmonary nodules.
abstract: BACKGROUND
  It is critical to develop a non-invasive and accurate method for differentiating between malignant and benign solitary pulmonary nodules. In large sample studies, the effectiveness of the diagnostic prediction model as a tool of assessment of the probability of malignancy is still unclear. The establishment of a diagnostic model based on large samples is needed.
  
  
  METHODS
  In this study, 3358 patients diagnosed with a solitary pulmonary nodule between January 2005 and March 2013, were enrolled. All patients received surgery for pulmonary nodule resection. Clinical characters, preoperative biomarker results, and computed tomography scan findings were collected. All patients were randomly separated into a training set (n = 1679) and a test set (n = 1679); we used training sets to build a diagnostic model for the malignancy probability of pulmonary nodules, and applied the test set to validate our model, as well as other published diagnostic models.
  
  
  RESULTS
  Logistic regression analysis identified 11 clinical characteristics as independent predictors of malignancy in patients with a solitary pulmonary nodule. The goodness-of-fit statistic for the model indicated that the observed proportion of malignancies did not differ from the predicted proportion (P = 0.571). The area under the curves of the receiver operator characteristic curve for our model in the training set was 0.935.
  
  
  CONCLUSIONS
  As the accuracy of the model was high, we suggest that the diagnostic model can be used as a tool to help guiding clinical decisions, when the clinician cannot make a definitive diagnosis of a solitary pulmonary nodule.
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Jingsi
authors.initials: J
authors.lastname: Dong
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Nan
authors.initials: N
authors.lastname: Sun
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Jiagen
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Ziyuan
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Baihua
authors.initials: B
authors.lastname: Zhang
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Zhaoli
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Yibo
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Zhou
authors.affiliation: Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences Beijing, China.
authors.forename: Jie
authors.initials: J
authors.lastname: He
keywords: Benign; lung cancer; malignant; solitary pulmonary nodules