title: Establishment of an Adjusted Prognosis Analysis Model for Initially Diagnosed Non-Small-Cell Lung Cancer With Brain Metastases From Sun Yat-Sen University Cancer Center.
abstract: The current published prognosis models for brain metastases (BMs) from cancer have not addressed the issue of either newly diagnosed non-small-cell lung cancer (NSCLC) with BMs or the lung cancer genotype. We sought to build an adjusted prognosis analysis (APA) model, a new prognosis model specifically for NSCLC patients with BMs at the initial diagnosis using adjusted prognosis analysis (APA).
  
  
  
  The model was derived using data from 1158 consecutive patients, with 837 in the derivation cohort and 321 in the validation cohort. The patients had initially received a diagnosis of BMs from NSCLC at Sun Yat-Sen University Cancer Center from 1994 to 2015. The prognostic factors analyzed included patient characteristics, disease characteristics, and treatments. The APA model was built according to the numerical score derived from the hazard ratio of each independent prognostic variable. The predictive accuracy of the APA model was determined using a concordance index and was compared with current prognosis models. The results were validated using bootstrap resampling and a validation cohort.
  
  
  
  We established 2 prognostic models (APA 1 and 2) for the whole group of patients and for those with known epidermal growth factor receptor (EGFR) genotype, respectively. Six factors were independently associated with survival time: Karnofsky performance status, age, smoking history (replaced by EGFR mutation in APA 2), local treatment of intracranial metastases, EGFR-tyrosine kinase inhibitor treatment, and chemotherapy. Patients in the derivation cohort were stratified into low- (score, 0-2), moderate- (score, 3-5), and high-risk (score 6-7) groups according to the median survival time (16.6, 10.3, and 5.2 months, respectively; P < .001). The results were further confirmed in the validation cohort.
  
  
  
  Compared with recursive partition analysis and graded prognostic assessment, APA seems to be more suitable for initially diagnosed NSCLC with BMs.
authors.affiliation: Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
authors.forename: Xiao-Xiao
authors.initials: XX
authors.lastname: Dinglin
authors.affiliation: Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
authors.forename: Shu-Xiang
authors.initials: SX
authors.lastname: Ma
authors.affiliation: Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.affiliation: Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
authors.forename: De-Lan
authors.initials: DL
authors.lastname: Li
authors.affiliation: Department of Pathology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
authors.forename: Jian-Zhong
authors.initials: JZ
authors.lastname: Liang
authors.affiliation: Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
authors.forename: Xin-Ru
authors.initials: XR
authors.lastname: Chen
authors.affiliation: Department of Cancer Prevention, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
authors.forename: Yin-Duo
authors.initials: YD
authors.lastname: Zeng
authors.affiliation: Department of Medical Oncology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China. Electronic address: chenlk@sysucc.org.cn.
authors.forename: Li-Kun
authors.initials: LK
authors.lastname: Chen
mesh_terms: D000046:Academic Medical Centers; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000401:mortality / Q000556:secondary; D002173:Cancer Care Facilities; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis* / Q000401:mortality / Q000556:secondary; D002681:China; D015331:Cohort Studies; D066246:ErbB Receptors / Q000235:genetics; D005260:Female; D005838:Genotype; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000401:mortality / Q000473:pathology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D047428:Protein Kinase Inhibitors / Q000627:therapeutic use; D012306:Risk; D016019:Survival Analysis
chemical_list: D047428:Protein Kinase Inhibitors; C512478:EGFR protein, human; D066246:ErbB Receptors
keywords: Adjusted prognosis analysis model; BMs; Epidermal growth factor receptor; NSCLC; Survival model