title: [Prognostic factors in patients with small cell lung cancer].
abstract: OBJECTIVE
  To investigate the prognostic factors of small cell lung cancer (SCLC) and establish a reliable model of clinical prognostic index.
  
  
  METHODS
  Kaplan-Meier and Cox regression were used to analyze the relationship between survival time and prognostic factors in 60 cases of SCLC. The prognostic factors included clinical and laboratory parameters, serum cytokeratin fragment 19 (CYFRA21-1), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), CA125, interleukin-2 (IL-2) and soluble interleukin-2 receptors (sIL-2R).
  
  
  RESULTS
  Kaplan-Meier analysis showed that poor prognosis was in patients with KPS < 80 or extensive disease and unrelated to other clinical parameters such as age, sex and smoking index, and in patients with serum NSE > 30 micro g/L, CEA > 5.0 micro g/L, CA125 > 37 KU/L and sIL-2R > 500 KU/L. Serum IL-2 and CYFRA21-1 were also elevated, but had no significant prognostic value. Multivariate analysis indicated that serum NSE, stage and treatment of disease were independent prognostic factors. The three prognostic factors enabled establishment of a prognostic index (PI) based on a simple algorithm: PI = NSE (0 if < or = 30 micro g/L, 1 if > 30 microg/L) + stage (0 = LD, 1 = ED) + CEA (0 if < or = 5.0 microg/L, 1 if > 5.0 microg/L).
  
  
  CONCLUSIONS
  The stage of disease, systemic treatment and the level of serum NSE are independent prognostic factors. Without considering the influence of treatment-related factors on survival, the levels of serum CEA, NSE and stage of disease before treatment are significant independent prognostic factors. PI calculated on the basis of CEA, NSE and stage is recommended to predict the survival of SCLC.
authors.affiliation: Department of Chemotherapy, Shandong Tumor Hospital, Jinan 250117, China. slh9999@vip.163.com
authors.forename: Li-hua
authors.initials: LH
authors.lastname: Song
authors.forename: Xian-rang
authors.initials: XR
authors.lastname: Song
authors.forename: Xi-qin
authors.initials: XQ
authors.lastname: Zhang
authors.forename: Jie-lin
authors.initials: JL
authors.lastname: Qi
authors.forename: Xiu-ju
authors.initials: XJ
authors.lastname: Li
authors.forename: He
authors.initials: H
authors.lastname: Tian
authors.forename: Bing
authors.initials: B
authors.lastname: Bu
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood; D001932:Brain Neoplasms / Q000556:secondary; D018288:Carcinoma, Small Cell / Q000401:mortality / Q000556:secondary / Q000628:therapy; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008113:Liver Neoplasms / Q000556:secondary; D008175:Lung Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009367:Neoplasm Staging; D011379:Prognosis; D016016:Proportional Hazards Models; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor