title: Prognostic nomogram to predict survival after surgery for synchronous multiple lung cancers in multiple lobes.
abstract: BACKGROUND
  In the absence of metastatic disease, surgery for synchronous non-small-cell lung cancers involving multiple lobes can be curative. However, there currently exists no reliable prognostic instrument for this patient population after surgery. We undertook an analysis to examine the prognostic significance of adenocarcinoma histology and developed a prognostic nomogram.
  
  
  METHODS
  This study was a pooled analysis of six previously reported datasets. Patients without extra-thoracic metastasis who underwent surgical resection of synchronous lung cancers in multiple lobes were included. Those with small cell cancer, carcinoid tumor, or exclusively carcinoma in situ were excluded. A multivariable Cox proportional hazards regression model was fitted to identify independent survival predictors for nomogram development.
  
  
  RESULTS
  Data from 467 patients were analyzed. Adenocarcinoma was a sole histology in 253 patients (54.2%). Those with exclusively adenocarcinoma histology had a better median survival than their counterparts: 67.4 versus 36.2 months, (p < 0.001). Multivariable analysis incorporating histology, sex, age, maximal T-size, highest N-stage, and laterality demonstrated that having exclusively adenocarcinoma histology independently predicted an improved survival: hazard ratio 0.61 (95% confidence interval: 0.48, 0.78). Other favorable survival predictors were N0, T-size less than or equal to 3 cm, bilateral cancers, age less than 70 years, and women sex. The developed nomogram was well calibrated and demonstrated a moderate to good discrimination with a bootstrap-corrected Harrell C-statistic of 0.70.
  
  
  CONCLUSIONS
  Several unique features among patients with resected synchronous multiple lung cancers, including the presence of exclusively adenocarcinoma histology, are of prognostic significance. A simple nomogram incorporating these factors can be utilized to predict patient survival with acceptable accuracy.
authors.affiliation: *H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL; †Department of Surgery, Memoral Sloan Kettering Cancer Center, New York, NY; ‡Department of Surgery, Albany Medical Center, Albany, NY; §Thoracic Surgery Department, Georges Pompidou European Hospital, Paris, France; ║Thoracic Surgery Unit, University Hospital of Siena, Siena, Italy; ¶Yedikule Hospital for Chest Disease and Thoracic Surgery, Istanbul, Turkey; **Department of Surgery, University of Alabama at Birmingham, Birmingham, AL; and ††Department of Thoracic Oncology, H. Lee Moffitt Cancer Center, Tampa, FL.
authors.forename: Tawee
authors.initials: T
authors.lastname: Tanvetyanon
authors.forename: David J
authors.initials: DJ
authors.lastname: Finley
authors.forename: Thomas
authors.initials: T
authors.lastname: Fabian
authors.forename: Marc
authors.initials: M
authors.lastname: Riquet
authors.forename: Luca
authors.initials: L
authors.lastname: Voltolini
authors.forename: Celalettin
authors.initials: C
authors.lastname: Kocaturk
authors.forename: Ayesha
authors.initials: A
authors.lastname: Bryant
authors.forename: Lary
authors.initials: L
authors.lastname: Robinson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality* / Q000601:surgery*; D008297:Male; D049451:Nomograms; D011379:Prognosis; D016016:Proportional Hazards Models; D016019:Survival Analysis; D016896:Treatment Outcome