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Title: Lung cancer prognosis before and after recurrence in a population-based setting.
Authors: Consonni D,  Pierobon M,  Gail MH,  Rubagotti M,  Rotunno M,  Goldstein A,  Goldin L,  Lubin J,  Wacholder S,  Caporaso NE,  Bertazzi PA,  Tucker MA,  Pesatori AC,  Landi MT
Journal: J Natl Cancer Inst
Date: 2015 Jun
Branches: BB, ITEB, HGP, LTG, OEEB
PubMed ID: 25802059
PMC ID: PMC4838060
Abstract: BACKGROUND: Population-based estimates of absolute risk of lung cancer recurrence, and of mortality rates after recurrence, can inform clinical management. METHODS: We evaluated prognostic factors for recurrences and survival in 2098 lung cancer case patients from the general population of Lombardy, Italy, from 2002 to 2005. We conducted survival analyses and estimated absolute risks separately for stage IA to IIIA surgically treated and stage IIIB to IV non-surgically treated patients. RESULTS: Absolute risk of metastases exceeded that of local recurrence in every stage and cell type, highlighting the systemic threat of lung cancer. In stage I, the probability of dying within the first year after diagnosis was 2.7%, but it was 48.3% within first year after recurrence; in stage IV, the probabilities were 57.3% and 80.6%, respectively. Over half the patients died within one year of first metastasis. Although in stages IA to IB about one-third of patients had a recurrence, stage IIA patients had a recurrence risk (61.2%) similar to stage IIB (57.9%) and IIIA (62.8%) patients. Risk of brain metastases in stage IA to IIIA surgically treated non-small cell lung cancer patients increased with increasing tumor grade. Absolute risk of recurrence was virtually identical in adenocarcinoma and squamous cell carcinoma patients. CONCLUSIONS: This population-based study provides clinically useful estimates of risks of lung cancer recurrence and mortality that are applicable to the general population. These data highlight the need for more effective adjuvant treatments overall and within specific subgroups. The estimated risks of various endpoints are useful for designing clinical trials, whose power depends on absolute numbers of events.