Risk Stratification for Second Primary Lung Cancer
Menée à partir des données des registres américains des cancers portant sur 20 032 patients ayant survécu au moins 5 ans à un premier cancer primitif du poumon diagnostiqué entre 1988 et 2003, cette étude estime le risque à 10 ans de second cancer primitif du poumon, puis analyse l'utilité d'un modèle mathématique, basé sur 7 critères clinico-pathologiques, pour identifier parmi les patients ayant survécu à un cancer du poumon ceux pouvant bénéficier d'un examen de dépistage par tomographie numérique
Purpose : This study estimated the 10-year risk of developing second primary lung cancer (SPLC) among survivors of initial primary lung cancer (IPLC) and evaluated the clinical utility of the risk prediction model for selecting eligibility criteria for screening.
Methods : SEER data were used to identify a population-based cohort of 20,032 participants diagnosed with IPLC between 1988 and 2003 and who survived ≥ 5 years after the initial diagnosis. We used a proportional subdistribution hazards model to estimate the 10-year risk of developing SPLC among survivors of lung cancer LC in the presence of competing risks. Considered predictors included age, sex, race, treatment, histology, stage, and extent of disease. We examined the risk-stratification ability of the prediction model and performed decision curve analysis to evaluate the clinical utility of the model by calculating its net benefit in varied risk thresholds for screening.
Results : Although the median 10-year risk of SPLC among survivors of LC was 8.36%, the estimated risk varied substantially (range, 0.56% to 14.3%) when stratified by age, histology, and extent of IPLC in the final prediction model. The stratification by deciles of estimated risk showed that the observed incidence of SPLC was significantly higher in the tenth-decile group (12.5%) versus the first-decile group (2.9%; P < 10−10). The decision curve analysis yielded a range of risk thresholds (1% to 11.5%) at which the clinical net benefit of the risk model was larger than those in hypothetical all-screening or no-screening scenarios.
Conclusion : The risk stratification approach in SPLC can be potentially useful for identifying survivors of LC to be screened by computed tomography. More comprehensive environmental and genetic data may help enhance the predictability and stratification ability of the risk model for SPLC.
Journal of Clinical Oncology , résumé, 2016