Risk Stratification for Second Primary Lung Cancer

Abstract

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.

Fig 1.

Importance of variables in predicting the risk of second primary lung cancer. The y-axis shows the likelihood ratio test χ² statistic subtracted by the degrees of freedom (df) conducted for each variable. Note that none of interaction terms were significant; hence, the importance metric was displayed for the main effects of the variables in the multivariable model.

Fig 2.

Cumulative risk of second primary lung cancer (SPLC) among survivors of lung cancer (ie, patients with initial primary lung cancer [IPLC] who survived ≥ 5 years after the diagnosis of IPLC). The cumulative risk of SPLC is shown by (A) age group, and by (B) histologic subtype. The risks across different groups were compared and tested using the method by Gray. (C) The cumulative risk is shown by percentile of estimated risk on the basis of the prediction model. AD, adenocarcinoma; LC, large cell; OTH, other; SC, small-cell lung cancer; SQ, squamous cell.

Fig 3.

Cumulative 10-year incidence of second primary lung cancer (SPLC) by decile (D) of the estimated risk using the prediction model. In each decile of individuals, the marginal cumulative incidence of SPLC was calculated and the equality of the estimated incidences across groups was tested using the method by Gray.

Fig 4.

Decision curve analysis for the risk model for second primary lung cancer. The x-axis is the risk threshold probability that changes from 0 to 1 (right truncated at 0.2) and the y-axis is the calculated net benefit for a given threshold probability. The blue curve depicts the net benefit of the risk model–based selection strategy for screening, whereas the gold and gray lines display the net benefits in the alternative strategies of screening all patients (gold) versus screening no patients (gray) in the data set.

Fig A1.

Calibration plots. (A) The calibration on the entire data used to fit the model. (B) The validation of the prediction model using bootstrap cross-validation method. The x-axis shows the mean predicted probability of the conditional cumulative incidence model. The y-axis indicates marginal cumulative incidence probabilities for the respective cohorts. The gray line represents equality between the predicted and observed marginal cumulative incidences.