Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups. Issue 2 (November 2015) - Wang, Xiaofei; Gu, Lin; Zhang, Ying; Sargent, Daniel J.; Richards, William; Ganti, Apar Kishor; Crawford, Jeffery; Cohen, Harvey Jay; Stinchcombe, Thomas; Vokes, Everett; Pang, Herbert
Highlights: Prognostic models for stage I and stage IV NSCLC were validated using data from 23CALGB trials. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated. For stage IV, Blanchon (B) and Mandrekar (M) models were evaluated. Some models have better concordance with survival than the PS only model, but the absolute improvement is small. Abstract: Purpose: Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. Patients and methods: The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c -index was used to assess the concordance between survival and risk scores. The c -index difference ( c -difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. Results: For stage I, B and PS have better survival separation. The c -index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c -difference = 0.034. For stage IV, B, M and PS have c -index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c -difference = 0.015 and 0.033, respectively. Conclusion: Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.