Abstract
Currently, individual clinical prognostic variables are used sequentially with risk-stratification after TNM staging in clinical practice for the prognostic assessment of patients with NSCLC, which is not effective for estimating the collective impact of multiple individual variables on patient outcomes. Here, we developed a clinical and PET/CT volumetric prognostic (CPVP) index that integrates the prognostic power of multiple clinical variables and metabolic tumor volume from baseline FDG-PET, for use immediately after definitive therapy. This retrospective cohort study included 998 NSCLC patients diagnosed between 2004 and 2017, randomly assigned to two cohorts for modeling the CPVP index using Cox regression models examining overall survival (OS) and subsequent validation. The CPVP index generated from the model cohort included pretreatment variables (whole-body metabolic tumor volume [MTVwb], clinical TNM stage, tumor histology, performance status, age, race, gender, smoking history) and treatment type. A clinical variable (CV) index without MTVwb and PET/CT volumetric prognostic (PVP) index without clinical variables were also generated for comparison. In the validation cohort, univariate Cox modeling showed a significant association of the index with overall survival (OS; Hazard Ratio [HR] 3.14; 95% confidence interval [95% CI] =2.71 to 3.65, p < 0.001). Multivariate Cox regression analysis demonstrated a significant association of the index with OS (HR = 3.13, 95% CI =2.66 to 3.67, p < 0.001). The index showed greater prognostic power (C-statistic = 0.72) than any of its independent variables including clinical TNM stage (C-statistic ranged from 0.50 to 0.69, all p < 0.003), CV index (C-statistic = 0.68, p < 0.001) and PVP index (C-statistic = 0.70, p = 0.006). The CPVP index for NSCLC patients has moderately strong prognostic power and is more prognostic than its individual prognostic variables and other indices. It provides a practical tool for quantitative prognostic assessment after initial treatment and therefore may be helpful for the development of individualized treatment and monitoring strategy for NSCLC patients.
Highlights
Accurate prediction of survival in patients with nonsmall cell lung cancer (NSCLC) is essential for recommending initial therapy
To determine if the addition of the MTVwb from PET data made the clinical and PET/CT volumetric prognostic (CPVP) index model perform significantly better than if the MTVwb were not included, a clinical variable (CV) index was constructed as the CPVP index excluding the MTVwb term
This is because the literature suggested that these are important predictors for lung cancer survival, and all the variables were statistically significantly associated with overall survival in the univariate Cox regression model
Summary
Accurate prediction of survival in patients with nonsmall cell lung cancer (NSCLC) is essential for recommending initial therapy. Individual clinical prognostic variables are used sequentially after TNM staging with risk-stratification in clinical practice, which is not effective for estimating the collective impact of multiple individual prognostic variables on survival after initial therapy. Individual clinical prognostic variables are used sequentially with risk-stratification after TNM staging in clinical practice for the prognostic assessment of patients with NSCLC, which is not effective for estimating the collective impact of multiple individual variables on patient outcomes. Results: The CPVP index generated from the model cohort included pretreatment variables (whole-body metabolic tumor volume [MTVwb], clinical TNM stage, tumor histology, performance status, age, race, gender, smoking history) and treatment type. It provides a practical tool for quantitative prognostic assessment after initial treatment and may be helpful for the development of individualized treatment and monitoring strategy for NSCLC patients
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