The Lung Cancer Prognostic Index provides personalized and disease-specific prognostic information for patients with non-small cell lung cancer (NSCLC) and may result in better survival outcomes, according to a recent study published in the British Journal of Cancer (online July 20, 2017; doi:10.1038/bjc.2017.232).
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The current prognostic models for patients with NSCLC utilize disease staging information to provide limited prognoses and determine optimal therapy. However, the heterogeneity within stage groups indicates that there are variable disease factors and disease characteristics that affect prognosis in this patient population.
Marliese Alexander, MD, Peter MacCallum Cancer Centre (Australia), and colleagues conducted a study to develop and validate a simple prognostic module using patient and disease variables to improve prognostic accuracy in NSCLC. Researchers analyzed a prospective registry and study data to derive the Lung Cancer Prognostic Index (n = 695 patients), which was subsequently tested in two independent validation cohorts (n = 479 patients; n = 284 patients).
Variables found to demonstrate significant associations with overall survival (OS) in the Lung Cancer Prognostic Index included stage, mutation status, histology, performance status, smoking history, respiratory comorbidity, weight loss, sex, and age.
According to the Lung Cancer Prognostic Index, the 2-year OS rates in the derivation cohort and two validation cohorts, respectively, were 84%, 77%, and 68% (Lung Cancer Prognostic Index 1 score ≤ 9); 61%, 61%, and 42% (Lung Cancer Prognostic Index 2 score 10-13); 33%, 32%, and 14% (Lung Cancer Prognostic Index 3 score 14-16); and 7%, 16%, and 5% (Lung Cancer Prognostic Index 4 score > 15).
Discrimination (Harrell’s c-statistic) was 0.74 for the derivation cohort, 0.72, and 0.71 for the two validation cohorts, respectively.
Researchers concluded that the prognostic model should be considered a simple and generalizable scoring system in patients with NSCLC. “The Lung Cancer Prognostic Index contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardize mortality risk for epidemiological analyses,” they wrote.—Zachary Bessette
