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M. Conron



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    P3.13 - Radiology/Staging/Screening (ID 729)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      P3.13-003 - The Lung Cancer Prognostic Index – a Risk Score to Predict Overall Survival after the Diagnosis of Non-Small Cell Lung Cancer (ID 7551)

      09:30 - 09:30  |  Author(s): M. Conron

      • Abstract
      • Slides

      Background:
      Outcomes in Non-Small Cell Lung Cancer (NSCLC) are poor but heterogeneous, even within TNM stage groups. To improve prognostic precision we aimed to develop and validate a simple model for the prediction of overall survival (OS) using patient and disease variables.

      Method:
      The study population included 1458 patients from three independent cohorts. Associations between baseline variables and OS were estimated in a derivation cohort from a prospective single-centre study (n=695) using Cox proportional hazards regression. Points were allocated to variables based on the strength of association to create the Lung Cancer Prognostic Index (LCPI). Model performance was assessed (by a c-statistic for discrimination and Cox-Snell residuals for calibration) in two independent validation cohorts (n=479 and n=284).

      Result:
      Three disease-related and six patient-related variables were found to predict OS: stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex and age. Patients were classified according to predicted LCPI score. Two-year OS rates according to LCPI in the derivation and two validation cohorts respectively were 84%, 77% and 68% (LCPI 1: score≤9); 61%, 61% and 42% (LCPI 2: score 10-13); 33%, 32% and 14% (LCPI 3: score 14-16); 7%, 16% and 5% (LCPI 4: score ≥15). Predictive performance (Harrell’s c-statistics) were 0·74 for the derivation cohort, 0·72 and 0·71 for the two validation cohorts.

      Conclusion:
      The LCPI contributes additional prognostic information which, in conjunction with other validated tools and evidence based management guidelines, may be applied to counsel patients, guide clinical trial eligibility, or standardise mortality risk for epidemiological analyses.

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