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J. Kim



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    O23 - Imaging and Screening (ID 125)

    • Event: WCLC 2013
    • Type: Oral Abstract Session
    • Track: Imaging, Staging & Screening
    • Presentations: 1
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      O23.03 - Metabolic Imaging Based Prognostic Model for Predicting Survival of Patients with Stage I Non-Small Cell Lung Cancer (ID 1841)

      16:35 - 16:45  |  Author(s): J. Kim

      • Abstract
      • Presentation
      • Slides

      Background
      The objective of this study was to develop a pretreatment prognostic model based on metabolic imaging biomarkers that could be used to predict overall survival (OS) in patients with stage I non–small cell lung cancer (NSCLC).

      Methods
      We evaluated 198 patients with pathologic stage I NSCLC who underwent pretreatment FDG PET/CT. Metabolic imaging biomarkers included maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), and coefficient of variation (COV) for primary tumors. SUV is a semiquantitative index of metabolic activity. TLG is a volumetric measurement of tumor glycolytic activity. COV is an index of tumor uptake heterogeneity. The prognostic significance of clinical variables and imaging biomarkers (age, sex, histologic cell type, tumor size, SUVmax, TLG, COV) was assessed by Cox proportional hazards regression model. Statistically significant clinical variables and imaging biomarkers in the multivariable analysis were used to construct a prognostic model for predicting survival. The predictive accuracy of the prognostic model was evaluated by Harrell's concordance index (C-index).

      Results
      Median follow-up for surviving patients was 7.5 years with a range of 5.2 to 9.9. At the time of analysis, 52 (26.3%) patients had died. Age (HR = 1.05 for 1-year increase, P = 0.007), histologic cell type (HR = 0.54 for adenocarcinoma, P = 0.027), SUVmax (HR = 1.08 for 1-unit increase, P = 0.002), and TLG (HR = 1.23 for a doubling of TLG, P = 0.021) were significantly associated with OS by univariable analysis, whereas only age (HR = 1.07 for 1-year increase, P = 0.005) and SUVmax (HR = 1.04 for 1-unit increase, P = 0.012) were significantly associated with OS by multivariable analysis. The final prognostic model included age as a clinical variable and SUVmax as a metabolic imaging biomarker to predict OS. The predictive performance of the prognostic model for OS was not improved by addition of TLG or COV. The C-index was 0.694 for the final model with age and SUVmax. Kaplan-Meier survival curves stratified by risk score showed high-risk group of patients (n = 58, SUVmax > 12 and age > 60) and low-risk group of patients (n = 48, SUVmax ≤ 12 and age ≤ 60). Figure 1

      Conclusion
      A new prognostic model based on pretreatment metabolic imaging may have potential clinical utility for risk stratification of stage I NSCLC patients.

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