Author of

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  P2.01 - Advanced NSCLC (Not CME Accredited Session) (ID 950)

  • Event: WCLC 2018
  • Type: Poster Viewing in the Exhibit Hall
  • Track:
  • Presentations: 1
  • Moderators:
  • Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall

  • 26804

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    P2.01-10 - Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer (ID 12877)

    16:45 - 18:00  |  Author(s): Insuk Sohn
    • Abstract
    • Slides

    Background
    

    Tumor growth dynamics varies substantially in non-small cell lung cancer (NSCLC). We aimed to develop novel biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate prognostic power of those.

    a9ded1e5ce5d75814730bb4caaf49419 Method
    

    Fifty-three patients with advanced NSCLC included in this retrospective study. Measurable lesions on baseline and follow-up computed tomography (CT) were segmented and 23 radiomic features were extracted. All three variables reflecting patterns of longitudinal change were extracted: the area under the curve (AUC), beta value, and AUC2. We constructed models for predicting survival using multivariate cox regression, and identified the performance of these models.

    4c3880bb027f159e801041b1021e88e8 Result
    

    In volume, AUC2 showed an excellent correlation with pattern of longitudinal volume change (r = 0.848, p < 0.000), and showed a significant difference in overall survival time (p = 0.035). In multivariate regression analysis, kurtosis of positive pixel values (p < 0.000), and surface area (p = 0.001) on baseline CT, and AUC2 of density (p < 0.000), skewness of positive pixel values (p = 0.003), and entropy at inner (p = 0.001) were found to be associated with overall survival time, and the area under the receiver operating characteristics curves were 0.922, and 0.771 at 1 year, and 3 years of follow-up.

    8eea62084ca7e541d918e823422bd82e Conclusion
    

    Longitudinal change of radiomic tumor features would be prognostic biomarkers in patients with advanced NSCLC.

    6f8b794f3246b0c1e1780bb4d4d5dc53

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