Author of

• 31154

  +

  P2.08 - Oligometastatic NSCLC (ID 172)

  • Event: WCLC 2019
  • Type: Poster Viewing in the Exhibit Hall
  • Track: Oligometastatic NSCLC
  • Presentations: 1
  • Moderators:
  • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall

  • 31160

    +

    P2.08-06 - Development and Validation of a 18F-FDG PET/CT-Based Radiomics Model for Prognosis Prediction in Synchronous Oligometastatic NSCLC (ID 1818)

    10:15 - 18:15  |  Author(s): Yu Zhang
    • Abstract

    Background
    

    Oligometastatic non-small cell lung cancer (NSCLC) exists high heterogeneity with distinct outcome, and there is a lack of available biomarkers for patient stratification. In this study, we identified a positron emission tomography (PET)/computed tomography(CT)-based radiomics signature capable of predicting overall survival (OS) in patients with synchronous oligometastatic NSCLC.

    Method
    

    A primary cohort consisted of 46 patients with synchronous oligometastatic NSCLC (≤5 metastases) between January 2012 and December 2017. Clinicopathologic data was acquired from medical records and database. A total of 20648 radiomic features were extracted from pretreatment CT and PET images, which were generated from the same PET/CT scanner. A radiomics signature was built by using the least absolute shrinkage and selection operator (LASSO) regression model. Multivariate Cox regression analysis was performed to establish the predictive model. A prospective internal validation cohort contained 14 patients from January 2018 to December 2018. The performance was evaluated with Harrell’ concordance index (C-index).

    Result
    

    7 radiomics features were selected to build the radiomics signature. Smoking status (P=0.01) was the only independent clinicopathologic risk factor for overall survival prediction. Multivariate analysis indicated that the radiomics signature (P=0.007) was an independent prognostic factor, with a C-index of 0.810 and 0.900 for the primary and validation cohort.

    Conclusion
    

    This study developed a radiomics model for predicting OS in synchronous oligometastatic NSCLC, which may serve as a predictive tool to identify individualized treatment strategy. Further external validation of the model are required. Support: 81572279, 2016J004, LC2016PY016, 2018CR033.