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Y. Wang



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    MA 14 - Diagnostic Radiology, Staging and Screening for Lung Cancer I (ID 672)

    • Event: WCLC 2017
    • Type: Mini Oral
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      MA 14.04 - Therapeutic Response Assessment of NSCLC Patients Treated with Apatinib: A Radiomics Approach Based on CT Texture Features (ID 9468)

      16:00 - 16:05  |  Author(s): Y. Wang

      • Abstract
      • Presentation
      • Slides

      Background:
      Apatinib is a novel small molecular drug targeting vascular endothelial growth factor receptor-2 (VEGFR-2), which is currently being studied in multiple tumor types. The purpose of this study was to assess the treatment response in non-small cell lung cancer (NSCLC) patients enrolled in a clinical trial of apatinib according to the response evaluation criteria in solid tumors (RECIST) using non-contrast-enhanced computed tomography (CT) texture-based radiomics approach.

      Method:
      A total of 19 NSCLC patients from our single center participated in the currently undergoing multi-center phase III ANSWER study of apatinib (NCT 02332512). Patients were categorized as responders (CR and PR) and non-responders (SD and PD) according to RECIST criteria. Radiomic texture features were extracted from target lesions in post-therapy CT of NSCLC. Lasso regression was used to establish a model to discriminate between responders and non-responders. The performance of the model was assessed with ROC in both internal and independent validation cohorts.

      Result:
      Altogether, 108 CT scans were performed. Among them, 75 scans were randomly selected as internal validation group (70%), while the remaining 33 scans (30%) were identified as an independent validation group. Three hundred and eighty-four CT texture parameters were extracted and 21 out of 384 CT texture were finally selected for the model. The area under the curve (AUC) of ROC was 0.903 in the internal validation group, and that of the independent validation group was 0.714. Figure 1



      Conclusion:
      A radiomic discriminate model was built based on post-therapy CT texture features, which demonstrated a good performance in assessing the therapeutic response in NSCLC patients treated with apatinib.

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