Author of

• 11890

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  P2.19 - Poster Session 2 - Imaging (ID 180)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Imaging, Staging & Screening
  • Presentations: 1
  • Moderators:
  • Coordinates: 10/29/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 11893

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    P2.19-003 - A texture analysis approach to assess the severity of acute normal tissue changes in thoracic CT scans following radiation therapy for lung cancer (ID 970)

    09:30 - 09:30  |  Author(s): C. Straus
    • Abstract

    Background
    Quantitative analysis of thoracic computed tomography (CT) scans may be used to assess response to radiation therapy (RT). The purpose was to (1) identify a set of mathematical descriptors of image texture that correlate with the severity of radiologic normal lung tissue changes following RT and (2) quantitatively measure the extent of severity-dependent change in the values of these texture features.

    Methods
    Twenty-four patients who underwent definitive RT (median dose: 66 Gy, 2 Gy/fraction) for lung cancer were retrospectively identified. For each patient, three CT scans were collected: pre-treatment scan, post-treatment (median: 34 days, range: 8-82 days) scan, and RT planning scan with an associated dose map. Four dose regions (0-10 Gy, 10-30 Gy, 30-50 Gy, and >50 Gy) in the RT dose map were identified and mapped to the post-treatment scan through demons deformable registration. Sixty regions of interest (ROIs) distributed evenly among the four dose regions were automatically identified in the normal lung tissue of each post-treatment scan. An experienced radiologist compared the severity of change in each ROI from pre-treatment to post-treatment and categorized each as containing: 1) no abnormality, 2) mild abnormality, 3) moderate abnormality, or 4) severe abnormality. Twenty texture features that characterized gray-level intensity, region morphology, and gray-level distribution were calculated in all post-treatment ROIs and compared with anatomically matched ROIs mapped to the pre-treatment scan using demons registration. Fitted coefficients for the percent feature value change (ΔFV) between pre-treatment and post-treatment ROIs at each category of visible radiation damage were calculated using linear regression.

    Results
    Most ROIs contained no abnormality (66%) or mild abnormality (30%). ROIs with moderate (3%) or severe (1%) abnormalities were observed in 9 patients. For 19 of 20 features, significant differences (p<0.05) in ΔFV existed among ROIs assigned to various severity levels. For 12 features, significant differences in ΔFV existed among all four categories of radiation damage (see figure). Compared with regions with no abnormality, ΔFV for these 12 features increased, on average, by 1.6% (95% CI: 1.3-2%), 12% (95% CI: 9-15%), and 30% (95% CI: 19-41%), respectively, for the mild, moderate, and severe abnormality ROI categories.Figure 1

    Conclusion
    For 12 of the 20 features, ΔFV was significantly different among all categories of visible radiation damage. As severity increased, the mean feature value change from the pre-treatment scan also increased. In future studies, this approach may be used as a quantitative indicator of the severity of acute normal lung tissue damage following RT.