SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

Authors

  • Nawrocki J,

    1. Duke University Medical Physics Graduate Program, Durham, NC
    2. Duke University Medical Center, Durham, NC
    3. University of North Carolina School of Medicine, Chapel Hill, NC
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  • Chino J,

    1. Duke University Medical Physics Graduate Program, Durham, NC
    2. Duke University Medical Center, Durham, NC
    3. University of North Carolina School of Medicine, Chapel Hill, NC
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  • Das S,

    1. Duke University Medical Physics Graduate Program, Durham, NC
    2. Duke University Medical Center, Durham, NC
    3. University of North Carolina School of Medicine, Chapel Hill, NC
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  • Craciunescu O

    1. Duke University Medical Physics Graduate Program, Durham, NC
    2. Duke University Medical Center, Durham, NC
    3. University of North Carolina School of Medicine, Chapel Hill, NC
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Abstract

Purpose:

This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation.

Methods:

A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Each clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters.

Results:

For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05.

Conclusion:

Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.

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