SU-E-J-249: Characterization of Gynecological Tumor Heterogeneity Using Texture Analysis 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 Department of Radiation Oncology, 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 Department of Radiation Oncology, 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 Department of Radiation Oncology, 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 Department of Radiation Oncology, Durham, NC
    3. University of North Carolina School of Medicine, Chapel Hill, NC
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Abstract

Purpose:

We propose a method to examine gynecological tumor heterogeneity using texture analysis in the context of an adaptive PET protocol in order to establish if texture metrics from baseline PET-CT predict tumor response better than SUV metrics alone as well as determine texture features correlating with tumor response during radiation therapy.

Methods:

This IRB approved protocol included 29 women with node positive gynecological cancers visible on FDG-PET treated with EBRT to the PET positive nodes. A baseline and intra-treatment PET-CT was obtained. Tumor outcome was determined based on RECIST on posttreatment PET-CT. Primary GTVs were segmented using 40% threshold and a semi-automatic gradient-based contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). SUV histogram features, Metabolic Volume (MV), and Total Lesion Glycolysis (TLG) were calculated. Four 3D texture matrices describing local and regional relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 texture features were calculated. Prognostic power of baseline features derived from gradientbased and threshold GTVs were determined using the Wilcoxon rank-sum test. Receiver Operating Characteristics and logistic regression was performed using JMP (SAS Institute Inc., Cary, NC) to find probabilities of predicting response. Changes in features during treatment were determined using the Wilcoxon signed-rank test.

Results:

Of the 29 patients, there were 16 complete responders, 7 partial responders, and 6 non-responders. Comparing CR/PR vs. NR for gradient-based GTVs, 7 texture values, TLG, and SUV kurtosis had a p < 0.05. Threshold GTVs yielded 4 texture features and TLG with p < 0.05. From baseline to intra-treatment, 14 texture features, SUVmean, SUVmax, MV, and TLG changed with p < 0.05.

Conclusion:

Texture analysis of PET imaged gynecological tumors is an effective method for early prognosis and should be used complimentary to SUV metrics, especially when using gradient based segmentation.

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