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<p>In radiation oncology, 18F-FDG Positron Emission Tomography (PET) is used for determining
metabolic activity of cancers as well as delineating gross tumor volumes (GTV) for
treatment planning. More recently, PET is being utilized for adaptive therapies for
gynecological malignancies in which tumor response may be estimated and treatments
adjusted during the course of radiation. In addition to treatment assessment, 18F-FDG
PET has become a tool in the prediction of tumor response because of the derived Standard
Uptake Value (SUV), a measure of the metabolic activity of a tumor. In this study,
we seek to establish texture analysis as complimentary to SUV for predicting tumor
response as well as understanding temporal changes during treatment in gynecological
cancers. An additional experiment was performed studying the variability of texture
features from baseline and intra-treatment PET scans due to reconstruction parameters
in order to identify features that show statistically significant changes during treatment
and that are independent of reconstruction parameters.</p><p>In this IRB approved
clinical research study, 29 women with node positive gynecological malignancies visible
on PET including cervical, endometrial, vulvar, and vaginal cancers are treated with
radiation therapy. Prescribed dose varied between 45-50.4Gy, with a 55-70Gy boost
to the PET positive nodes. A baseline, intra-treatment (between 30-36Gy), and post-treatment
PET-CT were obtained with tumor response determined by a physician according to post-treatment
RECIST. All volumes were re-contoured on the intra-treatment PET-CT. Primary GTVs
were segmented both with the 40% SUVmax threshold method and a validated gradient-based
contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). A MATLAB Graphical
User Interface (GUI) called Duke FIRE (Functional Imaging Research Environment) was
developed for this study in order to calculate four mathematical algorithms representing
the spatial distribution of pixels in an image: gray level co-occurrence matrix (GLCM),
gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), and the
neighborhood gray level difference matrix (NGLDM). Features representing characteristics
of the image are derived from these texture matrices: 12 local features from the GLCM,
11 regional features from the GLRLM, 11 regional features from the GLSZM, and 5 local
features from the NGLDM. Additionally, 6 global SUV histogram features including
SUVmean, SUVmedian, SUVmax, skewness, kurtosis, and variance as well as metabolic
volume (MV) and total lesion glycolysis (TLG) are extracted. The prognostic power
of each baseline feature derived from both gradient-based and threshold segmentation
methods was determined using the Wilcoxon rank-sum test. Receiver operating characteristic
(ROC) curves were calculated to understand the sensitivity and specificity of baseline
texture features compared to SUV metrics. Changes in features from baseline to intra-treatment
PET-CT were determined using the Wilcoxon signed-rank test. A subset of 7 patient
baseline and intra-treatment raw PET data was reconstructed 6 times using a TrueX+TOF
algorithm on a Siemens Biograph mCT with varying iterations and Gaussian filter widths.
Texture features were derived from the GTV as before. Texture features per patient
were normalized to the respective clinical baseline value in order to limit variability
to reconstruction parameters. Mean percent ranges of each feature at baseline and
intra-treatment were determined and the change in features was compared using the
Wilcoxon signed-rank test.</p><p>Of the 29 patients, there were 16 complete responders,
7 partial responders, and 6 non-responders. Comparing CR/PR vs. NR for the gradient-based
GTVs, 7 texture values had a p < 0.05. The threshold GTVs yielded 4 texture features
with p < 0.05. ROC and logistic regression was performed and texture features from
both PET Edge and thresholding yielding a higher area under the curve (AUC) than SUV
metrics. Features derived from PET Edge GTVs also showed higher AUCs than the threshold
GTVs. From baseline to intra-treatment, 16 texture features changed with p < 0.05.
Texture analysis of PET imaged gynecological tumors is considerably more powerful
than SUV in early prognosis of tumor response, especially when using a gradient based
method. </p><p>We then took the 16 texture features showing significant changes (p
< 0.05) between baseline and intra-treatment PET scans in 29 patients and tested these
against the subset of reconstructed features to determine if these changes were dependent
upon the method in which the scans were reconstructed. A total of 13 features (including
entropy, zone non-uniformity, and complexity) were found to be consistently different
even when subjected to different means of reconstruction, however 3 of the 16 (inverse
variance, run percentage, and zone percentage) were found to be dependent upon these
reconstruction parameters. Texture features such as entropy, zone non-uniformity,
and complexity are excellent candidates for future investigations of changes in texture
analysis during radiation therapy of gynecological cancers. Caution should be taken
with inverse variance, run percentage, and zone percentage due to their dependence
upon reconstruction parameters.</p><p>This comprehensive work characterizes gynecological
cancers using texture analysis in order to identify texture features that may be used
for predicting tumor response as well as reflecting changes during treatment. It
is the first study to our knowledge that utilizes all 4 texture matrices (GLCM, GLRLM,
GLSZM, and NGLDM) and found 7 statistically significant features classifying responding
and non-responding gynecological tumors: energy, entropy, max probability, zone gray
level non uniformity, zone size non uniformity, contrast (NGLDM), and complexity.
A novel method was implemented extending the NGLDM and its respective features to
3D space for this study. It is also the first study concluding that a semi-automatic
gradient-based segmentation method results in more, stronger predictors than using
a 40% SUVmax threshold method. Finally, this is the first study to examine variability
of texture features with reconstruction parameters and to identify texture features
as reliable and independent of reconstruction. In conclusion, texture analysis is
a promising method of characterizing tumors visible on PET and should be considered
for future studies.</p>
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