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Reproducibility with Repeat CT in Radiomics Study for Rectal Cancer

Overview
Journal Oncotarget
Specialty Oncology
Date 2016 Sep 28
PMID 27669756
Citations 38
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Abstract

Purpose: To evaluate the reproducibility of radiomics features by repeating computed tomographic (CT) scans in rectal cancer. To choose stable radiomics features for rectal cancer.

Results: Volume normalized features are much more reproducible than unnormalized features. The average value of all slices is the most reproducible feature type in rectal cancer. Different filters have little effect for the reproducibility of radiomics features. For the average type features, 496 out of 775 features showed high reproducibility (ICC ≥ 0.8), 225 out of 775 features showed medium reproducibility (0.8 > ICC ≥ 0.5) and 54 out of 775 features showed low reproducibility (ICC < 0.5).

Methods: 40 rectal cancer patients with stage II were enrolled in this study, each of whom underwent two CT scans within average 8.7 days. 775 radiomics features were defined in this study. For each features, five different values (value from the largest slice, maximum value, minimum value, average value of all slices and value from superposed intermediate matrix) were extracted. Meanwhile a LOG filter with different parameters was applied to these images to find stable filter value. Concordance correlation coefficients (CCC) and inter-class correlation coefficients (ICC) of two CT scans were calculated to assess the reproducibility, based on original features and volume normalized features.

Conclusions: Features are recommended to be normalized to volume in radiomics analysis. The average type radiomics features are the most stable features in rectal cancer. Further analysis of these features of rectal cancer can be warranted for treatment monitoring and prognosis prediction.to modulate two anticancer compounds in well-defined sets of GBM patients.

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References
1.
Fave X, Mackin D, Yang J, Zhang J, Fried D, Balter P . Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?. Med Phys. 2015; 42(12):6784-97. PMC: 5148115. DOI: 10.1118/1.4934826. View

2.
Zou K, Tuncali K, Silverman S . Correlation and simple linear regression. Radiology. 2003; 227(3):617-22. DOI: 10.1148/radiol.2273011499. View

3.
Leijenaar R, Carvalho S, Velazquez E, van Elmpt W, Parmar C, Hoekstra O . Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013; 52(7):1391-7. PMC: 4533992. DOI: 10.3109/0284186X.2013.812798. View

4.
Parmar C, Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak R . Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One. 2014; 9(7):e102107. PMC: 4098900. DOI: 10.1371/journal.pone.0102107. View

5.
Zhao B, James L, Moskowitz C, Guo P, Ginsberg M, Lefkowitz R . Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology. 2009; 252(1):263-72. PMC: 2797680. DOI: 10.1148/radiol.2522081593. View