Reproducibility with repeat CT in radiomics study for rectal cancer
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Panpan Hu1,2, Jiazhou Wang1,2, Haoyu Zhong1,2, Zhen Zhou1,2, Lijun Shen1,2, Weigang Hu1,2, Zhen Zhang1,2
1Department of Radiotherapy, Fudan University Shanghai Cancer Center, Shanghai, China
2Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
Zhen Zhang, email: firstname.lastname@example.org
Keywords: radiomics, rectal cancer, reproducibility, textural features
Received: July 26, 2016 Accepted: September 16, 2016 Published: September 22, 2016
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.
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