Reproducibility of the principal component analysis (PCA)-based data-driven respiratory gating on texture features in
Shohei Fukai1,2, Hiromitsu Daisaki2,3, Mitsutomi Ishiyama1
1Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan.
Objective:
Texture analysis is one of the lung cancer countermeasures in the field of radiomics. Even though image quality affects texture features, the reproducibility of principal component analysis (PCA)-based data-driven respiratory gating (DDG) on texture features remains poorly understood. Hence, this study aimed to clarify the reproducibility of PCA-based DDG on texture features in non-small cell lung cancer (NSCLC) patients with 18 F-Fluorodeoxyglucose (18 F-FDG) Positron emission tomography/computed tomography (PET/CT).
Methods:
Twenty patients with NSCLC who underwent 18 F-FDG PET/CT in routine clinical practice were retrospectively analyzed. Each patient's PET data were reconstructed in two PET groups of no gating (NG-PET) and PCA-based DDG gating (DDG-PET). Forty-six image features were analyzed using LIFEx software. Reproducibility was evaluated using Lin's concordance correlation coefficient ( ) and percentage difference (%Diff). Non-reproducibility was defined as having unacceptable strength < 0.8) and a %Diff of >10%. NG-PET and DDG-PET were compared using the Wilcoxon signed-rank test.
Results:
A total of 3/46 (6.5%) image features had unacceptable strength, and 9/46 (19.6%) image features had a %Diff of >10%. Significant differences between the NG-PET and DDG-PET groups were confirmed in only 4/46 (8.7%) of the high %Diff image features.
Conclusion:
Although the DDG application affected several texture features, most image features had adequate reproducibility. PCA-based DDG-PET can be routinely used as interchangeable images for texture feature extraction from NSCLC patients.
