Reproducibility of the principal component analysis (PCA)‐based data‐driven respiratory gating on texture features in non‑small cell lung cancer patients with (18)F‑FDG PET/CT

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 ([Formula: see text]) and percentage difference (%Diff). Non‐reproducibility was defined as having unacceptable strength [Formula: see text] < 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.