Impact of respiratory motion on 18F‐FDG PET radiomics stability: Clinical evaluation with a digital PET scanner

Abstract

AbstractPurpose18F‐FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18F‐FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18F‐FDG PET images using a data‐driven gating (DDG) algorithm on the digital PET scanner.Materials and MethodsA total of 101 patients who underwent oncological 18F‐FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18F‐FDG‐avid lesions from the thorax to the upper abdomen were analyzed on the non‐DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first‐order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability.ResultsIn total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18F‐FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first‐order features (entropy), one from the shape features (sphericity), four from the gray‐level co‐occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray‐level run‐length matrix features (run entropy), and 20 from the wavelet filter‐based features.ConclusionRespiratory motion has a significant impact on 18F‐FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.