[The stability and repeatability of radiomics features based on lung diffusion-weighted imaging]

Objective: To evaluate the feasibility, robustness and reproducibility of radiomics features derived from lung diffusion-weighted imaging (DWI). Methods: Thirty patients with pulmonary nodules/masses who underwent magnetic resonance imaging examination in the Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, from January 4 2019 to May 5 2019, including 16 males and 14 females, aged from 27 to 69 (57±11) years, were prospectively collected. Planar echo imaging (EPI) -DWI and fast spin-echo (TSE) -DWI scans were performed under free-breathing conditions. Each scan was repeated at an interval of 5 minutes, and the corresponding apparent diffusion coefficient (ADC) maps were reconstructed. Each DWI and ADC sequence (a total of eight groups of images) were manually segmented by two radiologists, and a total of 396 radiomics features in 6 categories were extracted from each group of images. Consistency correlation coefficient (CCC) and dynamic range (DR) were used to evaluate the robustness of features between two scans, and stable features were defined as both CCC values and DR values ≥0.85. Intra-observer and interobserver reproducibility were evaluated by intra-group correlation coefficient (ICC), and ICC values≥0.75 was considered to be good reproducibility. Results: Regardless of EPI or TSE technique, the number of robust features extracted fromDWI (TSE: n=197, EPI: n=169) were higher than that of the corresponding ADC (TSE: n=126, EPI: n=148). The proportion of robust features of TSE-DWI、EPI-DWI、TSE-ADC、EPI-ADC was 49.7% (197/396), 42.7% (169/396), 31.8% (126/396) and 37.4% (148/396), respectively. Of the 396 features, 54 (13.6%) of them demonstrated great robustness (CCC and DR≥0.85) and interobserver and interobserver reproducibility (ICC≥0.75) across all sequences. Conclusions: Radiomics features derived from lung DWI showed robustness and reproducibility. Different sequences and different feature clusters have different proportions of stable features, and some features have good robustness and reproducibility between different scans, different observers, and even different sequences.