Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan

Abstract

Radiomic features are usually used to predict target variables such as the absence or presence of a disease, treatment response, or time to symptom progression. One of the potential clinical applications is in patients with Parkinson's disease. Robust radiomic features for this specific imaging method have not yet been identified, which is necessary for proper feature selection. Thus, we are assessing the robustness of radiomic features in dopamine transporter imaging (DaT). For this study, we made an anthropomorphic head phantom with tissue heterogeneity using a personal 3D printer (polylactide 82% infill); the bone was subsequently reproduced with plaster. A surgical cotton ball with radiotracer (123I-ioflupane) was inserted. Scans were performed on the two-detector hybrid camera with acquisition parameters corresponding to international guidelines for DaT single photon emission tomography (SPECT). Reconstruction of SPECT was performed on a clinical workstation with iterative algorithms. Open-source LifeX software was used to extract 134 radiomic features. Statistical analysis was made in RStudio using the intraclass correlation coefficient (ICC) and coefficient of variation (COV). Overall, radiomic features in different reconstruction parameters showed a moderate reproducibility rate (ICC = 0.636, p <0.01). Assessment of ICC and COV within CT attenuation correction (CTAC) and non-attenuation correction (NAC) groups and within particular feature classes showed an excellent reproducibility rate (ICC > 0.9, p < 0.01), except for an intensity-based NAC group, where radiomic features showed a good repeatability rate (ICC = 0.893, p <0.01). By our results, CTAC becomes the main threat to feature stability. However, many radiomic features were sensitive to the selected reconstruction algorithm irrespectively to the attenuation correction. Radiomic features extracted from DaT-SPECT showed moderate to excellent reproducibility rates. These results make them suitable for clinical practice and human studies, but awareness of feature selection should be held, as some radiomic features are more robust than others.

Fig 1. Radiomic features heatmap.

This heatmap of the z-score shows the heterogeneity of radiomic features values according to different types of reconstruction. CTAC = CT-based attenuation correction; FBP = filtered back projection; NAC = no attenuation correction; OSEM = ordered subset expectation maximization.

Fig 2. COV density plot.

This plot is showing density distribution of coefficient of variation values within two groups—CT attenuation correction and no attenuation correction group; each group contains COV of radiomic features extracted from different reconstruction settings (OSEM1 –OSEM7), radiomics features that showed a significantly higher rate of COV (outliers) are demonstrated by blue dots. Listed outliers can be found in Table 3. CTAC = CT attenuation correction; NAC = no attenuation correction.