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Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing—in terms of voxel size resampling, discretization, and filtering—on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson's or Spearman's correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson's and Spearman's dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features' stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-order, GLCM, GLDM, GLRLM, GLSZM, and NGTDM). Radiomic features were estimated for 6 different isotropic resampling voxel sizes, using 10 interpolation algorithms (at fixed bin width) and 6 different bin widths (at fixed interpolation algorithm). The intraclass correlation coefficient (ICC) and the coefficient of variation (CV) were calculated to assess the variability in radiomic features estimation due to preprocessing. A repeated measures correlation analysis was performed to assess any linear correlation between radiomic feature estimate and resampling voxel size or bin width. Reproducibility of radiomic feature estimate, when assessed through ICC analysis, was nominally excellent (ICC > 0.9) for shape features, good (0.75 < ICC ≤ 0.9) or moderate (0.5 < ICC ≤ 0.75) for first-order features, and moderate or poor (0 ≤ ICC ≤ 0.5) for textural features. A number of radiomic features characterized by good or excellent reproducibility in terms of ICC showed however median CV values greater than 15%. For most textural features, a significant (p < 0.05) correlation between their estimate and resampling voxel size or bin width was found. In CT imaging of patients with LARC, the estimate of textural features, as well as of first-order features to a lesser extent, is appreciably biased by preprocessing. Accordingly, this should be taken into account when planning clinical or research studies, as well as when comparing results from different studies and performing multicenter studies. [ABSTRACT FROM AUTHOR]

Given the inherent characteristics of nonlinearity and nonstationarity of iterative reconstruction algorithms in computed tomography (CT) imaging, this study aimed to perform, for the first time, a voxel-based characterization of noise properties in CT imaging with the ASiR-V and ASiR algorithms as compared with conventional filtered back projection (FBP). Multiple repeated scans of the Catphan-504 phantom were carried out. CT images were reconstructed using FBP and ASiR/ASiR-V with different blending levels of reconstruction (20%, 40%, 60%, 80%, 100%). Noise maps and their nonuniformity index (NUI) were obtained according to the approach proposed by the report of AAPM TG-233. For the homogeneous CTP486 module, ASiR-V/ASiR allowed a noise reduction of up to 63.7%/52.9% relative to FBP. While the noise reduction values of ASiR-V-/ASiR-reconstructed images ranged up to 33.8%/39.9% and 31.2%/35.5% for air and Teflon contrast objects, respectively, these values were approximately 60%/50% for other contrast objects (PMP, LDPE, polystyrene, acrylic, Delrin). Moreover, for all contrast objects but air and Teflon, ASiR-V showed a greater noise reduction potential than ASiR when the blending level was ≥40%. While noise maps of the homogenous CTP486 module showed only a slight spatial variation of noise (NUI < 5.2%) for all reconstruction algorithms, the NUI values of iterative-reconstructed images of the nonhomogeneous CTP404 module increased nonlinearly with blending level and were 19%/15% and 6.7% for pure ASiR-V/ASiR and FBP, respectively. Overall, these results confirm the potential of ASiR-V and ASiR in reducing noise as compared with conventional FBP, suggesting, however, that the use of pure ASiR-V or ASiR might be suboptimal for specific clinical applications. [ABSTRACT FROM AUTHOR]

Breast imaging represents a relatively recent and promising field of application of quantitative diffusion-MRI techniques. In view of the importance of guaranteeing and assessing its reliability in clinical as well as research settings, the aim of this study was to specifically characterize how the main MR scanner system-related factors affect quantitative measurements in diffusion-MRI of the breast. In particular, phantom acquisitions were performed on three 1.5 T MR scanner systems by different manufacturers, all equipped with a dedicated multi-channel breast coil as well as acquisition sequences for diffusion-MRI of the breast. We assessed the accuracy, inter-scan and inter-scanner reproducibility of the mean apparent diffusion coefficient measured along the main orthogonal directions () as well as of diffusion-tensor imaging (DTI)-derived mean diffusivity (MD) measurements. Additionally, we estimated spatial non-uniformity of (NU) and MD (NUMD) maps. We showed that the signal-to-noise ratio as well as overall calibration of high strength diffusion gradients system in typical acquisition sequences for diffusion-MRI of the breast varied across MR scanner systems, introducing systematic bias in the measurements of diffusion indices. While and MD values were not appreciably different from each other, they substantially varied across MR scanner systems. The mean of the accuracies of measured and MD was in the range [−2.3%,11.9%], and the mean of the coefficients of variation for and MD measurements across MR scanner systems was 6.8%. The coefficient of variation for repeated measurements of both and MD was < 1%, while NU and NUMD values were <4%. Our results highlight that MR scanner system-related factors can substantially affect quantitative diffusion-MRI of the breast. Therefore, a specific quality control program for assessing and monitoring the performance of MR scanner systems for diffusion-MRI of the breast is highly recommended at every site, especially in multicenter and longitudinal studies. [ABSTRACT FROM AUTHOR]

Background: In the context of the coronavirus disease 2019 (COVID-19) pandemic, liver-directed therapies (LDTs) may offer minimally invasive integrative tools for tumor control. Among them, selective internal radiation therapy (SIRT) represents a safe, flexible and effective treatment. Purpose of this study is to present our experience with SIRT during the first wave of COVID-19 pandemic and provide an overview of the indications and challenges of SIRT in this scenario. Methods: We retrospectively analyzed the number of patients evaluated by Multidisciplinary Liver Tumor Board (MLTB) and who were undergoing LDTs between March and July 2020 and compared it with 2019. For patients treated with SIRT, clinical data, treatment details and the best radiological response were collected. Results: Compared to 2019, we observed a 27.5% reduction in the number of patients referred to MLTB and a 28.3% decrease in percutaneous ablations; transarterial chemoembolizations were stable, while SIRT increased by 64%. The majority of SIRT patients (75%) had primary tumors, mostly HCC. The best objective response and disease control rates were 56.7% and 72.2%, respectively. Conclusion: The first wave of the COVID-19 pandemic was characterized by an increased demand for SIRT, which represents a safe, flexible and effective treatment, whose manageability will further improve by simplifying the treatment workflow, developing user-friendly and reliable tools for personalized dosimetry and improving interdisciplinary communication. [ABSTRACT FROM AUTHOR]