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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]

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]