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Acute brain injuries such as intracerebral hemorrhage (ICH) and ischemic stroke have been reported in critically ill COVID-19 patients as well as in patients treated with veno-venous (VV)-ECMO independently of their COVID-19 status. The purpose of this study was to compare critically ill COVID-19 patients with and without VV-ECMO treatment with regard to acute neurological symptoms, pathological neuroimaging findings (PNIF) and long-term deficits. The single center study was conducted in critically ill COVID-19 patients between February 1, 2020 and June 30, 2021. Demographic, clinical and laboratory parameters were extracted from the hospital’s databases. Retrospective imaging modalities included head computed tomography (CT) and magnetic resonance imaging (MRI). Follow-up MRI and neurological examinations were performed on survivors > 6 months after the primary occurrence. Of the 440 patients, 67 patients received VV-ECMO treatment (15%). Sixty-four patients (24 with VV-ECMO) developed acute neurological symptoms (pathological levels of arousal/brain stem function/motor responses) during their ICU stay and underwent neuroimaging with brain CT as the primary modality. Critically ill COVID-19 patients who received VV-ECMO treatment had a significantly lower survival during their hospital stay compared to those without (p < 0.001). Among patients treated with VV-ECMO, 10% showed acute PNIF in one of the imaging modalities during their ICU stay (vs. 4% of patients in the overall COVID-19 ICU cohort). Furthermore, 9% showed primary or secondary ICH of any severity (vs. 3% overall), 6% exhibited severe ICH (vs. 1% overall) and 1.5% were found to have non-hemorrhagic cerebral infarctions (vs. < 1% overall). There was a weak, positive correlation between patients treated with VV-ECMO and the development of acute neurological symptoms. However, the association between the VV-ECMO treatment and acute PNIF was negligible. Two survivors (one with VV-ECMO-treatment/one without) showed innumerable microhemorrhages, predominantly involving the juxtacortical white matter. None of the survivors exhibited diffuse leukoencephalopathy. Every seventh COVID-19 patient developed acute neurological symptoms during their ICU stay, but only every twenty-fifth patient had PNIF which were mostly ICH. VV-ECMO was found to be a weak risk factor for neurological complications (resulting in a higher imaging rate), but not for PNIF. Although logistically complex, repeated neuroimaging should, thus, be considered in all critically ill COVID-19 patients since ICH may have an impact on the treatment decisions and outcomes.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Rationale and objectives: The objective of this study was to analyze the role of dynamic magnetic resonance imaging (MRI) in patients who suffered from groin pain and whose physical examination and ultrasound returned inconclusive/indefinite results, as well as in patients receiving an ongoing assessment for a previous herniotomy.
Material and methods: For this study, 25 patients 14 women and 11 men were selected with a mean age of 41.6 years, including clinical complaints, such as groin pain and or a previous herniotomies. These patients underwent dynamic MRI. Reports were created by a radiology resident and a radiology consultant. Clinical and ultrasound documentation were compared to with imaging results from the MRI.
Results: The results of the dynamic MRI were negative for 23 patients (92%) and positive for two patients (8%). One patient suffered from an indirect hernia and one from a femoral hernia. A repeated hernia was an excluding for the preoperated patients with pain and ongoing assessment.
Conclusions: Dynamic MRI shows substantially higher diagnostic performance in exclusion of inguinal hernia, when compared to a physical examination and ultrasound. The examination can also be used in assessments to analyze the operation’s results.