Explore the vast range of titles available.

A variety of Magnetic Resonance (MR) and nuclear medicine (NM) techniques have been used in symptomatic and presymptomatic SCA2 gene carriers to explore, in vivo, the physiopathological biomarkers of the neurological dysfunctions characterizing the associated progressive disease that presents with a cerebellar syndrome, or less frequently, with a levodopa-responsive parkinsonian syndrome. Morphometry performed on T1-weighted images and diffusion MR imaging enable structural and microstructural evaluation of the brain in presymptomatic and symptomatic SCA2 gene carriers, in whom they show the typical pattern of olivopontocerebellar atrophy observed at neuropathological examination. Proton MR spectroscopy reveals, in the pons and cerebellum of SCA2 gene carriers, a more pronounced degree of abnormal neurochemical profile compared to other spinocerebellar ataxias with decreased NAA/Cr and Cho/Cr, increased mi/Cr ratios, and decreased NAA and increased mI concentrations. These neurochemical abnormalities are detectable also in presymtomatic gene carriers. Resting state functional MRI (rsfMRI) demonstrates decreased functional connectivity within the cerebellum and of the cerebellum with fronto-parietal cortices and basal ganglia in symptomatic SCA2 subjects. 18F-fluorodeoxyglucose Positron Emission Tomography (PET) shows a symmetric decrease of the glucose uptake in the cerebellar cortex, the dentate nucleus, the brainstem and the parahippocampal cortex. Single photon emission tomography and PET using several radiotracers have revealed almost symmetric nigrostriatal dopaminergic dysfunction irrespective of clinical signs of parkinsonism which are already present in presymtomatic gene carriers. Longitudinal small size studies have proven that morphometry and diffusion MR imaging can track neurodegeneration in SCA2, and hence serve as progression biomarkers. So far, such a capability has not been reported for proton MR spectroscopy, rsfMRI and NM techniques. A search for the best surrogate marker for future clinical trials represents the current challenge for the neuroimaging community.

Lung cancer screening with low-dose CT can save lives. This European Union (EU) position statement presents the available evidence and the major issues that need to be addressed to ensure the successful implementation of low-dose CT lung cancer screening in Europe. This statement identified specific actions required by the European lung cancer screening community to adopt before the implementation of low-dose CT lung cancer screening. This position statement recommends the following actions: a risk stratification approach should be used for future lung cancer low-dose CT programmes; that individuals who enter screening programmes should be provided with information on the benefits and harms of screening, and smoking cessation should be offered to all current smokers; that management of detected solid nodules should use semi-automatically measured volume and volume-doubling time; that national quality assurance boards should be set up to oversee technical standards; that a lung nodule management pathway should be established and incorporated into clinical practice with a tailored screening approach; that non-calcified baseline lung nodules greater than 300 mm3, and new lung nodules greater than 200 mm3, should be managed in multidisciplinary teams according to this EU position statement recommendations to ensure that patients receive the most appropriate treatment; and planning for implementation of low-dose CT screening should start throughout Europe as soon as possible. European countries need to set a timeline for implementing lung cancer screening.

Cognitive control is a critical feature in adapting our behavior to environmental and internal demands with two types of inhibition having been identified, namely the proactive and the reactive. Aiming to shed light on their respective neural correlates, we decided to focus on the cerebral activity before or after presentation of the target demanding a subject’s stop as a way to separate the proactive from the reactive components associated with the tasks. Accordingly, we performed three Activation Likelihood Estimation (ALE) meta-analyses of fMRI studies exploring proactive and reactive inhibitory phases of cognitive control. For this purpose, we searched for fMRI studies investigating brain activity preceding or following target stimuli. Eight studies (291 subjects, 101 foci) were identified for the proactive analysis. Five of these studies and those previously analyzed by others (348 subjects, 199 foci) were meta-analyzed to explore the neural correlates of reactive inhibition. Overall, our results showed different networks for the two inhibitory components. Notably, we observed a contiguity between areas in the right inferior frontal gyrus pertaining to proactive inhibition and in the right middle frontal gyrus regarding reactive inhibition. These neural correlates allow proposal of a new comprehensive model of cognitive control.

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T 1 -weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.

Cardiac Troponin I (cTnI) could be used to identify individuals at elevated risk of cardiac death in lung cancer (LC) screening settings. In a population-based, randomized LC screening trial in Germany (“LUSI” study) serum cTnI was measured by high-sensitivity assay in blood samples collected at baseline, and categorized into unquantifiable/low (< 6 ng/L), intermediate (≥ 6–15 ng/L), and elevated (≥ 16 ng/L). Cox proportional-hazard models were used to estimate risk of all-cause and cardiac mortality with cTnI levels. After exclusion criteria, 3653 participants were included for our analyses, of which 82.4% had low, 12.8% intermediate and 4.8% elevated cTnI, respectively. Over a median follow up of 11.87 years a total of 439 deaths occurred, including 67 caused by cardiac events. Within the first 5 years after cTnI measurement, intermediate or elevated cTnI levels showed approximately 1.7 (HR = 1.69 [95% CI 0.57–5.02) and 4.7-fold (HR = 4.66 [1.73–12.50]) increases in risk of cardiac death relative to individuals with unquantifiable/low cTnI, independently of age, sex, smoking and other risk factors. Within this time interval, a risk model based on age, sex, BMI, smoking history and cTnI showed a combined area under the ROC curve (AUC) of 73.6 (58.1–87.3), as compared to 70.4 (53.3–83.5) for a model without cTnI. Over the time interval of > 5–10 years after blood donation, the relative risk associations with cTnI and were weaker. cTnI showed no association with mortality from any other (non-cardiac) cause. Our findings show that cTnI may be of use for identifying individuals at elevated risk specifically of short-term cardiac mortality in the context of LC screening.

Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.

The relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T hyperintensities in the cerebral WM, also known as leukoaraiosis. Sixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model. After 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia. Our findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features.

To assess the potential of histogram metrics of diffusion-tensor imaging (DTI)-derived indices in revealing neurodegeneration and its progression in spinocerebellar ataxia type 2 (SCA2). Nine SCA2 patients and 16 age-matched healthy controls, were examined twice (SCA2 patients 3.6±0.7 years and controls 3.3±1.0 years apart) on the same 1.5T scanner by acquiring T1-weighted and diffusion-weighted (b-value = 1000 s/mm2) images. Cerebrum and brainstem-cerebellum regions were segmented using FreeSurfer suite. Histogram analysis of DTI-derived indices, including mean diffusivity (MD), fractional anisotropy (FA), axial (AD) / radial (RD) diffusivity and mode of anisotropy (MO), was performed. At baseline, significant differences between SCA2 patients and controls were confined to brainstem-cerebellum. Median values of MD/AD/RD and FA/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (1.11/1.30/1.03×10(-3) mm2/s and 0.14/0.19) than in controls (0.80/1.00/0.70×10(-3) mm2/s and 0.20/0.41). Also, peak location values of MD/AD/RD and FA were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.91/1.11/0.81×10(-3) mm2/s and 0.12) than in controls (0.71/0.91/0.63×10(-3) mm2/s and 0.18). Peak height values of FA and MD/AD/RD/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.20 and 0.07/0.06/0.07×10(-3) mm2/s/year /0.07) than in controls (0.15 and 0.14/0.11/0.12/×10(-3) mm2/s/year /0.09). The rate of change of MD median values was significantly (p<0.001) higher (i.e., increased) in SCA2 patients (0.010×10(-3) mm2/s/year) than in controls (-0.003×10(-3) mm2/s/year) in the brainstem-cerebellum, whereas no significant difference was found for other indices and in the cerebrum. Histogram analysis of DTI-derived indices is a relatively straightforward approach which reveals microstructural changes associated with pontocerebellar degeneration in SCA2 and the median value of MD is capable to track its progression.