HUNCHEST-II contributes to a shift to earlier-stage lung cancer detection: final results of a nationwide screening program.

OBJECTIVES: The introduction of low-dose CT (LDCT) altered the landscape of lung cancer (LC) screening and contributed to the reduction of mortality rates worldwide. Here we report the final results of HUNCHEST-II, the largest population-based LDCT screening program in Hungary, including the screening and diagnostic outcomes, and the characteristics of the LC cases. METHODS: A total of 4215 high-risk individuals aged between 50 and 75 years with a smoking history of at least 25 pack-years were assigned to undergo LDCT screening. Screening outcomes were determined based on the volume, growth, and volume doubling time of pulmonary nodules or masses. The clinical stage distribution of screen-detected cancers was compared with two independent practice-based databases consisting of unscreened LC patients. RESULTS: The percentage of negative and indeterminate tests at baseline were 74.2% and 21.7%, respectively, whereas the prevalence of positive LDCT results was 4.1%. Overall, 76 LC patients were diagnosed throughout the screening rounds (1.8% of total participants), out of which 62 (1.5%) patients were already identified in the first screening round. The overall positive predictive value of a positive test was 58%. Most screen-detected malignancies were stage I LCs (60.7%), and only 16.4% of all cases could be classified as stage IV disease. The percentage of early-stage malignancies was significantly higher among HUNCHEST-II screen-detected individuals than among the LC patients in the National Koranyi Institute of Pulmonology's archive or the Hungarian Cancer Registry (p < 0.001). CONCLUSIONS: HUNCHEST-II demonstrates that LDCT screening for LC facilitates early diagnosis, thus arguing in favor of introducing systematic LC screening in Hungary. CLINICAL RELEVANCE STATEMENT: HUNCHEST-II is the so-far largest population-based low-dose CT screening program in Hungary. A positive test's overall positive predictive value was 58%, and most screen-detected malignancies were early-stage lesions. These results pave the way for expansive systematic screening in the region. KEY POINTS: • Conducted in 18 medical facilities, HUNCHEST-II is the so far largest population-based low-dose CT screening program in Hungary. • The vast majority of screen-detected malignancies were early-stage lung cancers, and the overall positive predictive value of a positive test was 58%. • HUNCHEST-II facilitates early diagnosis, thus arguing in favor of introducing systematic lung cancer screening in Hungary.

Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approach.

ABSTRACT: Sensitivity to pain shows a remarkable interindividual variance that has been reported to both forecast and accompany various clinical pain conditions. Although pain thresholds have been reported to be associated to brain morphology, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity (as measured with pain thresholds) using structural magnetic resonance imaging-based cortical thickness data from a multicentre data set (3 centres and 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearson r = 0.36, P < 0.0002, R2 = 0.13). The predictions were found to be specific to physical pain thresholds and not biased towards potential confounding effects (eg, anxiety, stress, depression, centre effects, and pain self-evaluation). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus, and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain.

Pain-free resting-state functional brain connectivity predicts individual pain sensitivity.

Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies.

Gray Matter Atrophy Is Primarily Related to Demyelination of Lesions in Multiple Sclerosis: A Diffusion Tensor Imaging MRI Study.

Objective: Cortical pathology, periventricular demyelination, and lesion formation in multiple sclerosis (MS) are related (Hypothesis 1). Factors in the cerebrospinal fluid close to these compartments could possibly drive the parallel processes. Alternatively, the cortical atrophy could be caused by remote axonal transection (Hypothesis 2). Since MRI can differentiate between demyelination and axon loss, we used this imaging modality to investigate the correlation between the pattern of diffusion parameter changes in the periventricular- and deep white matter and the gray matter atrophy. Methods: High-resolution T1-weighted, FLAIR, and diffusion MRI images were acquired in 52 RRMS patients and 50 healthy, age-matched controls. We used EDSS to estimate the clinical disability. We used Tract Based Spatial Statistics to compare diffusion parameters (fractional anisotropy, mean, axial, and radial diffusivity) between groups. We evaluated global brain, white, and gray matter atrophy with SIENAX. Averaged, standard diffusion parameters were calculated in four compartment: periventricular lesioned and normal appearing white matter, non-periventricular lesioned and normal appearing white matter. PLS regression was used to identify which diffusion parameter and in which compartment best predicts the brain atrophy and clinical disability. Results: In our diffusion tensor imaging study compared to controls we found extensive alterations of fractional anisotropy, mean and radial diffusivity and smaller changes of axial diffusivity (maximal p > 0.0002) in patients that suggested demyelination in the lesioned and in the normal appearing white matter. We found significant reduction in total brain, total white, and gray matter (patients: 718.764 ± 14.968, 323.237 ± 7.246, 395.527 ± 8.050 cm3, controls: 791.772 ± 22.692, 355.350 ± 10.929, 436.422 ± 12.011 cm3; mean ± SE), (p < 0.015; p < 0.0001; p < 0.009; respectively) of patients compared to controls. The PLS analysis revealed a combination of demyelination-like diffusion parameters (higher mean and radial diffusivity in patients) in the lesions and in the non-lesioned periventricular white matter, which best predicted the gray matter atrophy (p < 0.001). Similarly, EDSS was best predicted by the radial diffusivity of the lesions and the non-lesioned periventricular white matter, but axial diffusivity of the periventricular lesions also contributed significantly (p < 0.0001). Interpretation: Our investigation showed that gray matter atrophy and white matter demyelination are related in MS but white matter axonal loss does not significantly contribute to the gray matter pathology.

Evidence for Plastic Processes in Migraine with Aura: A Diffusion Weighted MRI Study.

Background: Formerly white matter abnormalities in a mixed group of migraine patients with and without aura were shown. Here, we aimed to explore white matter alterations in a homogeneous group of migraineurs with aura and to delineate possible relationships between white matter changes and clinical variables. Methods: Eighteen patients with aura, 25 migraine patients without aura and 28 controls were scanned on a 1.5T MRI scanner. Diffusivity parameters of the white matter were estimated and compared between patients' groups and controls using whole-brain tract-based spatial statistics. Results: Decreased radial diffusivity (p < 0.036) was found bilaterally in the parieto-occipital white matter, the corpus callosum, and the cingular white matter of migraine with aura (MwA) patients compared to controls. Migraine without aura (MwoA) patients showed no alteration compared to controls. MwA compared to MwoA showed increased fractional anisotropy (p < 0.048) in the left parieto-occipital white matter. In MwA a negative correlation was found between axial diffusivity and disease duration in the left superior longitudinal fascicle (left parieto-occipital region) and in the left corticospinal tract (p < 0.036) and with the number of the attacks in the right superior longitudinal fascicle (p < 0.048). Conclusion: We showed for the first time that there are white matter microstructural differences between these two subgroups of migraine and hence it is important to handle the two groups separately in further researches. We propose that degenerative and maladaptive plastic changes coexist in the disease and the diffusion profile is a result of these processes.

Cortical and subcortical atrophy in Alzheimer disease: parallel atrophy of thalamus and hippocampus.

Brain atrophy is a key imaging hallmark of Alzheimer disease (AD). In this study, we carried out an integrative evaluation of AD-related atrophy. Twelve patients with AD and 13 healthy controls were enrolled. We conducted a cross-sectional analysis of total brain tissue volumes with SIENAX. Localized gray matter atrophy was identified with optimized voxel-wise morphometry (FSL-VBM), and subcortical atrophy was evaluated by active shape model implemented in FMRIB's Integrated Registration Segmentation Toolkit. SIENAX analysis demonstrated total brain atrophy in AD patients; voxel-based morphometry analysis showed atrophy in the bilateral mediotemporal regions and in the posterior brain regions. In addition, regarding the diminished volumes of thalami and hippocampi in AD patients, subsequent vertex analysis of the segmented structures indicated shrinkage of the bilateral anterior thalami and the left medial hippocampus. Interestingly, the volume of the thalami and hippocampi were highly correlated with the volume of the thalami and amygdalae on both sides in AD patients, but not in healthy controls. This complex structural information proved useful in the detailed interpretation of AD-related neurodegenerative process, as the multilevel approach showed both global and local atrophy on cortical and subcortical levels. Most importantly, our results raise the possibility that subcortical structure atrophy is not independent in AD patients.

Structural and functional bases for individual differences in motor learning.

People vary in their ability to learn new motor skills. We hypothesize that between-subject variability in brain structure and function can explain differences in learning. We use brain functional and structural MRI methods to characterize such neural correlates of individual variations in motor learning. Healthy subjects applied isometric grip force of varying magnitudes with their right hands cued visually to generate smoothly-varying pressures following a regular pattern. We tested whether individual variations in motor learning were associated with anatomically colocalized variations in magnitude of functional MRI (fMRI) signal or in MRI differences related to white and grey matter microstructure. We found that individual motor learning was correlated with greater functional activation in the prefrontal, premotor, and parietal cortices, as well as in the basal ganglia and cerebellum. Structural MRI correlates were found in the premotor cortex [for fractional anisotropy (FA)] and in the cerebellum [for both grey matter density and FA]. The cerebellar microstructural differences were anatomically colocalized with fMRI correlates of learning. This study thus suggests that variations across the population in the function and structure of specific brain regions for motor control explain some of the individual differences in skill learning. This strengthens the notion that brain structure determines some limits to cognitive function even in a healthy population. Along with evidence from pathology suggesting a role for these regions in spontaneous motor recovery, our results also highlight potential targets for therapeutic interventions designed to maximize plasticity for recovery of similar visuomotor skills after brain injury.