Outcomes of pediatric patients with therapy-related myeloid neoplasms

Long-term outcomes after allogeneic hematopoietic cell transplantation (HCT) for therapy-related myeloid neoplasms (tMNs) are dismal. There are few multicenter studies defining prognostic factors in pediatric patients with tMNs. We have accumulated the largest cohort of pediatric patients who have undergone HCT for a tMN to perform a multivariate analysis defining factors predictive of long-term survival. Sixty-eight percent of the 401 patients underwent HCT using a myeloablative conditioning (MAC) regimen, but there were no statistically significant differences in the overall survival (OS), event-free survival (EFS), or cumulative incidence of relapse and non-relapse mortality based on the conditioning intensity. Among the recipients of MAC regimens, 38.4% of deaths were from treatment-related causes, especially acute graft versus host disease (GVHD) and end-organ failure, as compared to only 20.9% of deaths in the reduced-intensity conditioning (RIC) cohort. Exposure to total body irradiation (TBI) during conditioning and experiencing grade III/IV acute GVHD was associated with worse OS. In addition, a diagnosis of therapy-related myelodysplastic syndrome and having a structurally complex karyotype at tMN diagnosis were associated with worse EFS. Reduced-toxicity (but not reduced-intensity) regimens might help to decrease relapse while limiting mortality associated with TBI-based HCT conditioning in pediatric patients with tMNs

Observations and modelling of ion cyclotron emission observed in JET plasmas using a sub-harmonic arc detection system during ion cyclotron resonance heating

Peer reviewe

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox

Cerebral perfusion using ASL in patients with COVID-19 and neurological manifestations: A retrospective multicenter observational study

Background and purpose: Cerebral hypoperfusion has been reported in patients with COVID-19 and neurological manifestations in small cohorts. We aimed to systematically assess changes in cerebral perfusion in a cohort of 59 of these patients, with or without abnormalities on morphological MRI sequences. Methods: Patients with biologically-confirmed COVID-19 and neurological manifestations undergoing a brain MRI with technically adequate arterial spin labeling (ASL) perfusion were included in this retrospective multicenter study. ASL maps were jointly reviewed by two readers blinded to clinical data. They assessed abnormal perfusion in four regions of interest in each brain hemisphere: frontal lobe, parietal lobe, posterior temporal lobe, and temporal pole extended to the amygdalo-hippocampal complex. Results: Fifty-nine patients (44 men (75%), mean age 61.2 years) were included. Most patients had a severe COVID-19, 57 (97%) needed oxygen therapy and 43 (73%) were hospitalized in intensive care unit at the time of MRI. Morphological brain MRI was abnormal in 44 (75%) patients. ASL perfusion was abnormal in 53 (90%) patients, and particularly in all patients with normal morphological MRI. Hypoperfusion occurred in 48 (81%) patients, mostly in temporal poles (52 (44%)) and frontal lobes (40 (34%)). Hyperperfusion occurred in 9 (15%) patients and was closely associated with post-contrast FLAIR leptomeningeal enhancement (100% [66.4%-100%] of hyperperfusion with enhancement versus 28.6% [16.6%-43.2%] without, p = 0.002). Studied clinical parameters (especially sedation) and other morphological MRI anomalies had no significant impact on perfusion anomalies. Conclusion: Brain ASL perfusion showed hypoperfusion in more than 80% of patients with severe COVID-19, with or without visible lesion on conventional MRI abnormalities