Neurocognitive functioning and radiologic changes in primary CNS lymphoma patients:results from the HOVON 105/ALLG NHL 24 randomized controlled trial

BACKGROUND: To analyze the effect of treatment on neurocognitive functioning and the association of neurocognition with radiological abnormalities in primary central nervous system lymphoma (PCNSL). METHODS: One hundred and ninety-nine patients from a phase III trial (HOVON 105/ALLG NHL 24), randomized to standard chemotherapy with or without rituximab, followed in patients ≤60 years old by 30-Gy whole-brain radiotherapy (WBRT), were asked to participate in a neuropsychological evaluation before and during treatment, and up to 2 years posttreatment. Scores were transformed into a standardized z-score; clinically relevant changes were defined as a change in z-score of ≥1 SD. The effect of WBRT was analyzed in irradiated patients. All MRIs were centrally assessed for white matter abnormalities and cerebral atrophy, and their relation with neurocognitive scores over time in each domain was calculated. RESULTS: 125/199 patients consented to neurocognitive evaluation. Statistically significant improvements in neurocognition were seen in all domains. A clinically relevant improvement was seen only in the motor speed domain, without differences between the arms. In the follow-up of irradiated patients (n = 43), no change was observed in any domain score, compared to after WBRT. Small but significant inverse correlations were found between neurocognitive scores over time and changes in white matter abnormalities (regression coefficients: −0.048 to −0.347) and cerebral atrophy (−0.212 to −1.774). CONCLUSIONS: Addition of rituximab to standard treatment in PCNSL patients did not impact neurocognitive functioning up to 2 years posttreatment, nor did treatment with 30-Gy WBRT in patients ≤60 years old. Increased white matter abnormalities and brain atrophy showed weak associations with neurocognition

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Feature-space transformation improves supervised segmentation across scanners

Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques. We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner. We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used

Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners

Many successful approaches in MR brain segmentation use supervised voxel classification, which requires manually labeled training images that are representative of the test images to segment. However, the performance of such methods often deteriorates if training and test images are acquired with different scanners or scanning parameters, since this leads to differences in feature representations between training and test data.In this paper we propose a feature-space transformation (FST) to overcome such differences in feature representations. The proposed FST is derived from unlabeled images of a subject that was scanned with both the source and the target scan protocol. After an affine registration, these images give a mapping between source and target voxels in the feature space. This mapping is then used to map all training samples to the feature representation of the test samples.We evaluated the benefit of the proposed FST on hippocampus segmentation. Experiments were performed on two datasets: one with relatively small differences between training and test images and one with large differences. In both cases, the FST significantly improved the performance compared to using only image normalization. Additionally, we showed that our FST can be used to improve the performance of a state-of-the-art patch-based-atlas-fusion technique in case of large differences between scanners. Keywords: Classification, Domain adaptation, Hippocampus, MRI, Segmentation, Transfer learnin

Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia

Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range=86-91%) than all other approaches (AUC=57-84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC=91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself