210 research outputs found
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
Purpose: As an important branch of machine learning pipelines in medical
imaging, radiomics faces two major challenges namely reproducibility and
accessibility. In this work, we introduce open-radiomics, a set of radiomics
datasets along with a comprehensive radiomics pipeline based on our proposed
technical protocol to investigate the effects of radiomics feature extraction
on the reproducibility of the results.
Materials and Methods: Experiments are conducted on BraTS 2020 open-source
Magnetic Resonance Imaging (MRI) dataset that includes 369 adult patients with
brain tumors (76 low-grade glioma (LGG), and 293 high-grade glioma (HGG)).
Using PyRadiomics library for LGG vs. HGG classification, 288 radiomics
datasets are formed; the combinations of 4 MRI sequences, 3 binWidths, 6 image
normalization methods, and 4 tumor subregions.
Random Forest classifiers were used, and for each radiomics dataset the
training-validation-test (60%/20%/20%) experiment with different data splits
and model random states was repeated 100 times (28,800 test results) and Area
Under Receiver Operating Characteristic Curve (AUC) was calculated.
Results: Unlike binWidth and image normalization, tumor subregion and imaging
sequence significantly affected performance of the models. T1 contrast-enhanced
sequence and the union of necrotic and the non-enhancing tumor core subregions
resulted in the highest AUCs (average test AUC 0.951, 95% confidence interval
of (0.949, 0.952)). Although 28 settings and data splits yielded test AUC of 1,
they were irreproducible.
Conclusion: Our experiments demonstrate the sources of variability in
radiomics pipelines (e.g., tumor subregion) can have a significant impact on
the results, which may lead to superficial perfect performances that are
irreproducible
VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI
BACKGROUND Objective and volumetric quantification is a necessary step in the assessment and comparison of endolymphatic hydrops (ELH) results. Here, we introduce a novel tool for automatic volumetric segmentation of the endolymphatic space (ELS) for ELH detection in delayed intravenous gadolinium-enhanced magnetic resonance imaging of inner ear (iMRI) data.
METHODS The core component is a novel algorithm based on Volumetric Local Thresholding (VOLT). The study included three different data sets: a real-world data set (D1) to develop the novel ELH detection algorithm and two validating data sets, one artificial (D2) and one entirely unseen prospective real-world data set (D3). D1 included 210 inner ears of 105 patients (50 male; mean age 50.4 ± 17.1 years), and D3 included 20 inner ears of 10 patients (5 male; mean age 46.8 ± 14.4 years) with episodic vertigo attacks of different etiology. D1 and D3 did not differ significantly concerning age, gender, the grade of ELH, or data quality. As an artificial data set, D2 provided a known ground truth and consisted of an 8-bit cuboid volume using the same voxel-size and grid as real-world data with different sized cylindrical and cuboid-shaped cutouts (signal) whose grayscale values matched the real-world data set D1 (mean 68.7 ± 7.8; range 48.9-92.8). The evaluation included segmentation accuracy using the Sørensen-Dice overlap coefficient and segmentation precision by comparing the volume of the ELS.
RESULTS VOLT resulted in a high level of performance and accuracy in comparison with the respective gold standard. In the case of the artificial data set, VOLT outperformed the gold standard in higher noise levels. Data processing steps are fully automated and run without further user input in less than 60 s. ELS volume measured by automatic segmentation correlated significantly with the clinical grading of the ELS (p < 0.01).
CONCLUSION VOLT enables an open-source reproducible, reliable, and automatic volumetric quantification of the inner ears' fluid space using MR volumetric assessment of endolymphatic hydrops. This tool constitutes an important step towards comparable and systematic big data analyses of the ELS in patients with the frequent syndrome of episodic vertigo attacks. A generic version of our three-dimensional thresholding algorithm has been made available to the scientific community via GitHub as an ImageJ-plugin
Beyond binary parcellation of the vestibular cortex - A dataset
The data-set presented in this data article is supplementary to the original publication, doi:10.1016/j.neuroimage.2018.05.018 (Kirsch et al., 2018). Named article describes handedness-dependent organizational patterns of functional subunits within the human vestibular cortical network that were revealed by functional magnetic resonance imaging (fMRI) connectivity parcellation. 60 healthy volunteers (30 left-handed and 30 right-handed) were examined on a 3T MR scanner using resting state fMRI. The multisensory (non-binary) nature of the human (vestibular) cortex was addressed by using masked binary and non-binary variations of independent component analysis (ICA). The data have been made publicly available via github (https://github.com/RainerBoegle/BeyondBinar yParcellationData). (C) 2019 The Authors. Published by Elsevier Inc
Increased rate of significant findings on brain MRI during the early stage of the COVID-19 pandemic
Associations between aerobic fitness and brain structure in schizophrenia with a focus on hippocampal formation subfield volume [Abstract]
Different Imaging Strategies in Patients With Possible Basilar Artery Occlusion Cost-Effectiveness Analysis
Background and Purpose-This study evaluated the cost-effectiveness of different noninvasive imaging strategies in patients with possible basilar artery occlusion. Methods-A Markov decision analytic model was used to evaluate long-term outcomes resulting from strategies using computed tomographic angiography (CTA), magnetic resonance imaging, nonenhanced CT, or duplex ultrasound with intravenous (IV) thrombolysis being administered after positive findings. The analysis was performed from the societal perspective based on US recommendations. Input parameters were derived from the literature. Costs were obtained from United States costing sources and published literature. Outcomes were lifetime costs, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios, an
Animal tissue-based quantitative comparison of dual-energy CT to SPR conversion methods using high-resolution gel dosimetry
Dual-energy computed tomography (DECT) has been shown to allow for more accurate ion therapy treatment planning by improving the estimation of tissue stopping power ratio (SPR) relative to water, among other tissue properties. In this study, we measured and compared the accuracy of SPR values derived using both dual- and single-energy CT (SECT) based on different published conversion algorithms. For this purpose, a phantom setup containing either fresh animal soft tissue samples (beef, pork) and a water reference or tissue equivalent plastic materials was designed and irradiated in a clinical proton therapy facility. Dosimetric polymer gel was positioned downstream of the samples to obtain a three-dimensional proton range distribution with high spatial resolution. The mean proton range in gel for each tissue relative to the water sample was converted to a SPR value. Additionally, the homogeneous samples were probed with a variable water column encompassed by two ionization chambers to benchmark the SPR accuracy of the gel dosimetry. The SPR values measured with both methods were consistent with a mean deviation of 0.2%, but the gel dosimetry captured range variations up to 5 mm within individual samples.
Across all fresh tissue samples the SECT approach yielded significantly greater mean absolute deviations from the SPR deduced using gel range measurements, with an average difference of 1.2%, compared to just 0.3% for the most accurate DECT-based algorithm. These results show a significant advantage of DECT over SECT for stopping power prediction in a realistic setting, and for the first time allow to compare a large set of methods under the same conditions
Exploring effects of exercise on the functional connectome in patients with schizophrenia [Abstract]
Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles
BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (p < 0.0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (p = 0.002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy
Association between composite scores of domain-specific cognitive functions and regional patterns of atrophy and functional connectivity in the Alzheimer's disease spectrum
Background: Cognitive decline has been found to be associated with gray matter atrophy and disruption of
functional neural networks in Alzheimer’s disease (AD) in structural and functional imaging (fMRI) studies. Most
previous studies have used single test scores of cognitive performance among monocentric cohorts. However,
cognitive domain composite scores could be more reliable than single test scores due to the reduction of measurement error. Adopting a multicentric resting state fMRI (rs-fMRI) and cognitive domain approach, we provide
a comprehensive description of the structural and functional correlates of the key cognitive domains of AD.
Method: We analyzed MRI, rs-fMRI and cognitive domain score data of 490 participants from an interim baseline
release of the multicenter DELCODE study cohort, including 54 people with AD, 86 with Mild Cognitive
Impairment (MCI), 175 with Subjective Cognitive Decline (SCD), and 175 Healthy Controls (HC) in the ADspectrum. Resulting cognitive domain composite scores (executive, visuo-spatial, memory, working memory
and language) from the DELCODE neuropsychological battery (DELCODE-NP), were previously derived using
confirmatory factor analysis. Statistical analyses examined the differences between diagnostic groups, and the
association of composite scores with regional atrophy and network-specific functional connectivity among the
patient subgroup of SCD, MCI and AD.
Result: Cognitive performance, atrophy patterns and functional connectivity significantly differed between
diagnostic groups in the AD-spectrum. Regional gray matter atrophy was positively associated with visuospatial
and other cognitive impairments among the patient subgroup in the AD-spectrum. Except for the visual network,
patterns of network-specific resting-state functional connectivity were positively associated with distinct
cognitive impairments among the patient subgroup in the AD-spectrum.
Conclusion: Consistent associations between cognitive domain scores and both regional atrophy and networkspecific functional connectivity (except for the visual network), support the utility of a multicentric and
cognitive domain approach towards explicating the relationship between imaging markers and cognition in the
AD-spectrum
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