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

Depiction of pneumothoraces in a large animal model using x-ray dark-field radiography

The aim of this study was to assess the diagnostic value of x-ray dark-field radiography to detect pneumothoraces in a pig model. Eight pigs were imaged with an experimental grating-based large-animal dark-field scanner before and after induction of a unilateral pneumothorax. Image contrast-to-noise ratios between lung tissue and the air-filled pleural cavity were quantified for transmission and dark-field radiograms. The projected area in the object plane of the inflated lung was measured in dark-field images to quantify the collapse of lung parenchyma due to a pneumothorax. Means and standard deviations for lung sizes and signal intensities from dark-field and transmission images were tested for statistical significance using Student’s two-tailed t-test for paired samples. The contrast-to-noise ratio between the air-filled pleural space of lateral pneumothoraces and lung tissue was significantly higher in the dark-field (3.65 ± 0.9) than in the transmission images (1.13 ± 1.1; p = 0.002). In case of dorsally located pneumothoraces, a significant decrease (−20.5%; p > 0.0001) in the projected area of inflated lung parenchyma was found after a pneumothorax was induced. Therefore, the detection of pneumothoraces in x-ray dark-field radiography was facilitated compared to transmission imaging in a large animal model

Hippocampal and Hippocampal-Subfield Volumes From Early-Onset Major Depression and Bipolar Disorder to Cognitive Decline

Background: The hippocampus and its subfields (HippSub) are reported to be diminished in patients with Alzheimer's disease (AD), bipolar disorder (BD), and major depressive disorder (MDD). We examined these groups vs healthy controls (HC) to reveal HippSub alterations between diseases. Methods: We segmented 3T-MRI T2-weighted hippocampal images of 67 HC, 58 BD, and MDD patients from the AFFDIS study and 137 patients from the DELCODE study assessing cognitive decline, including subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI), and AD, via Free Surfer 6.0 to compare volumes across groups. Results: Groups differed significantly in several HippSub volumes, particularly between patients with AD and mood disorders. In comparison to HC, significant lower volumes appear in aMCI and AD groups in specific subfields. Smaller volumes in the left presubiculum are detected in aMCI and AD patients, differing from the BD group. A significant linear regression is seen between left hippocampus volume and duration since the first depressive episode. Conclusions: HippSub volume alterations were observed in AD, but not in early-onset MDD and BD, reinforcing the notion of different neural mechanisms in hippocampal degeneration. Moreover, duration since the first depressive episode was a relevant factor explaining the lower left hippocampal volumes present in groups

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

The effect of radio-adaptive doses on HT29 and GM637 cells

<p>Abstract</p> <p>Background</p> <p>The shape of the dose-response curve at low doses differs from the linear quadratic model. The effect of a radio-adaptive response is the centre of many studies and well known inspite that the clinical applications are still rarely considered.</p> <p>Methods</p> <p>We studied the effect of a low-dose pre-irradiation (0.03 Gy – 0.1 Gy) alone or followed by a 2.0 Gy challenging dose 4 h later on the survival of the HT29 cell line (human colorectal cancer cells) and on the GM637 cell line (human fibroblasts).</p> <p>Results</p> <p>0.03 Gy given alone did not have a significant effect on both cell lines, the other low doses alone significantly reduced the cell survival. Applied 4 h before the 2.0 Gy fraction, 0.03 Gy led to a significant induced radioresistance in GM637 cells, but not in HT29 cells, and 0.05 Gy led to a significant hyperradiosensitivity in HT29 cells, but not in GM637 cells.</p> <p>Conclusion</p> <p>A pre-irradiation with 0.03 Gy can protect normal fibroblasts, but not colorectal cancer cells, from damage induced by an irradiation of 2.0 Gy and the application of 0.05 Gy prior to the 2.0 Gy fraction can enhance the cell killing of colorectal cancer cells while not additionally damaging normal fibroblasts. If these findings prove to be true in vivo as well this may optimize the balance between local tumour control and injury to normal tissue in modern radiotherapy.</p

Depiction of pneumothoraces in a large animal model using x-ray dark-field radiography

The aim of this study was to assess the diagnostic value of x-ray dark-field radiography to detect pneumothoraces in a pig model. Eight pigs were imaged with an experimental grating-based large-animal dark-field scanner before and after induction of a unilateral pneumothorax. Image contrast-tonoise ratios between lung tissue and the air-filled pleural cavity were quantified for transmission and dark-field radiograms. The projected area in the object plane of the inflated lung was measured in dark-field images to quantify the collapse of lung parenchyma due to a pneumothorax. Means and standard deviations for lung sizes and signal intensities from dark-field and transmission images were tested for statistical significance using Student's two-tailed t-test for paired samples. The contrast-to-noise ratio between the air-filled pleural space of lateral pneumothoraces and lung tissue was significantly higher in the dark-field (3.65 +/- 0.9) than in the transmission images (1.13 +/- 1.1;p = 0.002). In case of dorsally located pneumothoraces, a significant decrease (-20.5%;p > 0.0001) in the projected area of inflated lung parenchyma was found after a pneumothorax was induced. Therefore, the detection of pneumothoraces in x-ray dark-field radiography was facilitated compared to transmission imaging in a large animal model

Volumetric Analysis of Hearing-Related Structures of Brain in Children with GJB2-Related Congenital Deafness

Background: Children with non-syndromic hereditary sensorineural hearing loss (SNHL) provide an opportunity to explore the impact of hearing on brain development. Objective: This study investigates volumetric differences of key hearing-related structures in children with gap junction protein beta 2 GJB2-related SNHL compared to controls. Materials and methods: Ninety-four children with SNHL (n = 15) or normal hearing (n = 79) were studied using automated volumetric segmentation. Heschl&rsquo;s gyrus (HG), anterior HG (aHG), planum temporale (PT), medial geniculate nucleus (MGN), and nucleus accumbens (NA) were analyzed relative to total brain volume (TBV) at two different age groups: (1) 7&ndash;12 months and (2) 13 months&ndash;18 years. Two-sided t-tests were used to evaluate differences between groups. Differences were considered significant if p &lt; 0.007. Results: Significantly smaller aHG-to-TBV ratios were found in 13-month-to-18-year-old patients (p &lt; 0.0055). HG-, PT-, MGN-, and NA-to-TBV ratios were smaller in the same age group, without reaching a significant level. Conversely, HG- and NA-to-TBV were larger in the younger age group. No significant differences were found between the groups for age and TBV. Conclusions: In this exploratory volumetric analysis of key hearing-related structures, we observed age-related changes in volume in children with GJB2-related SNHL