Refine
Year of publication
Has Fulltext
- yes (33)
Is part of the Bibliography
- no (33)
Keywords
- Artificial intelligence (2)
- CT (2)
- Machine learning (2)
- Prostate cancer (2)
- Radiomics (2)
- Acer pseudoplatanus (1)
- Anemia (1)
- Angiography (1)
- Blood (1)
- Brain metastasis (1)
Institute
- Medizin (14)
- Physik (9)
- Informatik (5)
- Biowissenschaften (4)
- Frankfurt Institute for Advanced Studies (FIAS) (4)
- Informatik und Mathematik (3)
- ELEMENTS (2)
- Geowissenschaften (2)
- Institut für Ökologie, Evolution und Diversität (2)
- Biochemie und Chemie (1)
- Biodiversität und Klima Forschungszentrum (BiK-F) (1)
- Exzellenzcluster Makromolekulare Komplexe (1)
- Kulturwissenschaften (1)
- MPI für Biophysik (1)
- Sonderforschungsbereiche / Forschungskollegs (1)
- Zentrum für Biomolekulare Magnetische Resonanz (BMRZ) (1)
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Background Anti-angiogenic treatment is believed to have at least cystostatic effects in highly vascularized tumours like pancreatic cancer. In this study, the treatment effects of the angiogenesis inhibitor Cilengitide and gemcitabine were compared with gemcitabine alone in patients with advanced unresectable pancreatic cancer. Methods A multi-national, open-label, controlled, randomized, parallel-group, phase II pilot study was conducted in 20 centers in 7 countries. Cilengitide was administered at 600 mg/m2 twice weekly for 4 weeks per cycle and gemcitabine at 1000 mg/m2 for 3 weeks followed by a week of rest per cycle. The planned treatment period was 6 four-week cycles. The primary endpoint of the study was overall survival and the secondary endpoints were progression-free survival (PFS), response rate, quality of life (QoL), effects on biological markers of disease (CA 19.9) and angiogenesis (vascular endothelial growth factor and basic fibroblast growth factor), and safety. An ancillary study investigated the pharmacokinetics of both drugs in a subset of patients. Results Eighty-nine patients were randomized. The median overall survival was 6.7 months for Cilengitide and gemcitabine and 7.7 months for gemcitabine alone. The median PFS times were 3.6 months and 3.8 months, respectively. The overall response rates were 17% and 14%, and the tumor growth control rates were 54% and 56%, respectively. Changes in the levels of CA 19.9 went in line with the clinical course of the disease, but no apparent relationships were seen with the biological markers of angiogenesis. QoL and safety evaluations were comparable between treatment groups. Pharmacokinetic studies showed no influence of gemcitabine on the pharmacokinetic parameters of Cilengitide and vice versa. Conclusion There were no clinically important differences observed regarding efficacy, safety and QoL between the groups. The observations lay in the range of other clinical studies in this setting. The combination regimen was well tolerated with no adverse effects on the safety, tolerability and pharmacokinetics of either agent.
An experiment addressing electron capture (EC) decay of hydrogen-like 142Pm60+ions has been conducted at the experimental storage ring (ESR) at GSI. The decay appears to be purely exponential and no modulations were observed. Decay times for about 9000 individual EC decays have been measured by applying the single-ion decay spectroscopy method. Both visually and automatically analysed data can be described by a single exponential decay with decay constants of 0.0126(7)s−1 for automatic analysis and 0.0141(7)s−1 for manual analysis. If a modulation superimposed on the exponential decay curve is assumed, the best fit gives a modulation amplitude of merely 0.019(15), which is compatible with zero and by 4.9 standard deviations smaller than in the original observation which had an amplitude of 0.23(4).
NeuLAND (New Large-Area Neutron Detector) is the next-generation neutron detector for the R3B (Reactions with Relativistic Radioactive Beams) experiment at FAIR (Facility for Antiproton and Ion Research). NeuLAND detects neutrons with energies from 100 to 1000 MeV, featuring a high detection efficiency, a high spatial and time resolution, and a large multi-neutron reconstruction efficiency. This is achieved by a highly granular design of organic scintillators: 3000 individual submodules with a size of 5 × 5 × 250 cm3 are arranged in 30 double planes with 100 submodules each, providing an active area of 250 × 250 cm2 and a total depth of 3 m. The spatial resolution due to the granularity together with a time resolution of 150 ps ensures high-resolution capabilities. In conjunction with calorimetric properties, a multi-neutron reconstruction efficiency of 50% to 70% for four-neutron events will be achieved, depending on both the emission scenario and the boundary conditions allowed for the reconstruction method. We present in this paper the final design of the detector as well as results from test measurements and simulations on which this design is based.
In March 2019 the HADES experiment recorded 14 billion Ag+Ag collisions at √sNN = 2.55 GeV as a part of the FAIR phase-0 physics program. In this contribution, we present and investigate our capabilities to reconstruct and analyze weakly decaying strange hadrons and hypernuclei emerging from these collisions. The focus is put on measuring the mean lifetimes of these particles.
Radiative transition of an excited baryon to a nucleon with emission of a virtual massive photon converting to dielectron pair (Dalitz decays) provides important information about baryon-photon coupling at low q2 in timelike region. A prominent enhancement in the respective electromagnetic transition Form Factors (etFF) at q2 near vector mesons ρ/ω poles has been predicted by various calculations reflecting strong baryon-vector meson couplings. The understanding of these couplings is also of primary importance for the interpretation of the emissivity of QCD matter studied in heavy ion collisions via dilepton emission. Dedicated measurements of baryon Dalitz decays in proton-proton and pion-proton scattering with HADES detector at GSI/FAIR are presented and discussed. The relevance of these studies for the interpretation of results obtained from heavy ion reactions is elucidated on the example of the HADES results.
The majority of bacterial membrane-bound NiFe-hydrogenases and formate dehydrogenases have homologous membrane-integral cytochrome b subunits. The prototypic NiFe-hydrogenase of Wolinella succinogenes (HydABC complex) catalyzes H2 oxidation by menaquinone during anaerobic respiration and contains a membrane-integral cytochrome b subunit (HydC) that carries the menaquinone reduction site. Using the crystal structure of the homologous FdnI subunit of Escherichia coli formate dehydrogenase-N as a model, the HydC protein was modified to examine residues thought to be involved in menaquinone binding. Variant HydABC complexes were produced in W. succinogenes, and several conserved HydC residues were identified that are essential for growth with H2 as electron donor and for quinone reduction by H2. Modification of HydC with a C-terminal Strep-tag II enabled one-step purification of the HydABC complex by Strep-Tactin affinity chromatography. The tagged HydC, separated from HydAB by isoelectric focusing, was shown to contain 1.9 mol of heme b/mol of HydC demonstrating that HydC ligates both heme b groups. The four histidine residues predicted as axial heme b ligands were individually replaced by alanine in Strep-tagged HydC. Replacement of either histidine ligand of the heme b group proximal to HydAB led to HydABC preparations that contained only one heme b group. This remaining heme b could be completely reduced by quinone supporting the view that the menaquinone reduction site is located near the distal heme b group. The results indicate that both heme b groups are involved in electron transport and that the architecture of the menaquinone reduction site near the cytoplasmic side of the membrane is similar to that proposed for E. coli FdnI.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
Predicting adult Attention Deficit Hyperactivity Disorder (ADHD) using vocal acoustic features
(2021)
Background: It is a key concern in psychiatric research to investigate objective measures to support and ultimately improve diagnostic processes. Current gold standard diagnostic procedures for attention deficit hyperactivity disorder (ADHD) are mainly subjective and prone to bias. Objective measures such as neuropsychological measures and EEG markers show limited specificity. Recent studies point to alterations of voice and speech production to reflect psychiatric symptoms also related to ADHD. However, studies investigating voice in large clinical samples allowing for individual-level prediction of ADHD are lacking. The aim of this study was to explore a role of prosodic voice measures as objective marker of ADHD.
Methods: 1005 recordings were analyzed from 387 ADHD patients, 204 healthy controls, and 100 clinical (psychiatric) controls. All participants (age range 18-59 years, mean age 34.4) underwent an extensive diagnostic examination according to gold standard methods and provided speech samples (3 min in total) including free and given speech. Paralinguistic features were calculated, and random forest based classifications were performed using a 10-fold cross-validation with 100 repetitions controlling for age, sex, and education. Association of voice features and ADHD-symptom severity assessed in the clinical interview were analyzed using random forest regressions.
Results and Conclusion ADHD was predicted with AUC = 0.76. The analysis of a non-comorbid sample of ADHD resulted in similar classification performance. Paralinguistic features were associated with ADHD-symptom severity as indicated by random forest regression. In female participants, particularly with age < 32 years, paralinguistic features showed the highest classification performance (AUC = 0.86).
Paralinguistic features based on derivatives of loudness and fundamental frequency seem to be promising candidates for further research into vocal acoustic biomarkers of ADHD. Given the relatively good performance in female participants independent of comorbidity, vocal measures may evolve as a clinically supportive option in the complex diagnostic process in this patient group.
Competing Interest Statement: EA participated and received payments in the national advisory board ADHD of Shire/Takeda. JL is co-founder and CTO of PeakProfiling GmbH. He created audio-features used in this study, that are intellectual property of PeakProfiling GmbH. FH received payments by PeakProfiling GmbH.
Clinical Trial: NCT01104623
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
ALICE (A Large Ion Collider Experiment) is studying the physics of strongly interacting matter, and in particular the properties of the Quark–Gluon Plasma (QGP), using proton–proton, proton–nucleus and nucleus–nucleus collisions at the CERN LHC (Large Hadron Collider). The ALICE Collaboration is preparing a major upgrade of the experimental apparatus, planned for installation in the second long LHC shutdown in the years 2018–2019. These plans are presented in the ALICE Upgrade Letter of Intent, submitted to the LHCC (LHC experiments Committee) in September 2012. In order to fully exploit the physics reach of the LHC in this field, high-precision measurements of the heavy-flavour production, quarkonia, direct real and virtual photons, and jets are necessary. This will be achieved by an increase of the LHC Pb–Pb instant luminosity up to 6×1027 cm−2s−1 and running the ALICE detector with the continuous readout at the 50 kHz event rate. The physics performance accessible with the upgraded detector, together with the main detector modifications, are presented.