OBJECTIVE\nMETHODS\nRESULTS\nCONCLUSIONS\nSIGNIFICANCE\nDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time... Show moreOBJECTIVE\nMETHODS\nRESULTS\nCONCLUSIONS\nSIGNIFICANCE\nDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.\nEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).\nDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.\nAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance.\nIn the future, machine learning algorithms may help improve the diagnostic accuracy of EMG examinations. Show less
Tannemaat, M.R.; Kefalas, M.; Geraedts, V.J.; Remijn-Nelissen, L.; Verschuuren, A.J.M.; Koch, M.; ... ; Bäck, T.H.W. 2022
ObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of... Show moreObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).ResultsDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.ConclusionsAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance. Show less
Yang, F.; Kefalas, M.; Koch, M.; Kononova, A.V.; Qiao, Y.; Bäck, T.H.W. 2022
Background Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine... Show moreBackground Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.Objective To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.Methods Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation.Results Tweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited.Conclusions Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. (c) 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society Show less
Objective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker... Show moreObjective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.Methods: A fully automated ML pipeline was applied to 112 PD patients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance.Results: Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (b =-0.23 (95% confidence interval (-0.29,-0.18))).Conclusions: Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PD patients according to cognition, rather than a single spectral EEG feature.Significance: Automated EEG assessment may have utility for cognitive profiling of PD patients during the DBS screening. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Herein we report the design and synthesis of a series of highly selective CCR2 antagonists as18F‐labeled PET tracers. The derivatives were evaluated extensively for their off-target profile at 48... Show moreHerein we report the design and synthesis of a series of highly selective CCR2 antagonists as18F‐labeled PET tracers. The derivatives were evaluated extensively for their off-target profile at 48 different targets. The most potent and selective candidate was applied in vivo in a biodistribution study, demonstrating a promising profile for further preclinical development. This compound represents the first potential nonpeptidic PET tracer for the imaging of CCR2 receptors. Show less
Machine Learning is becoming a more and more substantial technology for industry. It allows to learn from previous experiences in an automated way to make decisions based on the learned behavior.... Show moreMachine Learning is becoming a more and more substantial technology for industry. It allows to learn from previous experiences in an automated way to make decisions based on the learned behavior. Machine learning enables the development of completely new products like autonomous driving or services which are purely driven by data.The development of such new data-driven products is often a long procedure. Even the application of machine learning algorithms to specific problems is mostly not straightforward. To illustrate this, a data-driven service, called Automated Damage Assessment, from the automotive industry is introduced in this work. Based on the gained experience from such data-driven service developments, this dissertation proposes a methodology to develop data-driven services in an accurate and fast manner. The Automated Damage Assessment service is based on sensor data, i.e. data recorded from vehicle on-board sensors over time. Using such time series from more than one sensor results in a multivariate time series. The existent methods to solve multivariate time series classification-problems are often complex and developed for specific problems without being scalable. To overcome this, suitable approaches with different complexities are proposed in this work. These approaches are applied on multiple publicly available data sets and on real-world data sets from medical and industrial domain with the result that especially two AutoML (Automated Machine Learning) approaches, namely GAMA and ATM, as well as one of the proposed approaches (PHCP) are most suitable to solve these particular multivariate time series problems. Show less
Kefalas, M.; Koch, M.; Geraedts, V.J.; Wang, H.; Tannemaat, M.; Bäck, T.H.W. 2020
Needle electromyography (EMG) is a common technique used in clinical neurophysiology to record the electrical activity of muscles at different levels of activation. It can be used to diagnose... Show moreNeedle electromyography (EMG) is a common technique used in clinical neurophysiology to record the electrical activity of muscles at different levels of activation. It can be used to diagnose various neurological/muscular disorders, as the EMG signals of patients with both nerve diseases (neuropathies) andmuscle diseases (myopathies) differ from the signal in healthy controls. A major drawback of this examination is that it relies on visual inspection and as such, it is highly subjective and prone to errors. Based on EMG time series of 65 individuals (40 with ALS/IBM and 25 healthy), we aim to develop an automated machine-learning pipeline for the classification of EMG recordings of muscles in either disease or healthy (muscle-level). The automated pipeline consists of feature extraction, feature selection, modelling algorithm, and optimization, in which the most significant features are automatically selected from the feature space and the hyperparameters of the model are optimized by a Bayesian technique as part of the automatedapproach. Aside from the muscle-level approach, we also explore a patient-level approach, which uses the output of the muscle-level automated pipeline in a post-processing manner to classify patients in being either disease or healthy, based on their muscle recordings. The resulting two approaches yield an AUC scoreof 81.7% (muscle-level) and 81.5% (patient-level), indicating that such approaches can assist clinicians in diagnosing if a patient has a neuropathy/myopathy or is healthy. Show less
The prostate-specific membrane antigen (PSMA)-targeted radiotracers Ga-68/Lu-177-PSMA-I&T and Tc-99m-PSMA-I&S (for imaging and surgery) are currently successfully used for clinical PET... Show moreThe prostate-specific membrane antigen (PSMA)-targeted radiotracers Ga-68/Lu-177-PSMA-I&T and Tc-99m-PSMA-I&S (for imaging and surgery) are currently successfully used for clinical PET imaging, radionuclide therapy, and radioguided surgery of metastatic prostate cancer. To additionally exploit the high sensitivity and spatial resolution of fluorescence imaging for improved surgical guidance, a PSMA-I&T-based hybrid tracer, PSMA-I&F (DOTAGA-k(Sulfo-Cy5)-y-nal-k-Sub-KuE), has been developed and evaluated. Methods: The in vitro PSMA-targeting efficiency of PSMA-I&F, the reference PSMA-I&T, and their corresponding Ga-nat-/Ga-68- and Lu-nat/Lu-177 counterparts was determined in LNCaP cells via competitive binding assays (IC50) and dual-tracer radioligand and fluorescence internalization studies. Biodistribution and small-animal PET imaging studies were performed in CB17 SCID and LNCaP xenograft-bearing SHO mice, respectively, and complemented by intraoperative far-red fluorescence imaging using a clinical laparoscope. Additionally, fully automated serial cryosectioning and fluorescence imaging of 1 tumor-bearing animal as well as PSMA immunohistochemistry and fluorescence microscopy of organ cryosections (tumor, kidney, spleen) were also performed. Results: Compared with the parent PSMA-I&T analogs, the PSMA affinities of PSMA-I&F and its Ga-nat-/Lu-nat-complexes remained high and unaffected by dye conjugation (7.9 < IC50 < 10.5 nM for all ligands). The same was observed for the internalization of Ga-68- and Lu-177-PSMA-I&F. In vivo, blood clearance of Ga-68- and Lu-177-PSMA-I&F was only slightly delayed by high plasma protein binding (94%-95%), and very low accumulation in nontarget organs was observed already at 1 h after injection. Dynamic PET imaging confirmed PSMA-specific (as demonstrated by coinjection of 2-PMPA) uptake into the LNCaP xenograft (4.5% +/- 1.8 percentage injected dose per gram) and the kidneys (106% +/- 23 percentage injected dose per gram). Tumor-to-background ratios of 2.1, 5.2, 9.6, and 9.6 for blood, liver, intestines, and muscle, respectively, at 1 h after injection led to excellent imaging contrast in Ga-68-PSMA-I&F PET and in intraoperative fluorescence imaging. Furthermore, fluorescence imaging of tissue cryosections allowed high-resolution visualization of intraorgan PSMA-I&F distribution in vivo and its correlation with PSMA expression as determined by immunohistochemistry. Conclusion: Thus, with its high PSMA-targeting efficiency and favorable pharmacokinetic profile, Ga-68/Lu-177-PSMA-I&F serves as an excellent proof-of-concept compound for the general feasibility of PSMA-I&T-based hybrid imaging. The PSMA-I&T scaffold represents a versatile PSMA-targeted lead structure, allowing relatively straightforward adaptation to the different structural requirements of dedicated nuclear or hybrid imaging agents. Show less
Schottelius, M.; Wurzer, A.; Wissmiller, K.; Beck, R.; Koch, M.; Gorpas, D.; ... ; Wester, H.J. 2019
Activation of chemokine CC receptors subtype 2 (CCR2) plays an important role in chronic inflammatory processes such as atherosclerosis, multiple sclerosis and rheumatoid arthritis. A diverse set... Show moreActivation of chemokine CC receptors subtype 2 (CCR2) plays an important role in chronic inflammatory processes such as atherosclerosis, multiple sclerosis and rheumatoid arthritis. A diverse set of spirocyclic butanamides 4 (N-benzyl-4-(3,4-dihydrospiro[[2]benzopyran-1,4'-piperidin]-1'-yl)butanamides) was prepared by different combination of spirocyclic piperidines 8 (3,4-dihydrospiro[[2]benzopyran-1,4'-piperidines]) and γ-halobutanamides 11. A key step in the synthesis of spirocyclic piperidines 8 was an Oxa-Pictet-Spengler reaction of β-phenylethanols 5 with piperidone acetal 6. The substituted γ-hydroxybutanamides 11c-e were prepared by hydroxyethylation of methyl acetates 13 with ethylene sulfate giving the γ-lactones 14c and 14e. Aminolysis of the γ-lactones 14c and 14e with benzylamines provided the γ-hydroxybutanamides 15c-e, which were converted into the bromides 11c-e by an Appel reaction using polymer-bound PPh3. In radioligand binding assays the spirocyclic butanamides 4 did not displace the iodinated radioligand (125)I-CCL2 from the human CCR2. However, in the Ca(2+)-flux assay using human CCR2 strong antagonistic activity of butanamides 4 was detected. Analysis of the IC50-values led to clear relationships between the structure and the inhibition of the Ca(2+)-flux. 4g (4-(3,4-dihydrospiro[[2]benzopyran-1,4'-piperidin]-1'-yl)-N-[3,5-bis(trifluoromethylbenzyl)]-2-(4-fluorophenyl)butanamide) and 4o (N-[3,5-bis(trifluoromethyl)benzyl]-2-cyclopropyl-4-(3,4-dihydrospiro[[2]benzopyran-1,4'-piperidin]-1'-yl)butanamide) represent the most potent CCR2 antagonists with IC50-values of 89 and 17nM, respectively. Micromolar activities were found in the β-arrestin recruitment assay with murine CCR2, but the structure-activity-relationships detected in the Ca(2+)-flux assay were confirmed. Show less
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