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publications
Pathways for ligand activated nuclear receptors to unravel the genomic responses induced by hepatotoxicants
Fijten, R. R. R.; Jennen, D. G. J.; van Delft, J. H. M.. Current Drug Metabolism. 2013
The liver is a vital organ in vertebrates that can be subject to disease, among others due to exposure to toxic xenobiotic compounds. A group of transcription factors named ligand activated nuclear receptors (LANR) influence and regulate important liver functions, and can be activated by many xenobiotic compounds, which thereby can cause hepatotoxicity. Systematic analysis of the gene pathways regulated by LANR using modern 'omics technologies is important for investigating modes-of-action of hepatotoxicants. So far, these pathways are not publicly available in a format that allows these studies. We used PathVisio to build liver-specific LANR pathways, both for rats and humans. Since many LANR pathways are linked to each other, we also merged them into a meta-pathway. The pathways are in a GPML-format that enables pathway statistics and visualisations, and will be made available to the public through WikiPathways. We demonstrate the performance of these novel pathways in evaluating transcriptomic studies from the Japanese toxicogenomics project database (Open TG-GATEs). We show that the new pathways can be used to accurately analyse and visualize the effects of prototypical hepatotoxicants in important liver processes, and thus to evaluate the possible mode-of-actions of hepatotoxic xenobiotic compounds by assessing which LANRs are possible targets.
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Current breathomics–a review on data pre-processing techniques and machine learning in metabolomics breath analysis
Smolinska, A.; Hauschild, A.-Ch; Fijten, R. R. R.; Dallinga, J. W.; Baumbach, J.; van Schooten, F. J.. Journal of Breath Research. 2014
We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.
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Identification of microorganisms based on headspace analysis of volatile organic compounds by gas chromatography-mass spectrometry
Boots, A. W.; Smolinska, A.; van Berkel, J. J. B. N.; Fijten, R. R. R.; Stobberingh, E. E.; Boumans, M. L. L.; Moonen, E. J.; Wouters, E. F. M.; Dallinga, J. W.; Van Schooten, F. J.. Journal of Breath Research. 2014
The identification of specific volatile organic compounds (VOCs) produced by microorganisms may assist in developing a fast and accurate methodology for the determination of pulmonary bacterial infections in exhaled air. As a first step, pulmonary bacteria were cultured and their headspace analyzed for the total amount of excreted VOCs to select those compounds which are exclusively associated with specific microorganisms. Development of a rapid, noninvasive methodology for identification of bacterial species may improve diagnostics and antibiotic therapy, ultimately leading to controlling the antibiotic resistance problem. Two hundred bacterial headspace samples from four different microorganisms (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumoniae) were analyzed by gas chromatography-mass spectrometry to detect a wide array of VOCs. Statistical analysis of these volatiles enabled the characterization of specific VOC profiles indicative for each microorganism. Differences in VOC abundance between the bacterial types were determined using ANalysis of VAriance-principal component analysis (ANOVA-PCA). These differences were visualized with PCA. Cross validation was applied to validate the results. We identified a large number of different compounds in the various headspaces, thus demonstrating a highly significant difference in VOC occurrence of bacterial cultures compared to the medium and between the cultures themselves. Additionally, a separation between a methicillin-resistant and a methicillin-sensitive isolate of S. aureus could be made due to significant differences between compounds. ANOVA-PCA analysis showed that 25 VOCs were differently profiled across the various microorganisms, whereas a PCA score plot enabled the visualization of these clear differences between the bacterial types. We demonstrated that identification of the studied microorganisms, including an antibiotic susceptible and resistant S. aureus substrain, is possible based on a selected number of compounds measured in the headspace of these cultures. These in vitro results may translate into a breath analysis approach that has the potential to be used as a diagnostic tool in medical microbiology.
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Analysis of volatile organic compounds in exhaled breath to diagnose ventilator-associated pneumonia
Schnabel, Ronny; Fijten, Rianne; Smolinska, Agnieszka; Dallinga, Jan; Boumans, Marie-Louise; Stobberingh, Ellen; Boots, Agnes; Roekaerts, Paul; Bergmans, Dennis; van Schooten, Frederik Jan. Scientific Reports. 2015
Ventilator-associated pneumonia (VAP) is a nosocomial infection occurring in the intensive care unit (ICU). The diagnostic standard is based on clinical criteria and bronchoalveolar lavage (BAL). Exhaled breath analysis is a promising non-invasive method for rapid diagnosis of diseases and contains volatile organic compounds (VOCs) that can differentiate diseased from healthy individuals. The aim of this study was to determine whether analysis of VOCs in exhaled breath can be used as a non-invasive monitoring tool for VAP. One hundred critically ill patients with clinical suspicion of VAP underwent BAL. Before BAL, exhaled air samples were collected and analysed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). The clinical suspicion of VAP was confirmed by BAL diagnostic criteria in 32 patients [VAP(+)] and rejected in 68 patients [VAP(-)]. Multivariate statistical comparison of VOC profiles between VAP(+) and VAP(-) revealed a subset of 12 VOCs that correctly discriminated between those two patient groups with a sensitivity and specificity of 75.8% ± 13.5% and 73.0% ± 11.8%, respectively. These results suggest that detection of VAP in ICU patients is possible by examining exhaled breath, enabling a simple, safe and non-invasive approach that could diminish diagnostic burden of VAP.
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Altered gene expression profiles in the lungs of benzo[a]pyrene-exposed mice in the presence of lipopolysaccharide-induced pulmonary inflammation
Shi, Q.; Fijten, R. R.; Spina, D.; Riffo Vasquez, Y.; Arlt, V. M.; Godschalk, R. W.; Van Schooten, F. J.. Toxicology and Applied Pharmacology. 2017
Patients with inflammatory lung diseases are often additionally exposed to polycyclic aromatic hydrocarbons like B[a]P and B[a]P-induced alterations in gene expression in these patients may contribute to the development of lung cancer. Mice were intra-nasally treated with lipopolysaccharide (LPS, 20μg/mouse) to induce pulmonary inflammation and subsequently exposed to B[a]P (0.5mg/mouse) by intratracheal instillation. Gene expression changes were analyzed in mouse lungs by RNA microarrays. Analysis of genes that are known to be involved in the cellular response to B[a]P indicated that LPS significantly inhibited gene expression of various enzymes linked to B[a]P metabolism, which was confirmed by phenotypic analyses of enzyme activity. Ultimately, these changes resulted in higher levels of B[a]P-DNA adducts in the lungs of mice exposed to B[a]P with prior LPS treatment compared to the lungs of mice exposed to B[a]P alone. Using principle component analysis (PCA), we found that of all the genes that were significantly altered in their expression, those that were able to separate the different exposure conditions were predominantly related to immune-response. Moreover, an overall analysis of differentially expressed genes indicated that cell-cell adhesion and cell-cell communication was inhibited in lungs of mice that received both B[a]P and LPS. Our results indicate that pulmonary inflammation increased the genotoxicity of B[a]P via inhibition of both phase I and II metabolism. Therefore, inflammation could be a critical contributor to B[a]P-induced carcinogenesis in humans.
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The necessity of external validation in exhaled breath research: a case study of sarcoidosis
Fijten, Rianne R. R.; Smolinska, Agnieszka; Drent, Marjolein; Dallinga, Jan W.; Mostard, Remy; Pachen, Daniëlle M.; van Schooten, Frederik J.; Boots, Agnes W.. Journal of Breath Research. 2017
As in other disciplines of 'omics' research, reproducibility is a major problem in exhaled breath research. Many studies report discriminatory volatiles in the same disease, yet the similarity between lists of identified compounds is low. This can occur due to many factors including the lack of internal and, in particular, external validation. In an ideal situation, an external validation-sampled at, for example, a different location-is always included to ensure generalization of the observed findings to a general population. In this study, we hypothesized that sarcoidosis patients and healthy controls could be discriminated based on a group of volatile organic compounds (VOCs) in exhaled breath and that these discriminating VOCs could be validated in an external population. The first dataset consisted of 87 sarcoidosis patients and 27 healthy controls, whereas the validation dataset consisted of 25 patients and 29 controls. Using the first dataset, nine VOCs were found that could predict sarcoidosis with 79.4% accuracy. Different types of internal and external validation were tested to assess the validity of the nine VOCs. Of the internal validations, randomly setting aside part of the data achieved the most accurate predictions while external validation was only possible by building a new prediction model that yielded a promising yet not entirely convincing accuracy of 74% due to the indirect approach. In conclusion, the initial results of this study are very promising but, as the results of our validation set already indicated, may not be reproducible in other studies. In order to achieve a reliable diagnostic breath fingerprint for sarcoidosis, we encourage other scientists to validate the presented findings. TRIAL REGISTRATION: NCT00741572 & NCT02361281.
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Exposure to genotoxic compounds alters in vitro cellular VOC excretion
Fijten, R. R. R.; Smolinska, A.; Shi, Q.; Pachen, D. M.; Dallinga, J. W.; Boots, A. W.; van Schooten, F. J.. Journal of Breath Research. 2018
Genotoxic carcinogens significantly damage cells and tissues by targeting macromolecules such as proteins and DNA, but their mechanisms of action and effects on human health are diverse. Consequently, determining the amount of exposure to a carcinogen and its cellular effects is essential, yet difficult. The aim of this manuscript was to investigate the potential of detecting alterations in volatile organic compounds (VOCs) profiles in the in vitro headspace of pulmonary cells after exposure to the genotoxic carcinogens cisplatin and benzo[a]pyrene using two different sampling set-ups. A prototype set-up was used for the cisplatin exposure, whereas a modified set-up was utilized for the benzo[a]pyrene exposure. Both carcinogens were added to the cell medium for 24 h. The headspace in the culture flask was sampled to measure the VOC content using gas chromatography-time-of-flight-mass spectrometry. Eight cisplatin-specific VOCs and six benzo[a]pyrene-specific VOCs were discriminatory between treated and non-treated cells. Since the in vivo biological effects of both genotoxic compounds are well-defined, the origin of the identified VOCs could potentially be traced back to common cellular processes including cell cycle pathways, DNA damage and repair. These results indicate that exposing lung cells to genotoxins alters headspace VOC profiles, suggesting that it might be possible to monitor VOC changes in vivo to study drug efficacy or exposure to different pollutants. In conclusion, this study emphasizes the innovative potential of in vitro VOCs experiments to determine their in vivo applicability and discover their endogenous origin.
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Radiation Oncology Terminology Linker: A Step Towards a Linked Data Knowledge Base
Lustberg, Tim; van Soest, Johan; Fick, Peter; Fijten, Rianne; Hendriks, Tim; Puts, Sander; Dekker, Andre. Studies in Health Technology and Informatics. 2018
Performing image feature extraction in radiation oncology is often dependent on the organ and tumor delineations provided by clinical staff. These delineation names are free text DICOM metadata fields resulting in undefined information, which requires effort to use in large-scale image feature extraction efforts. In this work we present a scale-able solution to overcome these naming convention challenges with a REST service using Semantic Web technology to convert this information to linked data. As a proof of concept an open source software is used to compute radiation oncology image features. The results of this work can be found in a public Bitbucket repository.
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The Benefits and Challenges of Using Patient Decision Aids to Support Shared Decision Making in Health Care
Ankolekar, Anshu; Dekker, Andre; Fijten, Rianne; Berlanga, Adriana. JCO clinical cancer informatics. 2018
Shared decision making (SDM) and patient-centered care require patients to actively participate in the decision-making process. Yet with the increasing number and complexity of cancer treatment options, it can be a challenge for patients to evaluate clinical information and make risk-benefit trade-offs to choose the most appropriate treatment. Clinicians face time constraints and communication challenges, which can further hamper the SDM process. In this article, we review patient decision aids (PDAs) as a means of supporting SDM by presenting clinical information and risk data to patients in a format that is accessible and easy to understand. We outline the benefits and limitations of PDAs as well as the challenges in their development, such as a lengthy and complex development process and implementation obstacles. Lastly, we discuss future trends and how change on multiple levels-PDA developers, clinicians, hospital administrators, and health care insurers-can support the use of PDAs and consequently SDM. Through this multipronged approach, patients can be empowered to take an active role in their health and choose treatments that are in line with their values.
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Development and validation of a patient decision aid for prostate Cancer therapy: from paternalistic towards participative shared decision making
Ankolekar, Anshu; Vanneste, Ben G. L.; Bloemen-van Gurp, Esther; van Roermund, Joep G.; van Limbergen, Evert J.; van de Beek, Kees; Marcelissen, Tom; Zambon, Victor; Oelke, Matthias; Dekker, Andre; Roumen, Cheryl; Lambin, Philippe; Berlanga, Adriana; Fijten, Rianne. BMC medical informatics and decision making. 2019
BACKGROUND: Patient decision aids (PDAs) can support the treatment decision making process and empower patients to take a proactive role in their treatment pathway while using a shared decision-making (SDM) approach making participatory medicine possible. The aim of this study was to develop a PDA for prostate cancer that is accurate and user-friendly. METHODS: We followed a user-centered design process consisting of five rounds of semi-structured interviews and usability surveys with topics such as informational/decisional needs of users and requirements for PDAs. Our user-base consisted of 8 urologists, 4 radiation oncologists, 2 oncology nurses, 8 general practitioners, 19 former prostate cancer patients, 4 usability experts and 11 healthy volunteers. RESULTS: Informational needs for patients centered on three key factors: treatment experience, post-treatment quality of life, and the impact of side effects. Patients and clinicians valued a PDA that presents balanced information on these factors through simple understandable language and visual aids. Usability questionnaires revealed that patients were more satisfied overall with the PDA than clinicians; however, both groups had concerns that the PDA might lengthen consultation times (42 and 41%, respectively). The PDA is accessible on http://beslissamen.nl/ . CONCLUSIONS: User-centered design provided valuable insights into PDA requirements but challenges in integrating diverse perspectives as clinicians focus on clinical outcomes while patients also consider quality of life. Nevertheless, it is crucial to involve a broad base of clinical users in order to better understand the decision-making process and to develop a PDA that is accurate, usable, and acceptable.
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Data Sharing and Toxicity Modelling : A Vision of the Near Future
Shi, Zhenwei; Fijten, Rianne; Zhou, Zhen; Dekker, Andre; Wee, Leonard. Modelling Radiotherapy Side Effects. 2019
This chapter examines the data landscape and general data requirements to develop quantitative toxicity models. Optimal cancer treatment requires maximising the chance of tumour eradication while simultaneously minimising the risk of adverse treatment-induced side effects. Real-world data refers to information about patients, medical interventions and clinical findings that have been derived from routine procedures in the standard-of-care care setting. The most crucial aspect defining the potential success of a predictive toxicity model is the availability and quality of outcomes data. Data sharing enables either a larger combined data corpus for training and cross-validation, or the option for a model developed on one corpus to be independently validated against the other corpus. An increasing amount of data is being generated in different fields of modern medicine including medical imaging, transcriptomics, metabolomics and proteomics. For the abovementioned distributed learning methodology to work, the local data needs to be parsed in a format that is fully machine-readable and machine-understandable.
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Distributed radiomics as a signature validation study using the Personal Health Train infrastructure
Shi, Zhenwei; Zhovannik, Ivan; Traverso, Alberto; Dankers, Frank J. W. M.; Deist, Timo M.; Kalendralis, Petros; Monshouwer, René; Bussink, Johan; Fijten, Rianne; Aerts, Hugo J. W. L.; Dekker, Andre; Wee, Leonard. Scientific Data. 2019
Prediction modelling with radiomics is a rapidly developing research topic that requires access to vast amounts of imaging data. Methods that work on decentralized data are urgently needed, because of concerns about patient privacy. Previously published computed tomography medical image sets with gross tumour volume (GTV) outlines for non-small cell lung cancer have been updated with extended follow-up. In a previous study, these were referred to as Lung1 (n = 421) and Lung2 (n = 221). The Lung1 dataset is made publicly accessible via The Cancer Imaging Archive (TCIA; https://www.cancerimagingarchive.net ). We performed a decentralized multi-centre study to develop a radiomic signature (hereafter “ZS2019”) in one institution and validated the performance in an independent institution, without the need for data exchange and compared this to an analysis where all data was centralized. The performance of ZS2019 for 2-year overall survival validated in distributed radiomics was not statistically different from the centralized validation (AUC 0.61 vs 0.61; p = 0.52). Although slightly different in terms of data and methods, no statistically significant difference in performance was observed between the new signature and previous work (c-index 0.58 vs 0.65; p = 0.37). Our objective was not the development of a new signature with the best performance, but to suggest an approach for distributed radiomics. Therefore, we used a similar method as an earlier study. We foresee that the Lung1 dataset can be further re-used for testing radiomic models and investigating feature reproducibility.
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Learning from scanners: Bias reduction and feature correction in radiomics
Zhovannik, Ivan; Bussink, Johan; Traverso, Alberto; Shi, Zhenwei; Kalendralis, Petros; Wee, Leonard; Dekker, Andre; Fijten, Rianne; Monshouwer, René. Clinical and Translational Radiation Oncology. 2019
Purpose: Radiomics are quantitative features extracted from medical images. Many radiomic features depend not only on tumor properties, but also on non-tumor related factors such as scanner signal-to-noise ratio (SNR), reconstruction kernel and other image acquisition settings. This causes undesirable value variations in the features and reduces the performance of prediction models. In this paper, we investigate whether we can use phantom measurements to characterize and correct for the scanner SNR dependence. Methods: We used a phantom with 17 regions of interest (ROI) to investigate the influence of different SNR values. CT scans were acquired with 9 different exposure settings. We developed an additive correction model to reduce scanner SNR influence. Results: Sixty-two of 92 radiomic features showed high variance due to the scanner SNR. Of these 62 features, 47 showed at least a factor 2 significant standard deviation reduction by using the additive correction model. We assessed the clinical relevance of radiomics instability by using a 221 NSCLC patient cohort measured with the same scanner. Conclusions: Phantom measurements show that roughly two third of the radiomic features depend on the exposure setting of the scanner. The dependence can be modeled and corrected significantly reducing the variation in feature values with at least a factor of 2. More complex models will likely increase the correctability. Scanner SNR correction will result in more reliable radiomics predictions in NSCLC.
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talks
Volatile Biomarkers of Cisplatin Toxicity: Monitoring chemotherapy resistance
. Annual NvT meeting. 2014
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Analysis of exhaled breath in lung diseases
. Imperial College. 2014
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Exhaled breath: non-invasive diagnosis of ventilator-associated pneumonia
. European Respiratory Society 2015. 2015
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It’s a matter of perspective
. Annual NvT meeting 2017. 2017
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The solution to overcoming the implementation barrier of Clinical Decision Support Systems: EHR-compatible, intuitive interfaces
. ESTRO 2019. 2017
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Big Data in Precision Medicine
. Benelux Precision Medicine. 2017
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Biological origin of Volatile Organic Compounds
. Breath biopsy conference 2018. 2018
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Pitch: workshop consortia building
. Digital Society conference 2918. 2018
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De potentie en implementatie van Big Data in onderzoek en kliniek
. CTCM conference 2019. 2019
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ProTRAIT: Protonentherapie onderzoeksinfrastructuur
. KWF visit at Maastro. 2019
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Reproducibility in data science
. Annual NvT meeting 2019. 2019
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The solution to overcoming the implementation barrier of Clinical Decision Support Systems: EHR-compatible, intuitive interfaces
. ICCR 2019. 2019
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Workshop: From data to personalised medicine
. BMosa conference. 2019
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The Personal Health Train
. VvE Symposium BIG DATA. 2019
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Panel discussion: Technology in healthcare
. NefeMed innovatielab 2019. 2019
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Debate
. GROW science day 2019. 2019
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Responsible AI for clinical shared decision making
. Digital Society conference 2019. 2019
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The Why behind FAIR data and PHT
. HealthRI 2020. 2020
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teaching
Teaching experience 1
. University 1, Department. 2014
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Teaching experience 2
. University 1, Department. 2015
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