Publications by authors named "Alex Zwanenburg"

25 Publications

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Pictures worth more than a thousand words: Prediction of survival in medulloblastoma patients.

EBioMedicine 2020 Dec 21;62:103136. Epub 2020 Nov 21.

OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; German Cancer Consortium (DKTK), partner site, Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany.

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http://dx.doi.org/10.1016/j.ebiom.2020.103136DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691739PMC
December 2020

Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC.

Cancers (Basel) 2020 Oct 19;12(10). Epub 2020 Oct 19.

OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, 01307 Dresden, Germany.

Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTV ). However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead to an improved outcome prediction. Therefore, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma. The GTV  was cropped by different margins to define the rim and the corresponding core sub-volumes of the tumour. Subsequently, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. Radiomic risk models were developed and validated using a retrospective cohort consisting of 291 patients in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group treated between 2005 and 2013. The validation concordance index (C-index) averaged over all applied learning algorithms and feature selection methods using the GTVentire achieved a moderate prognostic performance for loco-regional tumour control (C-index: 0.61 ± 0.04 (mean ± std)). The models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed higher median performances (C-index: 0.65 ± 0.02 and 0.64 ± 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 ± 0.01). The difference in C-index between the 5 mm tumour rim and the corresponding core volume showed a statistical trend ( = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.
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http://dx.doi.org/10.3390/cancers12103047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589463PMC
October 2020

Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy.

Phys Imaging Radiat Oncol 2020 Jul;15:52-59

Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany.

Background And Purpose: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET.

Materials And Methods: Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures.Secondly, a pre-defined [F]FMISO-PET tumour-to-muscle-ratio (TMR ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [F]FMISO-PET was performed using area-under-the-curve (AUC) and Kaplan-Meier analysis including log-rank test.

Results: The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [F]FMISO TMR reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [F]FMISO-PET significant differences were observed (p = 0.02).

Conclusions: No direct correlation of patient stratification using [F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients.
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http://dx.doi.org/10.1016/j.phro.2020.07.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536307PMC
July 2020

2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma.

Sci Rep 2020 09 24;10(1):15625. Epub 2020 Sep 24.

OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.

For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tumour control (LRC) from treatment-planning computed tomography images. Based on multicentre cohorts for exploration (206 patients) and independent validation (85 patients), multiple deep learning strategies including training of 3D- and 2D-convolutional neural networks (CNN) from scratch, transfer learning and extraction of deep autoencoder features were assessed and compared to a clinical model. Analyses were based on Cox proportional hazards regression and model performances were assessed by the concordance index (C-index) and the model's ability to stratify patients based on predicted hazards of LRC. Among all models, an ensemble of 3D-CNNs achieved the best performance (C-index 0.31) with a significant association to LRC on the independent validation cohort. It performed better than the clinical model including the tumour volume (C-index 0.39). Significant differences in LRC were observed between patient groups at low or high risk of tumour recurrence as predicted by the model ([Formula: see text]). This 3D-CNN ensemble will be further evaluated in a currently ongoing prospective validation study once follow-up is complete.
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http://dx.doi.org/10.1038/s41598-020-70542-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518264PMC
September 2020

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

Radiology 2020 05 10;295(2):328-338. Epub 2020 Mar 10.

From OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstr 74, PF 41, 01307 Dresden, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z.); Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz Association/Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany (A.Z., S. Leger, E.G.C.T.); German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); Medical Physics Unit, McGill University, Montréal, Canada (M.V., I.E.N.); Image Response Assessment Team Core Facility, Moffitt Cancer Center, Tampa, Fla (M.A.A.); Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Harvard University, Boston, Mass (H.J.W.L.A.); Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland (V.A., A.D., H.M.); Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY (A.A.); Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Md (S.A.); Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Md (S.A., A.R.); Center for Biomedical image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B.); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (R.J.B., R.B., E.A.G.P.); Radiology and Nuclear Medicine, VU University Medical Centre (VUMC), Amsterdam, the Netherlands (R.B.); Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (M.B., M.Guckenberger, S.T.L.); Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy (L.B., N.D., R.G., J.L., V.V.); Laboratoire d'Imagerie Translationnelle en Oncologie, Université Paris Saclay, Inserm, Institut Curie, Orsay, France (I.B., C.N., F.O.); Cancer Imaging Dept, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.J.R.C., V.G., M.M.S.); Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland (A.D.); Laboratory of Medical Information Processing (LaTIM)-team ACTION (image-guided therapeutic action in oncology), INSERM, UMR 1101, IBSAM, UBO, UBL, Brest, France (M.C.D., M.H., T.U.); Department of Radiation Oncology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (C.V.D.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (S.E., S.N.); Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, Mich (I.E.N., A.U.K.R.); Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, Mass (A.Y.F.); Department of Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, Fla (R.J.G.); Department of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (M. Götz, F.I., K.H.M.H., J.S.); The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands (P.L., R.T.H.L.); Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany (F.L., J.S.F., D.T.); Department of Clinical Medicine, University of Bergen, Bergen, Norway (A.L.); Department of Radiation Oncology, University of California, San Francisco, Calif (O.M.); University of Geneva, Geneva, Switzerland (H.M.); Department of Electrical Engineering, Stanford University, Stanford, Calif (S.N.); Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, Calif (S.N.); Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada (A.R.); Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Mich (A.U.K.R.); Department of Radiation Oncology, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (N.M.S., R.J.H.M.S., L.V.v.D.); School of Engineering, Cardiff University, Cardiff, United Kingdom (E.S., P.W.); Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom (E.S.); Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany (E.G.C.T., C.R., S. Löck), Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany (E.G.C.T., C.R.); Department of Nuclear Medicine, CHU Milétrie, Poitiers, France (T.U.); Department of Radiology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (J.v.G.); GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (J.v.G.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (J.v.G.); and Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands (F.H.P.v.V.).

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 See also the editorial by Kuhl and Truhn in this issue.
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http://dx.doi.org/10.1148/radiol.2020191145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193906PMC
May 2020

Why -aVF can be used in STAN as a proxy for scalp electrode-derived signal; reply to comments by Kjellmer et al.

PLoS One 2019 22;14(8):e0221220. Epub 2019 Aug 22.

Department of Biomedical Engineering, Maastricht University, Maastricht, the Netherlands.

The conclusion of our recent paper that performance of the STAN device in clinical practice is potentially limited by high false-negative and high false-positive STAN-event rates and loss of ST waveform assessment capacity during severe hypoxemia, evoked comments by Kjellmer, Lindecrantz and Rosén. These comments can be summarized as follows: 1) STAN analysis is based on a unipolar lead but the authors used a negative aVF lead, and they did not validate this methodology; 2) The fetuses used in the study were too young to display the signals that the authors were trying to detect. In response to these comments we now provide both a theoretical and an experimental underpinning of our approach. In an in vivo experiment in human we placed several electrodes over the head (simulating different places of a scalp electrode), simultaneously recorded Einthoven lead I and II, and constructed -aVF from these two frontal leads. Irrespective of scalp electrode placement, the correlation between any of unipolar scalp electrode-derived signals and constructed-aVF was excellent (≥ 0.92). In response to the second comment we refer to a study which demonstrated that umbilical cord occlusion resulted in rapid increase in T/QRS ratio that coincided with initial hypertension and bradycardia at all gestational ages which were tested from 0.6-0.8 gestation. The animals of our study were in this gestational range and, hence, our experimental setup can be used to assess STAN's quality to detect fetal hypoxia. In conclusion, we have clearly demonstrated the appropriateness of using-aVF as a proxy for a scalp electrode-derived signal in STAN in these preterm lambs. Investigation why STAN could not detect relevant ST-changes and instead produced erroneous alarms in our experimental setup is hampered by the fact that the exact STAN algorithm (signal processing and analysis) is not in the public domain.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221220PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6705853PMC
February 2020

Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.

Authors:
Alex Zwanenburg

Eur J Nucl Med Mol Imaging 2019 Dec 25;46(13):2638-2655. Epub 2019 Jun 25.

OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Helmholtz-Zentrum Dresden - Rossendorf, Technische Universität Dresden, Dresden, Germany.

Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUV and SUV were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.
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http://dx.doi.org/10.1007/s00259-019-04391-8DOI Listing
December 2019

RaCaT: An open source and easy to use radiomics calculator tool.

PLoS One 2019 20;14(2):e0212223. Epub 2019 Feb 20.

Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Purpose: The widely known field 'Radiomics' aims to provide an extensive image based phenotyping of e.g. tumors using a wide variety of feature values extracted from medical images. Therefore, it is of utmost importance that feature values calculated by different institutes follow the same feature definitions. For this purpose, the imaging biomarker standardization initiative (IBSI) provides detailed mathematical feature descriptions, as well as (mathematical) test phantoms and corresponding reference feature values. We present here an easy to use radiomic feature calculator, RaCaT, which provides the calculation of a large number of radiomic features for all kind of medical images which are in compliance with the standard.

Methods: The calculator is implemented in C++ and comes as a standalone executable. Therefore, it can be easily integrated in any programming language, but can also be called from the command line. No programming skills are required to use the calculator. The software architecture is highly modularized so that it is easily extendible. The user can also download the source code, adapt it if needed and build the calculator from source. The calculated feature values are compliant with the ones provided by the IBSI standard. Source code, example files for the software configuration, and documentation can be found online on GitHub (https://github.com/ellipfaehlerUMCG/RaCat).

Results: The comparison with the standard values shows that all calculated features as well as image preprocessing steps, comply with the IBSI standard. The performance is also demonstrated on clinical examples.

Conclusions: The authors successfully implemented an easy to use Radiomics calculator that can be called from any programming language or from the command line. Image preprocessing and feature settings and calculations can be adjusted by the user.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212223PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382170PMC
November 2019

Assessing robustness of radiomic features by image perturbation.

Sci Rep 2019 01 24;9(1):614. Epub 2019 Jan 24.

OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.

Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accurately. Test-retest imaging is recommended to assess robustness, but may not be available for the phenotype of interest. We therefore investigated 18 combinations of image perturbations to determine feature robustness, based on noise addition (N), translation (T), rotation (R), volume growth/shrinkage (V) and supervoxel-based contour randomisation (C). Test-retest and perturbation robustness were compared for combined total of 4032 morphological, statistical and texture features that were computed from the gross tumour volume in two cohorts with computed tomography imaging: I) 31 non-small-cell lung cancer (NSCLC) patients; II): 19 head-and-neck squamous cell carcinoma (HNSCC) patients. Robustness was determined using the 95% confidence interval (CI) of the intraclass correlation coefficient (1, 1). Features with CI ≥ 0:90 were considered robust. The NTCV, TCV, RNCV and RCV perturbation chain produced similar results and identified the fewest false positive robust features (NSCLC: 0.2-0.9%; HNSCC: 1.7-1.9%). Thus, these perturbation chains may be used as an alternative to test-retest imaging to assess feature robustness.
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http://dx.doi.org/10.1038/s41598-018-36938-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345842PMC
January 2019

CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer.

Radiother Oncol 2019 01 4;130:10-17. Epub 2018 Aug 4.

OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) partner site Dresden, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany.

Background And Purpose: The development of radiomic risk models to predict clinical outcome is usually based on pre-treatment imaging, such as computed tomography (CT) scans used for radiation treatment planning. Imaging data acquired during the course of treatment may improve their prognostic performance. We compared the performance of radiomic risk models based on the pre-treatment CT and CT scans acquired in the second week of therapy.

Material And Methods: Treatment planning and second week CT scans of 78 head and neck squamous cell carcinoma patients treated with primary radiochemotherapy were collected. 1538 image features were extracted from each image. Prognostic models for loco-regional tumour control (LRC) and overall survival (OS) were built using 6 feature selection methods and 6 machine learning algorithms. Prognostic performance was assessed using the concordance index (C-Index). Furthermore, patients were stratified into risk groups and differences in LRC and OS were evaluated by log-rank tests.

Results: The performance of radiomic risk model in predicting LRC was improved using the second week CT scans (C-Index: 0.79), in comparison to the pre-treatment CT scans (C-Index: 0.65). This was confirmed by Kaplan-Meier analyses, in which risk stratification based on the second week CT could be improved for LRC (p = 0.002) compared to pre-treatment CT (p = 0.063).

Conclusion: Incorporation of imaging during treatment may be a promising way to improve radiomic risk models for clinical treatment adaption, i.e., to select patients that may benefit from dose modification.
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http://dx.doi.org/10.1016/j.radonc.2018.07.020DOI Listing
January 2019

ST waveform analysis for monitoring hypoxic distress in fetal sheep after prolonged umbilical cord occlusion.

PLoS One 2018 16;13(4):e0195978. Epub 2018 Apr 16.

Department of Biomedical Engineering, Maastricht University, Maastricht, the Netherlands.

Introduction: The inconclusive clinical results for ST-waveform analysis (STAN) in detecting fetal hypoxemia may be caused by the signal processing of the STAN-device itself. We assessed the performance of a clinical STAN device in signal processing and in detecting hypoxemia in a fetal sheep model exposed to prolonged umbilical cord occlusion (UCO).

Methods: Eight fetal lambs were exposed to 25 minutes of UCO. ECG recordings were analyzed during a baseline period and during UCO. STAN-event rates and timing of episodic T/QRS rise, baseline T/QRS rise and the occurrence of biphasic ST-waveforms, as well as signal loss, were assessed.

Results: During baseline conditions of normoxemia, a median of 40 (IQR, 25-70) STAN-events per minute were detected, compared to 10 (IQR, 2-22) during UCO. During UCO STAN-events were detected in five subjects within 10 minutes and in six subjects after 18 minutes, respectively. Two subjects did not generate any STAN-event during UCO. Biphasic ST event rate was reduced during UCO (median 0, IQR 0-5), compared to baseline (median 32, IQR, 6-55). ST-waveforms could not be assessed in 62% of the recording time during UCO, despite a good quality of the ECG signal.

Conclusions: The STAN device showed limitations in detecting hypoxemia in fetal sheep after prolonged UCO. The STAN device produced high false positive event rates during baseline and did not detect T/QRS changes adequately after prolonged fetal hypoxemia. During 14% of baseline and 62% of the UCO period, the STAN-device could not process the ECG signal, despite its good quality. Resolving these issues may improve the clinical performance of the STAN device.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195978PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5901956PMC
July 2018

Why validation of prognostic models matters?

Radiother Oncol 2018 06 26;127(3):370-373. Epub 2018 Mar 26.

OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany. Electronic address:

Prognostic models are powerful tools for treatment personalisation. However, not all proposed models work well when validated using new data, despite impressive results being reported initially. Here, we will use a hands-on approach to highlight important aspects of prognostic modelling, as well as to demonstrate methods to generate generalisable models.
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http://dx.doi.org/10.1016/j.radonc.2018.03.004DOI Listing
June 2018

Development and Validation of a Gene Signature for Patients with Head and Neck Carcinomas Treated by Postoperative Radio(chemo)therapy.

Clin Cancer Res 2018 03 3;24(6):1364-1374. Epub 2018 Jan 3.

German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany.

The aim of this study was to identify and independently validate a novel gene signature predicting locoregional tumor control (LRC) for treatment individualization of patients with locally advanced HPV-negative head and neck squamous cell carcinomas (HNSCC) who are treated with postoperative radio(chemo)therapy (PORT-C). Gene expression analyses were performed using NanoString technology on a multicenter training cohort of 130 patients and an independent validation cohort of 121 patients. The analyzed gene set was composed of genes with a previously reported association with radio(chemo)sensitivity or resistance to radio(chemo)therapy. Gene selection and model building were performed comparing several machine-learning algorithms. We identified a 7-gene signature consisting of the three individual genes , and one metagene combining the highly correlated genes , and The 7-gene signature was used, in combination with clinical parameters, to fit a multivariable Cox model to the training data (concordance index, ci = 0.82), which was successfully validated (ci = 0.71). The signature showed improved performance compared with clinical parameters alone (ci = 0.66) and with a previously published model including hypoxia-associated genes and cancer stem cell markers (ci = 0.65). It was used to stratify patients into groups with low and high risk of recurrence, leading to significant differences in LRC in training and validation ( < 0.001). We have identified and validated the first hypothesis-based gene signature for HPV-negative HNSCC treated by PORT-C including genes related to several radiobiological aspects. A prospective validation is planned in an ongoing prospective clinical trial before potential application in clinical trials for patient stratification. .
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http://dx.doi.org/10.1158/1078-0432.CCR-17-2345DOI Listing
March 2018

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.

Sci Rep 2017 10 16;7(1):13206. Epub 2017 Oct 16.

OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g.

, Msr-rf: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.
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http://dx.doi.org/10.1038/s41598-017-13448-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643429PMC
October 2017

Mesenchymal Stromal Cell-Derived Extracellular Vesicles Protect the Fetal Brain After Hypoxia-Ischemia.

Stem Cells Transl Med 2016 Jun 9;5(6):754-63. Epub 2016 May 9.

School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands Department of Pediatrics, Maastricht University, Maastricht, The Netherlands School of Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands

Unlabelled: Preterm neonates are susceptible to perinatal hypoxic-ischemic brain injury, for which no treatment is available. In a preclinical animal model of hypoxic-ischemic brain injury in ovine fetuses, we have demonstrated the neuroprotective potential of systemically administered mesenchymal stromal cells (MSCs). The mechanism of MSC treatment is unclear but suggested to be paracrine, through secretion of extracellular vesicles (EVs). Therefore, we investigated in this study the protective effects of mesenchymal stromal cell-derived extracellular vesicles (MSC-EVs) in a preclinical model of preterm hypoxic-ischemic brain injury. Ovine fetuses were subjected to global hypoxia-ischemia by transient umbilical cord occlusion, followed by in utero intravenous administration of MSC-EVs. The therapeutic effects of MSC-EV administration were assessed by analysis of electrophysiological parameters and histology of the brain. Systemic administration of MSC-EVs improved brain function by reducing the total number and duration of seizures, and by preserving baroreceptor reflex sensitivity. These functional protections were accompanied by a tendency to prevent hypomyelination. Cerebral inflammation remained unaffected by the MSC-EV treatment. Our data demonstrate that MSC-EV treatment might provide a novel strategy to reduce the neurological sequelae following hypoxic-ischemic injury of the preterm brain. Our study results suggest that a cell-free preparation comprising neuroprotective MSC-EVs could substitute MSCs in the treatment of preterm neonates with hypoxic-ischemic brain injury, thereby circumventing the potential risks of systemic administration of living cells.

Significance: Bone marrow-derived mesenchymal stromal cells (MSCs) show promise in treating hypoxic-ischemic injury of the preterm brain. Study results suggest administration of extracellular vesicles, rather than intact MSCs, is sufficient to exert therapeutic effects and avoids potential concerns associated with administration of living cells. The therapeutic efficacy of systemically administered mesenchymal stromal cell-derived extracellular vesicles (MSC-EVs) on hypoxia-ischemia-induced injury was assessed in the preterm ovine brain. Impaired function and structural injury of the fetal brain was improved following global hypoxia-ischemia. A cell-free preparation of MSC-EVs could substitute for the cellular counterpart in the treatment of preterm neonates with hypoxic-ischemic brain injury. This may open new clinical applications for "off-the-shelf" interventions with MSC-EVs.
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http://dx.doi.org/10.5966/sctm.2015-0197DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878333PMC
June 2016

Comparison of ECG-based physiological markers for hypoxia in a preterm ovine model.

Pediatr Res 2016 06 11;79(6):907-15. Epub 2016 Feb 11.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands.

Background: Current methods for assessing perinatal hypoxic conditions did not improve infant outcomes. Various waveform-based and interval-based ECG markers have been suggested, but not directly compared. We compare performance of ECG markers in a standardized ovine model for fetal hypoxia.

Methods: Sixty-nine fetal sheep of 0.7 gestation had ECG recorded 4 h before, during, and 4 h after a 25-min period of umbilical cord occlusion (UCO), leading to severe hypoxia. Various ECG markers were calculated, among which were heart rate (HR), HR-corrected ventricular depolarization/repolarization interval (QTc), and ST-segment analysis (STAN) episodic and baseline rise markers, analogue to clinical STAN device alarms. Performance of interval- and waveform-based ECG markers was assessed by correlating predicted and actual hypoxic/normoxic state.

Results: Of the markers studied, HR and QTc demonstrated high sensitivity (≥86%), specificity (≥96%), and positive predictive value (PPV) (≥86%) and detected hypoxia in ≥90% of fetuses at 4 min after UCO. In contrast, STAN episodic and baseline rise markers displayed low sensitivity (≤20%) and could not detect severe fetal hypoxia in 65 and 28% of the animals, respectively.

Conclusion: Interval-based HR and QTc markers could assess the presence of severe hypoxia. Waveform-based STAN episodic and baseline rise markers were ineffective as markers for hypoxia.
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http://dx.doi.org/10.1038/pr.2016.21DOI Listing
June 2016

Multipotent adult progenitor cells for hypoxic-ischemic injury in the preterm brain.

J Neuroinflammation 2015 Dec 23;12:241. Epub 2015 Dec 23.

School of Mental Health and Neuroscience (MHENS), Maastricht University, Universiteitssingel 40, Maastricht, 6229, ER, The Netherlands.

Background: Preterm infants are at risk for hypoxic-ischemic encephalopathy. No therapy exists to treat this brain injury and subsequent long-term sequelae. We have previously shown in a well-established pre-clinical model of global hypoxia-ischemia (HI) that mesenchymal stem cells are a promising candidate for the treatment of hypoxic-ischemic brain injury. In the current study, we investigated the neuroprotective capacity of multipotent adult progenitor cells (MAPC®), which are adherent bone marrow-derived cells of an earlier developmental stage than mesenchymal stem cells and exhibiting more potent anti-inflammatory and regenerative properties.

Methods: Instrumented preterm sheep fetuses were subjected to global hypoxia-ischemia by 25 min of umbilical cord occlusion at a gestational age of 106 (term ~147) days. During a 7-day reperfusion period, vital parameters (e.g., blood pressure and heart rate; baroreceptor reflex) and (amplitude-integrated) electroencephalogram were recorded. At the end of the experiment, the preterm brain was studied by histology.

Results: Systemic administration of MAPC therapy reduced the number and duration of seizures and prevented decrease in baroreflex sensitivity after global HI. In addition, MAPC cells prevented HI-induced microglial proliferation in the preterm brain. These anti-inflammatory effects were associated with MAPC-induced prevention of hypomyelination after global HI. Besides attenuation of the cerebral inflammatory response, our findings showed that MAPC cells modulated the peripheral splenic inflammatory response, which has been implicated in the etiology of hypoxic-ischemic injury in the preterm brain.

Conclusions: In a pre-clinical animal model MAPC cell therapy improved the functional and structural outcome of the preterm brain after global HI. Future studies should establish the mechanism and long-term therapeutic effects of neuroprotection established by MAPC cells in the developing preterm brain exposed to HI. Our study may form the basis for future clinical trials, which will evaluate whether MAPC therapy is capable of reducing neurological sequelae in preterm infants with hypoxic-ischemic encephalopathy.
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http://dx.doi.org/10.1186/s12974-015-0459-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4690228PMC
December 2015

Propofol administration to the maternal-fetal unit improved fetal EEG and influenced cerebral apoptotic pathway in preterm lambs suffering from severe asphyxia.

Mol Cell Pediatr 2015 Dec 10;2(1). Epub 2015 Mar 10.

Department of Paediatrics, Maastricht University Medical Center, P. Debyelaan 25, 6202 AZ, Maastricht, The Netherlands.

Background: Term and near-term infants are at high risk of developing brain injury and life-long disability if they have suffered from severe perinatal asphyxia. We hypothesized that propofol administration to the maternal-fetal unit can diminish cerebral injury in term and near-term infant fetuses in states of progressive severe asphyxia.

Methods: Forty-four late preterm lambs underwent total umbilical cord occlusion (UCO) or sham treatment in utero. UCO resulted in global asphyxia and cardiac arrest. After emergency cesarean section under either maternal propofol or isoflurane anesthesia, the fetuses were resuscitated and subsequently anesthetized the same way as their mothers.

Results: Asphyctic lambs receiving isoflurane showed a significant increase of total and low-frequency spectral power in bursts indicating seizure activity and more burst-suppression with a marked increase of interburst interval length during UCO. Asphyctic lambs receiving propofol showed less EEG changes. Propofol increased levels of anti-apoptotic B-cell lymphoma-extra large (Bcl-xL) and phosphorylated STAT-3 and reduced the release of cytochrome c from the mitochondria and the protein levels of activated cysteinyl aspartate-specific protease (caspase)-3, -9, and N-methyl-d-aspartate (NMDA) receptor.

Conclusions: Improvement of fetal EEG during and after severe asphyxia could be achieved by propofol treatment of the ovine maternal-fetal unit. The underlying mechanism is probably the reduction of glutamate-induced cytotoxicity by down-regulation of NMDA receptors and an inhibition of the mitochondrial apoptotic pathway.
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http://dx.doi.org/10.1186/s40348-015-0016-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4530565PMC
December 2015

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

Physiol Meas 2015 Mar 5;36(3):369-84. Epub 2015 Feb 5.

Department of Biomedical Engineering, Maastricht University, Universiteitssingel 50, Maastricht, The Netherlands. Department of Pediatrics, Maastricht University, Maastricht, The Netherlands.

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p < 0.001, Wilcoxon signed rank test). Using trend templates might therefore aid in detection of short seizures by EEG monitoring at the NICU.
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http://dx.doi.org/10.1088/0967-3334/36/3/369DOI Listing
March 2015

Systemic G-CSF attenuates cerebral inflammation and hypomyelination but does not reduce seizure burden in preterm sheep exposed to global hypoxia-ischemia.

Exp Neurol 2013 Dec 8;250:293-303. Epub 2013 Oct 8.

School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Pediatrics, Maastricht University Medical Center+, Maastricht, The Netherlands.

Hypoxic-ischemic encephalopathy (HIE) is common in preterm infants, but currently no curative therapy is available. Cell-based therapy has a great potential in the treatment of hypoxic-ischemic preterm brain injury. Granulocyte-colony stimulating factor (G-CSF) is known to mobilize endogenous hematopoietic stem cells (HSC) and promotes proliferation of endogenous neural stem cells. On these grounds, we hypothesized that systemic G-CSF would be neuroprotective in a large translational animal model of hypoxic-ischemic injury in the preterm brain. Global hypoxia-ischemia (HI) was induced by transient umbilical cord occlusion in instrumented preterm sheep. G-CSF treatment (100μg/kg intravenously, during five consecutive days) was started one day before the global HI insult to ascertain mobilization of endogenous stem cells within the acute phase after global HI. Mobilization of HSC and neutrophils was studied by flow cytometry. Brain sections were stained for microglia (IBA-1), myelin basic protein (MBP) and myeloperoxidase (MPO) to study microglial proliferation, white matter injury and neutrophil invasion respectively. Electrographic seizure activity was analyzed using amplitude-integrated electroencephalogram (aEEG). G-CSF effectively mobilized CD34-positive HSC in the preterm sheep. In addition, G-CSF caused marked mobilization of neutrophils, but did not influence enhanced invasion of neutrophils into the preterm brain after global HI. Microglial proliferation and hypomyelination following global HI were reduced as a result of G-CSF treatment. G-CSF did not cause a reduction of the electrographic seizure activity after global HI. In conclusion, G-CSF induced mobilization of endogenous stem cells which was associated with modulation of the cerebral inflammatory response and reduced white matter injury in an ovine model of preterm brain injury after global HI. G-CSF treatment did not improve neuronal function as shown by seizure analysis. Our study shows that G-CSF treatment has neuroprotective potential following hypoxic-ischemic injury in the preterm brain.
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http://dx.doi.org/10.1016/j.expneurol.2013.09.026DOI Listing
December 2013

Mesenchymal stem cells induce T-cell tolerance and protect the preterm brain after global hypoxia-ischemia.

PLoS One 2013 26;8(8):e73031. Epub 2013 Aug 26.

School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.

Hypoxic-ischemic encephalopathy (HIE) in preterm infants is a severe disease for which no curative treatment is available. Cerebral inflammation and invasion of activated peripheral immune cells have been shown to play a pivotal role in the etiology of white matter injury, which is the clinical hallmark of HIE in preterm infants. The objective of this study was to assess the neuroprotective and anti-inflammatory effects of intravenously delivered mesenchymal stem cells (MSC) in an ovine model of HIE. In this translational animal model, global hypoxia-ischemia (HI) was induced in instrumented preterm sheep by transient umbilical cord occlusion, which closely mimics the clinical insult. Intravenous administration of 2 x 10(6) MSC/kg reduced microglial proliferation, diminished loss of oligodendrocytes and reduced demyelination, as determined by histology and Diffusion Tensor Imaging (DTI), in the preterm brain after global HI. These anti-inflammatory and neuroprotective effects of MSC were paralleled by reduced electrographic seizure activity in the ischemic preterm brain. Furthermore, we showed that MSC induced persistent peripheral T-cell tolerance in vivo and reduced invasion of T-cells into the preterm brain following global HI. These findings show in a preclinical animal model that intravenously administered MSC reduced cerebral inflammation, protected against white matter injury and established functional improvement in the preterm brain following global HI. Moreover, we provide evidence that induction of T-cell tolerance by MSC might play an important role in the neuroprotective effects of MSC in HIE. This is the first study to describe a marked neuroprotective effect of MSC in a translational animal model of HIE.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0073031PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753351PMC
June 2014

Automatic detection of burst synchrony in preterm infants.

Annu Int Conf IEEE Eng Med Biol Soc 2012 ;2012:4720-3

Department of Medical Physics, Máxima Medical Center, De Run 4600 Veldhoven, The Netherlands. a.zwanenburg at mmc.nl

Electroencephalographic characteristics are useful in assessment of the functional status of specific neuronal connections relative to postmenstrual age. Interhemispheric burst synchrony (IBS) is a measure of the functional connectivity between the hemispheres in the maturing preterm brain. An algorithm was developed to assess IBS and was used in a prospective, longitudinal EEG study on 18 very preterm infants (< 32 weeks gestational age) with normal follow-up at 2 years of age. The preterm infants underwent weekly 4-hour multi-channel EEG recordings, resulting in n = 77 EEGs. After automated detection of bursts, the algorithm defines the start and end of interhemispheric synchronous burst activity, based on selection criteria found in literature. The algorithm was designed to emulate visual inspection, providing objective results in an automated manner. This approach may be applied in clinical use and open novel avenues to automated analysis in EEG monitoring and, moreover, it may facilitate assessment of the functional status of interhemispheric connections. As such, assessment of low interhemispheric synchrony may be associated with brain injury.
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http://dx.doi.org/10.1109/EMBC.2012.6347021DOI Listing
July 2013

Cerebral inflammation and mobilization of the peripheral immune system following global hypoxia-ischemia in preterm sheep.

J Neuroinflammation 2013 Jan 24;10:13. Epub 2013 Jan 24.

School of Mental Health and Neuroscience, Maastricht University, Universiteitssingel 40, Maastricht, 6229 ER, The Netherlands.

Background: Hypoxic-ischemic encephalopathy (HIE) is one of the most important causes of brain injury in preterm infants. Preterm HIE is predominantly caused by global hypoxia-ischemia (HI). In contrast, focal ischemia is most common in the adult brain and known to result in cerebral inflammation and activation of the peripheral immune system. These inflammatory responses are considered to play an important role in the adverse outcomes following brain ischemia. In this study, we hypothesize that cerebral and peripheral immune activation is also involved in preterm brain injury after global HI.

Methods: Preterm instrumented fetal sheep were exposed to 25 minutes of umbilical cord occlusion (UCO) (n = 8) at 0.7 gestation. Sham-treated animals (n = 8) were used as a control group. Brain sections were stained for ionized calcium binding adaptor molecule 1 (IBA-1) to investigate microglial proliferation and activation. The peripheral immune system was studied by assessment of circulating white blood cell counts, cellular changes of the spleen and influx of peripheral immune cells (MPO-positive neutrophils) into the brain. Pre-oligodendrocytes (preOLs) and myelin basic protein (MBP) were detected to determine white matter injury. Electro-encephalography (EEG) was recorded to assess functional impairment by interburst interval (IBI) length analysis.

Results: Global HI resulted in profound activation and proliferation of microglia in the hippocampus, periventricular and subcortical white matter. In addition, non-preferential mobilization of white blood cells into the circulation was observed within 1 day after global HI and a significant influx of neutrophils into the brain was detected 7 days after the global HI insult. Furthermore, global HI resulted in marked involution of the spleen, which could not be explained by increased splenic apoptosis. In concordance with cerebral inflammation, global HI induced severe brain atrophy, region-specific preOL vulnerability, hypomyelination and persistent suppressed brain function.

Conclusions: Our data provided evidence that global HI in preterm ovine fetuses resulted in profound cerebral inflammation and mobilization of the peripheral innate immune system. These inflammatory responses were paralleled by marked injury and functional loss of the preterm brain. Further understanding of the interplay between preterm brain inflammation and activation of the peripheral immune system following global HI will contribute to the development of future therapeutic interventions in preterm HIE.
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http://dx.doi.org/10.1186/1742-2094-10-13DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3614445PMC
January 2013

Heart rate-mediated blood pressure control in preterm fetal sheep under normal and hypoxic-ischemic conditions.

Pediatr Res 2013 Apr 22;73(4 Pt 1):420-6. Epub 2013 Jan 22.

Neonatal Intensive Care Unit, Máxima Medical Centre, Veldhoven, The Netherlands.

Background: The understanding of hypoxemia-induced changes in baroreflex function is limited and may be studied in a fetal sheep experiment before, during, and after standardized hypoxic conditions.

Methods: Preterm fetal lambs were instrumented at 102 d gestation (term: 146 d). At 106 d, intrauterine hypoxia--ischemia was induced by 25 min of umbilical cord occlusion (UCO). Baroreflex-related fluctuations were calculated at 30-min intervals during the first week after UCO by transfer function (cross-spectral) analysis between systolic blood pressure (SBP) and R-R interval fluctuations, estimated in the low-frequency (LF, 0.04-0.15 Hz) band. LF transfer gain (baroreflex sensitivity) and delay (s) reflect the baroreflex function.

Results: Baseline did not differ in LF transfer gain and delay between controls and the UCO group. In controls, LF gain showed postnatal increase. By contrast, LF gain gradually decreased in the UCO group, resulting in significantly lower values 4-7 d after UCO. In the UCO group, LF delay increased and differed significantly from controls.

Conclusion: Our results show that intrauterine hypoxia-ischemia results in reduced baroreflex sensitivity over a period of 7 d, indicating limited efficacy to buffer BP changes by adapting heart rate. Cardiovascular dysregulation may augment already present cerebral damage after systemic hypoxia-ischemia in the reperfusion period.
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http://dx.doi.org/10.1038/pr.2013.15DOI Listing
April 2013