Distributed learning on 20 000+ lung cancer patients - The Personal Health Train

Background and purpose Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Measurement of charged-particle event shape variables in inclusive root(s)=7 TeV proton-proton interactions with the ATLAS detector

The measurement of charged-particle event shape variables is presented in inclusive inelastic pp collisions at a center-of-mass energy of 7 TeV using the ATLAS detector at the LHC. The observables studied are the transverse thrust, thrust minor, and transverse sphericity, each defined using the final-state charged particles' momentum components perpendicular to the beam direction. Events with at least six charged particles are selected by a minimum-bias trigger. In addition to the differential distributions, the evolution of each event shape variable as a function of the leading charged-particle transverse momentum, charged-particle multiplicity, and summed transverse momentum is presented. Predictions from several Monte Carlo models show significant deviations from data

Bioengineering of living renal membranes consisting of hierarchical, bioactive supramolecular meshes and human tubular cells

Maintenance of polarisation of epithelial cells and preservation of their specialized phenotype are great challenges for bioengineering of epithelial tissues Mimicking the basement membrane and underlying extracellular matrix (ECM) with respect to its hierarchical fiber-like morphology and display of bioactive signals is prerequisite for optimal epithelial cell function in vitro We report here on a bottom-up approach based on hydrogen-bonded supramolecular polymers and ECM-peptides to make an electro-spun bioactive supramolecular mesh which can be applied as synthetic basement membrane The supramolecular polymers used self-assembled into nano-meter scale fibers while at micro-meter scale fibers were formed by electro-spinning We introduced bioactivity into these nano-fibers by intercalation of different ECM-peptides designed for stable binding Living kidney membranes were shown to be bioengineered through culture of primary human renal tubular epithelial cells on these bioactive meshes Even after a long-term culturing period of 19 days we found that the cells on bioactive membranes formed tight monolayers while cells on non-active membranes lost their monolayer integrity Furthermore the bioactive membranes helped to support and maintain renal epithelial phenotype and function Thus incorporation of ECM-peptides Into electro-spun meshes via a hierarchical supramolecular method is a promising approach to engineer bioactive synthetic membranes with an unprecedented structure This approach may in future be applied to produce living bioactive membranes for a I:no-artificial kidney (C) 2010 Elsevier Ltd All rights reserve

Heme Oxygenase-1 and breast cancer resistance protein protect against heme-induced toxicity

Heme is the functional group of diverse hemoproteins and crucial for many cellular processes. However, heme is increasingly recognized as a culprit for a wide variety of pathologies, including sepsis, malaria, and kidney failure. Excess of free heme can be detrimental to tissues by mediating oxidative and inflammatory injury. Protective mechanisms against free heme are therefore pivotal for cellular survival. We postulated that overexpression of Heme Oxygenase-1 (HO-1) and Breast Cancer Resistance Protein (BCRP) would protect against heme-induced cytotoxicity. HO-1 is a heme-degrading enzyme generating carbon monoxide, iron, and biliverdin/bilirubin, while BCRP is a heme efflux transporter. Human embryonic kidney cells were transduced using a baculovirus system as a novel strategy to efficiently overexpress HO-1 and BCRP. Exposing cells to heme resulted in a dose-dependent increase in reactive oxygen species formation, DNA damage and cell death. Heme-induced cell death was significantly attenuated when cells overexpressed HO-1, BCRP, or both. The protective effects of HO-1 overexpression were most pronounced, while co-treatment with the HO-activity inhibitor tin mesoporphyrin reversed these protective effects. Also cells treated with the anti-oxidants N-acetylcysteine or HO-effector molecule bilirubin showed protection against heme insults, which may explain the increased protection by HO-1 compared to BCRP. In conclusion, both HO-1 and BCRP protect against heme-induced toxicity and may thus form novel therapeutic targets for heme-mediated pathologies

External validation of an NTCP model for acute esophageal toxicity in locally advanced NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

BACKGROUND AND PURPOSE: We externally validated a previously established multivariable normal-tissue complication probability (NTCP) model for Grade &gt;/=2 acute esophageal toxicity (AET) after intensity-modulated (chemo-)radiotherapy or volumetric-modulated arc therapy for locally advanced non-small cell lung cancer. MATERIALS AND METHODS: A total of 603 patients from five cohorts (A-E) within four different Dutch institutes were included. Using the NTCP model, containing predictors concurrent chemoradiotherapy, mean esophageal dose, gender and clinical tumor stage, the risk of Grade &gt;/=2 AET was estimated per patient and model discrimination and (re)calibration performance were evaluated. RESULTS: Four validation cohorts (A, B, D, E) experienced higher incidence of Grade &gt;/=2 AET compared to the training cohort (49.3-70.2% vs 35.6%; borderline significant for one cohort, highly significant for three cohorts). Cohort C experienced lower Grade &gt;/=2 AET incidence (21.7%, p&lt;0.001). For three cohorts (A-C), discriminative performance was similar to the training cohort (area under the curve (AUC) 0.81-0.89 vs 0.84). In the two remaining cohorts (D-E) the model showed poor discriminative power (AUC 0.64 and 0.63). Reasonable calibration performance was observed in two cohorts (A-B), and recalibration further improved performance in all three cohorts with good discrimination (A-C). Recalibration for the two poorly discriminating cohorts (D-E) did not improve performance. CONCLUSIONS: The NTCP model for AET prediction was successfully validated in three out of five patient cohorts (AUC &gt;/=0.80). The model did not perform well in two cohorts, which included patients receiving substantially different treatment. Before applying the model in clinical practice, validation of discrimination and (re)calibration performance in a local cohort is recommended

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145237/1/mp12967_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145237/2/mp12967.pd

Machine learning algorithms for outcome prediction in (chemo) radiotherapy:An empirical comparison of classifiers (vol 45, pg 3449, 2018)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147816/1/mp13329-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147816/2/mp13329.pd