34 research outputs found

    Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered collaborations

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    Research in modern healthcare requires vast volumes of data from various healthcare centers across the globe. It is not always feasible to centralize clinical data without compromising privacy. A tool addressing these issues and facilitating reuse of clinical data is the need of the hour. The Federated Learning approach, governed in a set of agreements such as the Personal Health Train (PHT) manages to tackle these concerns by distributing models to the data centers instead of the traditional approach of centralizing datasets. One of the prerequisites of PHT is using semantically interoperable datasets for the models to be able to find them. FAIR (Findable, Accessible, Interoperable, Reusable) principles help in building interoperable and reusable data by adding knowledge representation and providing descriptive metadata. However, the process of making data FAIR is not always easy and straight-forward. Our main objective is to disentangle this process by using domain and technical expertise and get data prepared for federated learning. This paper introduces applications that are easily deployable as Docker containers, which will automate parts of the aforementioned process and significantly simplify the task of creating FAIR clinical data. Our method bypasses the need for clinical researchers to have a high degree of technical skills. We demonstrate the FAIR-ification process by applying it to five Head and Neck cancer datasets (four public and one private). The PHT paradigm is explored by building a distributed visualization dashboard from the aggregated summaries of the FAIR-ified datasets. Using the PHT infrastructure for exchanging only statistical summaries or model coefficients allows researchers to explore data from multiple centers without breaching privacy

    Assessing the prognostic value of tumor-infiltrating CD57+ cells in advanced stage head and neck cancer using QuPath digital image analysis

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    This study aimed to assess the prognostic value of intratumoral CD57+ cells in head and neck squamous cell carcinoma (HNSCC) and to examine the reproducibility of these analyses using QuPath. Pretreatment biopsies of 159 patients with HPV-negative, stage III/IV HNSCC treated with chemoradiotherapy were immunohistochemically stained for CD57. The number of CD57+ cells per mm2 tumor epithelium was quantified by two independent observers and by QuPath, software for digital pathology image analysis. Concordance between the observers and QuPath was assessed by intraclass correlation coefficients (ICC). The correlation between CD57 and clinicopathological characteristics was assessed; associations with clinical outcome were estimated using Cox proportional hazard analysis and visualized using Kaplan-Meier curves. The patient cohort had a 3-year OS of 65.8% with a median follow-up of 54 months. The number of CD57+ cells/mm2 tumor tissue did not correlate to OS, DFS, or LRC. N stage predicted prognosis (OS: HR 0.43, p = 0.008; DFS: HR 0.41, p = 0.003; LRC: HR 0.24, p = 0.007), as did WHO performance state (OS: HR 0.48, p = 0.028; LRC: 0.33, p = 0.039). Quantification by QuPath showed moderate to good concordance with two human observers (ICCs 0.836, CI 0.805–0.863, and 0.741, CI 0.692–0.783, respectively). In conclusion, the presence of CD57+ TILs did not correlate to prognosis in advanced stage, HPV-negative HNSCC patients treated with chemoradiotherapy. Substantial concordance between human observers and QuPath was found, confirming a promising future role for digital, algorithm driven image analysis

    Spatial concordance of DNA methylation classification in diffuse glioma.

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    BACKGROUND: Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence. METHODS: We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites. RESULTS: Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account. CONCLUSION: Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists

    Genetic predisposition to ductal carcinoma in situ of the breast.

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    BACKGROUND: Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer. It is often associated with invasive ductal carcinoma (IDC), and is considered to be a non-obligate precursor of IDC. It is not clear to what extent these two forms of cancer share low-risk susceptibility loci, or whether there are differences in the strength of association for shared loci. METHODS: To identify genetic polymorphisms that predispose to DCIS, we pooled data from 38 studies comprising 5,067 cases of DCIS, 24,584 cases of IDC and 37,467 controls, all genotyped using the iCOGS chip. RESULTS: Most (67 %) of the 76 known breast cancer predisposition loci showed an association with DCIS in the same direction as previously reported for invasive breast cancer. Case-only analysis showed no evidence for differences between associations for IDC and DCIS after considering multiple testing. Analysis by estrogen receptor (ER) status confirmed that loci associated with ER positive IDC were also associated with ER positive DCIS. Analysis of DCIS by grade suggested that two independent SNPs at 11q13.3 near CCND1 were specific to low/intermediate grade DCIS (rs75915166, rs554219). These associations with grade remained after adjusting for ER status and were also found in IDC. We found no novel DCIS-specific loci at a genome wide significance level of P < 5.0x10(-8). CONCLUSION: In conclusion, this study provides the strongest evidence to date of a shared genetic susceptibility for IDC and DCIS. Studies with larger numbers of DCIS are needed to determine if IDC or DCIS specific loci exist

    A Combined Metabonomic and Proteomic Approach Identifies Frontal Cortex Changes in a Chronic Phencyclidine Rat Model in Relation to Human Schizophrenia Brain Pathology

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    Current schizophrenia (SCZ) treatments fail to treat the broad range of manifestations associated with this devastating disorder. Thus, new translational models that reproduce the core pathological features are urgently needed to facilitate novel drug discovery efforts. Here, we report findings from the first comprehensive label-free liquid-mass spectrometry proteomic-and proton nuclear magnetic resonance-based metabonomic profiling of the rat frontal cortex after chronic phencyclidine (PCP) intervention, which induces SCZ-like symptoms. The findings were compared with results from a proteomic profiling of post-mortem prefrontal cortex from SCZ patients and with relevant findings in the literature. Through this approach, we identified proteomic alterations in glutamate-mediated Ca2+ signaling (Ca2+ /calmodulin-dependent protein kinase II, PPP3CA, and VISL1), mitochondrial function (GOT2 and PKLR), and cytoskeletal remodeling (ARP3). Metabonomic profiling revealed changes in the levels of glutamate, glutamine, glycine, pyruvate, and the Ca2+ regulator taurine. Effects on similar pathways were also identified in the prefrontal cortex tissue from human SCZ subjects. The discovery of similar but not identical proteomic and metabonomic alterations in the chronic PCP rat model and human brain indicates that this model recapitulates only some of the molecular alterations of the disease. This knowledge may be helpful in understanding mechanisms underlying psychosis, which, in turn, can facilitate improved therapy and drug discovery for SCZ and other psychiatric diseases. Most importantly, these molecular findings suggest that the combined use of multiple models may be required for more effective translation to studies of human SCZ

    EPTN consensus-based guideline for the tolerance dose per fraction of organs at risk in the brain

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    &nbsp; EPTN consensus-based guideline for the tolerance dose per fraction of organs at risk in the brain In collaboration with the European Particle Therapy Network, a consensus table of tolerance doses for Organs at Risk in the brain was created. These values were all defined as a near maximum EQD2 values. Using the linear quadratic formula the doses are recalculated depending on the number of fractions. EQD2 = equivalent dose in 2Gy-fractions D = the total dose d = the dose per fraction α/β = were derived from literature &nbsp; Please note that this table does not..
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