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  • SAGE Publications  (2)
  • 1
    Online Resource
    Online Resource
    SAGE Publications ; 2017
    In:  Journal of Cerebral Blood Flow & Metabolism Vol. 37, No. 2 ( 2017-02), p. 444-455
    In: Journal of Cerebral Blood Flow & Metabolism, SAGE Publications, Vol. 37, No. 2 ( 2017-02), p. 444-455
    Abstract: Parameter-free assessment of the time-to-peak (TTP) histogram, termed ‘TTP-distribution curve’ (TDC), of dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) was introduced as a robust method to evaluate cerebral perfusion. TDC-assessment works fully automatically without the need of an arterial input function, thereby providing full comparability between different measurements. In the investigated sample of 106 patients, a strong dependency of TDC on the hemodynamic state of cerebral microvessels and the arterio-venous bolus-transit time [Formula: see text] was demonstrated. Accordingly, TDC-derived [Formula: see text] was 3.3–3.7 s for control patients and 4.4 s for cerebral small vessel disease patients. Measurements of associated bolus spread velocities ν and accelerations [Formula: see text] additionally revealed a direct effect from spin–spin relaxation time T 2 -weighted white matter hyperintensity volume, considered to indicate microangiopathy in cerebral small vessel disease, on the TDC-measurements. This strongly supports the prevailing hypothesis that cerebral small vessel disease directly influences DSC-measurements, where the degree could be estimated from an analysis of TDC. While this may be used to correct DSC-parameters for undesirable effects from cerebral small vessel disease, it could also serve to potentially identify patients at risk for cerebral small vessel disease at an early stage, since a subset of patients without yet significant WHM-volume, but clearly altered hemodynamics in TDC-measurements, was identified in this study.
    Type of Medium: Online Resource
    ISSN: 0271-678X , 1559-7016
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2017
    detail.hit.zdb_id: 2039456-1
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  • 2
    In: Therapeutic Advances in Gastroenterology, SAGE Publications, Vol. 12 ( 2019-01), p. 175628481987763-
    Abstract: The management of patients with metastatic pancreatic cancer (mPC) is challenging, and the optimal treatment strategy is debated among experts. In an attempt to identify treatment decision criteria and to investigate variations in the first-line management of this disease, we performed an analysis of treatment algorithms among experts in the field of pancreatic cancer. The aim of this study was to identify relevant criteria in the complex process of patient selection and decision making for the management of mPC patients. Methods: Experts from the ABCSG (Austrian Breast and Colorectal Cancer Study Group) Pancreatic Cancer Club were contacted and agreed to participate in this analysis. Eight experts from seven centers in Austria provided their decision algorithms for the first-line treatment of patients with mPC. Their responses were converted into decision trees based on the objective consensus methodology. The decision trees were used to identify consensus and discrepancies. Results: The final treatment algorithms included four decision criteria (performance status, age, comorbidities, and symptomatic disease) and six treatment options: mFOLFIRINOX, gemcitabine + nab-paclitaxel, gemcitabine mono, 5-FU mono, gemcitabine/erlotinib, and best supportive care (BSC). Conclusions: We identified consensus for the treatment of young and fit patients with mFOLFIRINOX. With higher age and reduced performance status, gemcitabine + nab-paclitaxel was increasingly used. For patients with Eastern Co-operative Oncology Group Performance Status (ECOG PS) 4, BSC was the treatment of choice. Among experts, different decision criteria and treatment options are implemented in clinical routine. Despite multiple options in current recommendations, a consensus for specific recommendations was identified.
    Type of Medium: Online Resource
    ISSN: 1756-2848 , 1756-2848
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2440710-0
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