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- Baumann, Stefan, et al.
(author)
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Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
- 2019
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In: European Journal of Radiology. - : ELSEVIER IRELAND LTD. - 0720-048X .- 1872-7727. ; 119
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Journal article (peer-reviewed)abstract
- Purpose: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFR mL ) for the detection of lesion-specific ischemia. Method: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR amp;lt;= 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis amp;gt;= 50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. Results: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007]. Conclusions: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.
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2. |
- Muelhopt, Sonja, et al.
(author)
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Characterization of Nanoparticle Batch-To-Batch Variability
- 2018
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In: Nanomaterials. - : MDPI. - 2079-4991. ; 8:5
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Journal article (peer-reviewed)abstract
- A central challenge for the safe design of nanomaterials (NMs) is the inherent variability of NM properties, both as produced and as they interact with and evolve in, their surroundings. This has led to uncertainty in the literature regarding whether the biological and toxicological effects reported for NMs are related to specific NM properties themselves, or rather to the presence of impurities or physical effects such as agglomeration of particles. Thus, there is a strong need for systematic evaluation of the synthesis and processing parameters that lead to potential variability of different NM batches and the reproducible production of commonly utilized NMs. The work described here represents over three years of effort across 14 European laboratories to assess the reproducibility of nanoparticle properties produced by the same and modified synthesis routes for four of the OECD priority NMs (silica dioxide, zinc oxide, cerium dioxide and titanium dioxide) as well as amine-modified polystyrene NMs, which are frequently employed as positive controls for nanotoxicity studies. For 46 different batches of the selected NMs, all physicochemical descriptors as prioritized by the OECD have been fully characterized. The study represents the most complete assessment of NMs batch-to-batch variability performed to date and provides numerous important insights into the potential sources of variability of NMs and how these might be reduced.
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