Background: Cutaneous neurofibromas (cNFs) are a major cause of disfigurement in patients with Neurofibromatosis Type 1 (NF1). However, clinical trials investigating cNF treatments lack standardised outcome measures to objectively evaluate changes in cNF size and appearance. 3D imaging has been proposed as an objective standardised outcome measure however various systems exist with different features that affect useability in clinical settings. The aim of this study was to compare the accuracy, precision, feasibility, reliability and accessibility of three imaging systems.
Materials And Methods: We compared the Vectra-H1, LifeViz-Micro and Cherry-Imaging systems. A total of 58 cNFs from 13 participants with NF1 were selected for imaging and analysis. The primary endpoint was accuracy as measured by comparison of measurements between imaging systems. Secondary endpoints included reliability between two operators, precision as measured with the average coefficient of variation, feasibility as determined by time to capture and analyse an image and accessibility as determined by cost.
Results: There was no significant difference in accuracy between the three devices for length or surface area measurements (p > 0.05), and reliability and precision were similar. Volume measurements demonstrated the most variability compared to other measurements; LifeViz-Micro demonstrated the least measurement variability for surface area and image capture and analysis were fastest with LifeViz-Micro. LifeViz-Micro was better for imaging smaller number of cNFs (1-3), Vectra-H1 better for larger areas and Cherry for uneven surfaces.
Conclusions: All systems demonstrated excellent reliability but possess distinct advantages and limitations. Surface area is the most consistent and reliable parameter for measuring cNF size in clinical trials.
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http://dx.doi.org/10.1111/srt.70020 | DOI Listing |
Sci Rep
December 2024
Department of Ecology, Faculty of Sciences, Institute of Biology, University of Pécs, Ifjúság útja 6, Pécs, 7624, Hungary.
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December 2024
School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, WA, Australia.
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December 2024
Department of Computer Engineering, Marwadi University, Rajkot, 360003, India.
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