RESUMEN
PURPOSE: Preclinical imaging, with translational potential, lacks a standardized method for defining volumes of interest (VOIs), impacting data reproducibility. The aim of this study was to determine the interobserver variability of VOI sizes and standard uptake values (SUVmean and SUVmax) of different organs using the same [18F]FDG-PET and PET/CT datasets analyzed by multiple observers. In addition, the effect of a standardized analysis approach was evaluated. PROCEDURES: In total, 12 observers (4 beginners and 8 experts) analyzed identical preclinical [18F]FDG-PET-only and PET/CT datasets according to their local default image analysis protocols for multiple organs. Furthermore, a standardized protocol was defined, including detailed information on the respective VOI size and position for multiple organs, and all observers reanalyzed the PET/CT datasets following this protocol. RESULTS: Without standardization, significant differences in the SUVmean and SUVmax were found among the observers. Coregistering CT images with PET images improved the comparability to a limited extent. The introduction of a standardized protocol that details the VOI size and position for multiple organs reduced interobserver variability and enhanced comparability. CONCLUSIONS: The protocol offered clear guidelines and was particularly beneficial for beginners, resulting in improved comparability of SUVmean and SUVmax values for various organs. The study suggested that incorporating an additional VOI template could further enhance the comparability of the findings in preclinical imaging analyses.
Asunto(s)
Fluorodesoxiglucosa F18 , Variaciones Dependientes del Observador , Tomografía Computarizada por Tomografía de Emisión de Positrones , Fluorodesoxiglucosa F18/química , Fluorodesoxiglucosa F18/farmacocinética , Tomografía Computarizada por Tomografía de Emisión de Positrones/normas , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Humanos , Estándares de Referencia , Animales , Procesamiento de Imagen Asistido por Computador/métodos , Reproducibilidad de los ResultadosRESUMEN
Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of â¼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.