Background: The clinical and research value of Computed Tomography (CT) volumetry of esophageal cancer tumor size remains controversial. Development in CT technique and image analysis has made CT volumetry less cumbersome and it has gained renewed attention. The aim of this study was to assess esophageal tumor volume by semi-automatic measurements as compared to manual.
Methods: A total of 23 esophageal cancer patients (median age 65, range 51-71), undergoing CT in the portal-venous phase for tumor staging, were retrospectively included between 2007 and 2012. One radiology resident and one consultant radiologist measured the tumor volume by semiautomatic segmentation and manual segmentation. Reproducibility of the respective measurements was assessed by intraclass correlation coefficients (ICC) and by average deviation from mean.
Results: Mean tumor volume was 46 ml (range 5-137 ml) using manual segmentation and 42 ml (range 3-111 ml) using semiautomatic segmentation. Semiautomatic measurement provided better inter-observer agreement than traditional manual segmentation. The ICC was significantly higher for semiautomatic segmentation in comparison to manual segmentation (0.86, 0.56, p < 0.01). The average absolute percentage difference from mean was reduced from 24 to 14% (p < 0.001) when using semiautomatic segmentation.
Conclusions: Semiautomatic analysis outperforms manual analysis for assessment of esophageal tumor volume, improving reproducibility.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6377716 | PMC |
| http://dx.doi.org/10.1186/s12880-019-0317-5 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hyperreflective retinal foci are associated with retinal degeneration after optic neuritis in neuromyelitis optica spectrum disorders and multiple sclerosis.
Eur J Neurol
January 2025
Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine Berlin and Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Background: Hyperreflective retinal foci (HRF) visualized by optical coherence tomography (OCT) potentially represent clusters of microglia. We compared HRF frequencies and their association with retinal neurodegeneration between people with clinically isolated syndrome (pwCIS), multiple sclerosis (pwMS), aquaporin 4-IgG positive neuromyelitis optica spectrum disorder (pwNMOSD), and healthy controls (HC)-as well as between eyes with (ONeyes) and without a history of optic neuritis (ONeyes).
Methods: Cross-sectional data of pwCIS, pwMS, and pwNMOSD with previous ON and HC were acquired at Charité-Universitätsmedizin Berlin.
Swin UNETR Segmentation with Automated Geometry Filtering for Biomechanical Modeling of Knee Joint Cartilage.
Ann Biomed Eng
January 2025
Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, CCIT216, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
Purpose: Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development.
View Article and Find Full Text PDFAutomated stenosis estimation of coronary angiographies using end-to-end learning.
Int J Cardiovasc Imaging
January 2025
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.
View Article and Find Full Text PDFDeep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.
Eye (Lond)
January 2025
Department of Surgical Sciences, University of Turin, Turin, Italy.
Purpose: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-proliferative diabetic retinopathy (NPDR).
Methods: A retrospective cohort of 249 patients (498 eyes) diagnosed with NPDR was analysed. Structural OCT scans were obtained using the Heidelberg Spectralis HRA + OCT device.
FAST: Fast, free, consistent, and unsupervised oligodendrocyte segmentation and tracking system.
eNeuro
January 2025
Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA.
To develop reparative therapies for neurological disorders like multiple sclerosis (MS), we need to better understand the physiology of loss and replacement of oligodendrocytes, the cells that make myelin and are the target of damage in MS. In vivo two-photon fluorescence microscopy allows direct visualization of oligodendrocytes in the intact brain of transgenic mouse models, promising a deeper understanding of the longitudinal dynamics of replacing oligodendrocytes after damage. However, the task of tracking the fate of individual oligodendrocytes requires extensive effort for manual annotation and is especially challenging in three-dimensional images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!