Objective: Tissue biobanks are an important source for discovery and validation studies aiming for new proteins that are causally related with disease development. There is an increasing demand for accurate and reproducible histological characterization, especially for subsequent analysis and interpretation of data in association studies. We assessed reproducibility of one semiquantative and two quantitative methods for histological tissue characterization. We introduce a new automated method for whole digital slide quantification. Carotid atherosclerotic plaques were used to test reproducibility.
Methods: 50 atherosclerotic plaques that were obtained during carotid endarterectomy were analysed. For the semiquantitative analysis, 6 different plaque characteristics were scored in categories by two independent observers, and Cohen's κ was used to test intra- and interobserver reproducibility. The computer-aided method (assessed by two independent observers) and automated method were tested on CD68 (for macrophages) and α smooth muscle actin (for smooth muscle cells) stainings. Agreement for these two methods (done on a continuous scale) was assessed by intraclass correlation coefficients (ICCs).
Results: For the semiquantitative analysis, κ values ranged from 0.55 to 0.69 for interobserver variability, and were slightly higher for intraobserver reproducibility in both observers. The computer-aided method yielded intra- and interobserver ICCs between 0.6 and 0.9. The new automated method performed most optimal regarding reproducibility, with ICCs ranging from 0.92 to 0.97.
Conclusions: The analysis of performance of three methods for histological slide characterization on carotid atherosclerotic plaques showed high precision and agreement in repeated measurements for the automated method for whole digital slide quantification. We suggest that this method can fulfill the need for reproducible histological quantification.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4277457 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0115907 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
FL-W3S: Cross-domain federated learning for weakly supervised semantic segmentation of white blood cells.
Int J Med Inform
January 2025
Department of Computer Science and Artificial Intelligence, University of Udine, 33100, Italy.
Background: Segmentation models for clinical data experience severe performance degradation when trained on a single client from one domain and distributed to other clients from different domain. Federated Learning (FL) provides a solution by enabling multi-party collaborative learning without compromising the confidentiality of clients' private data.
Methods: In this paper, we propose a cross-domain FL method for Weakly Supervised Semantic Segmentation (FL-W3S) of white blood cells in microscopic images.
Automated detection of traumatic bleeding in CT images using 3D U-Net# and multi-organ segmentation.
Biomed Phys Eng Express
January 2025
Chiba University Center for Frontier Medical Engineering, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba, 263-8522, JAPAN.
Traumatic injury remains a leading cause of death worldwide, with traumatic bleeding being one of its most critical and fatal consequences. The use of whole-body computed tomography (WBCT) in trauma management has rapidly expanded. However, interpreting WBCT images within the limited time available before treatment is particularly challenging for acute care physicians.
View Article and Find Full Text PDFOncology Provider and Patient Perspectives on a Cardiovascular Health Assessment Tool utilized during Post-treatment Survivorship Care in Community Oncology (Results from WF-1804CD): Mixed Methods Observational Study.
J Med Internet Res
January 2025
Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, US.
Background: Most cancer survivors have multiple cardiovascular risk factors, increasing their risk of poor cardiovascular and cancer outcomes. The Automated Heart-Health Assessment (AH-HA) tool is a novel electronic health record clinical decision support tool based on the American Heart Association's Life's Simple 7 cardiovascular health (CVH) metrics to promote CVH assessment and discussion in outpatient oncology. Before proceeding to future implementation trials, it is critical to establish the acceptability of the tool among providers and survivors.
View Article and Find Full Text PDFEfficacy of a clinical decision support tool to promote guideline concordant evaluations in patients with high-risk microscopic hematuria: a cluster randomized quality improvement project.
J Urol
January 2025
Department of Population Health, NYU Grossman School of Medicine, New York, New York.
Purpose: We aimed to determine whether implementation of clinical decision support (CDS) tool integrated into the electronic health record (EHR) of a multi-site academic medical center increased the proportion of patients with American Urological Association (AUA) "high risk" microscopic hematuria (MH) who receive guideline concordant evaluations.
Materials And Methods: We conducted a two-arm cluster randomized quality improvement project in which 202 ambulatory sites from a large health system were randomized to either have their physicians receive at time of test results an automated CDS alert for patients with 'high-risk' MH with associated recommendations for imaging and cystoscopy (intervention) or usual care (control). Primary outcome was met if a patient underwent both imaging and cystoscopy within 180 days from MH result.
Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
PLoS One
January 2025
Faculty of Science and Engineering, School of Computer Science, University of Hull, Hull, United Kingdom.
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image.
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!