A quantitative image analysis model of prostate biopsies for predicting clinical risk in men enrolled in an active surveillance program.

2014 ◽  
Vol 32 (15_suppl) ◽  
pp. e16002-e16002
Author(s):  
Faisal M Khan ◽  
Gerardo Fernandez ◽  
Richard Scott ◽  
Ray S. Lance ◽  
Carlos Cordon-Cardo ◽  
...  
Keyword(s):  
Image Analysis ◽  
Active Surveillance ◽  
Quantitative Image Analysis ◽  
Surveillance Program ◽  
Analysis Model ◽  
Clinical Risk ◽  
Prostate Biopsies ◽  
Quantitative Image ◽  
Active Surveillance Program

A quantitative image analysis model of prostate biopsies for predicting clinical risk in men enrolled in an active surveillance program.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 111-111
Author(s):  
Faisal M Khan ◽  
Gerardo Fernandez ◽  
Richard Scott ◽  
Raymond S. Lance ◽  
Carlos Cordon-Cardo ◽  
...  
Keyword(s):  
Image Analysis ◽  
Clinical Features ◽  
Active Surveillance ◽  
Relative Proportion ◽  
Quantitative Image Analysis ◽  
Multivariate Model ◽  
Gleason Grade ◽  
Clinical Risk ◽  
Quantitative Image ◽  
Univariate Distribution

111 Background: Quantitative image analysis of the prostate needle biopsy (PNB) has proven to be a robust and predictive platform for prostate cancer (PCa) prognosis. We sought to determine the performance of quantitative metrics in identifying which patients enrolled in an active surveillance (AS) program are most likely to present with significant clinical risk, including subsequent biopsy Gleason grade (GG) upgrading and/or a short (less than 24 month) prostate-specific antigen (PSA) doubling time (PSADT). Methods: One hundred sixty two AS patients (median age 70, 94% cT1-T2a, 85% <=GS6, median PSA 5.9 ng/mL) with overall 8 year median follow up and available diagnostic PNB specimens were analyzed. Computerized image analysis derived quantitative biometric features representing PCa morphology and immunofluorescent (IF) biomarkers from the PNB. Multivariate models predicting either GG upgrading on a subsequent PNB or a PSADT less than 24 months were evaluated. The AUC/concordance index (CI), sensitivity, specificity and hazard ratio (HR) were used to assess performance. Results: Univariate distribution of selective features, notably expression levels of AR and Ki67, were reflective of a low risk cohort.A multivariate model with three quantitative imaging metrics was trained with a CI of 0.77. Men at high risk within 24 months of the PNB were identified with 80% sensitivity and 73% specificity, HR of 5.7. The most important feature measured the relative proportion of tumor epithelial nuclei that were both Androgen Receptor and alpha-methylacyl-CoA racemase positive. The other two features were morphological assessments of epithelial cellular area compared to luminal area. Of note, clinical features such as age, GG and PSA were not selected in competition with the imaging metrics. Conclusions: Quantitative image analysis of morphology and IF biomarker expression in the PNB outperformed standard clinical features in a multivariate model to accurately predict which AS patients are at risk for Gleason upgrading and/or a shortened PSADT. Identifying such patients may prove beneficial in the primary treatment decision process.

Digital image acquisition, processing, and analysis in a polymer microscopy laboratory

1994 ◽  
Vol 52 ◽  
pp. 454-455
Author(s):  
Vinod K. Berry ◽  
Xiao Zhang
Keyword(s):  
United States ◽  
Image Analysis ◽  
Digital Image ◽  
Image Acquisition ◽  
Image Transmission ◽  
The United States ◽  
Quantitative Image Analysis ◽  
Quantitative Image

In recent years it became apparent that we needed to improve productivity and efficiency in the Microscopy Laboratories in GE Plastics. It was realized that digital image acquisition, archiving, processing, analysis, and transmission over a network would be the best way to achieve this goal. Also, the capabilities of quantitative image analysis, image transmission etc. available with this approach would help us to increase our efficiency. Although the advantages of digital image acquisition, processing, archiving, etc. have been described and are being practiced in many SEM, laboratories, they have not been generally applied in microscopy laboratories (TEM, Optical, SEM and others) and impact on increased productivity has not been yet exploited as well.In order to attain our objective we have acquired a SEMICAPS imaging workstation for each of the GE Plastic sites in the United States. We have integrated the workstation with the microscopes and their peripherals as shown in Figure 1.

scholarly journals Quantitative image analysis of microbial communities with BiofilmQ

2021 ◽  
Author(s):  
Raimo Hartmann ◽  
Hannah Jeckel ◽  
Eric Jelli ◽  
Praveen K. Singh ◽  
Sanika Vaidya ◽  
...  
Keyword(s):  
Image Analysis ◽  
Microbial Communities ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Software Tool ◽  
Image Cytometry ◽  
Quantitative Image Analysis ◽  
Space And Time ◽  
Quantitative Image ◽  
Three Dimensional Space

AbstractBiofilms are microbial communities that represent a highly abundant form of microbial life on Earth. Inside biofilms, phenotypic and genotypic variations occur in three-dimensional space and time; microscopy and quantitative image analysis are therefore crucial for elucidating their functions. Here, we present BiofilmQ—a comprehensive image cytometry software tool for the automated and high-throughput quantification, analysis and visualization of numerous biofilm-internal and whole-biofilm properties in three-dimensional space and time.

Quantitative Image Analysis of Nuclear Chromatin Distribution for Cytological Diagnosis

2011 ◽  
Vol 55 (5) ◽  
pp. 455-459 ◽  
Author(s):  
Ryotaro Jingu ◽  
Masafumi Ohki ◽  
Sumiko Watanabe ◽  
Sadafumi Tamiya ◽  
Setsuo Sugishima ◽  
...  
Keyword(s):  
Image Analysis ◽  
Quantitative Image Analysis ◽  
Cytological Diagnosis ◽  
Nuclear Chromatin ◽  
Quantitative Image
Sign in / Sign up

Export Citation Format

Share Document