3 March 2011 An analysis of the impact of tumor amount on the predictive power of a prostate biopsy prognostic assay
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Abstract
The Prostate Px prognostic assay offered by Aureon Biosciences is designed to predict progression post primary treatment for prostate cancer patients based on their diagnostic biopsy specimen. The assay is driven by the automated image analysis of a diagnostic prostate needle biopsy (PNB) and incorporates pathologist acquired and digitally masked images which reflect the morphometric (Hematoxylin and Eosin, H&E) and protein expression (immunofluorescence, IF) properties of the PNB. Up to 9 images (3 H&E and 6 IF) from each of 1027 patients, with varying amounts of tumor content were included in the study. We wanted to understand what was the minimal tumor volume required to maintain assay predictive robustness as a result of overall PNB tumor content and assess the impact of pathologist tumor masking variability. 232 patients were selected who had a minimum of 80% tumor volume in a 20x magnification image. In each of the three imaging domains (2 different multiplex (Mplex) IF images and one H&E), the tumor volume was artificially reduced in increments from 80% to 2.5% of the original image area. This simulated decreasing amounts of tumor as well as variations in digital tumor masking. The univariate predictive power of individual imaging domains remained robust down to the 10% tumor level, whereas the total assay was robust through the 20% to 10% tumor level. This work presents one of the first assessments of the variety in tumor amounts on the predictive power of a commercially available prognostic assay that is reliant on multiple bioimaging domains.
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Faisal M. Khan, Stephen I. Fogarasi, Douglas Powell, Gerardo Fernandez, Ricardo Mesa-Tejada, Michael J. Donovan, "An analysis of the impact of tumor amount on the predictive power of a prostate biopsy prognostic assay", Proc. SPIE 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, 796616 (3 March 2011); doi: 10.1117/12.878233; https://doi.org/10.1117/12.878233
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