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3 March 2011 Reproducibility of an imaging based prostate cancer prognostic assay
Faisal M. Khan, Douglas Powell, Valentina Bayer-Zubek, Rui Soares, Allison Mott, Gerardo Fernandez, Ricardo Mesa-Tejada, Michael J. Donovan
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Proceedings Volume 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment; 79661O (2011) https://doi.org/10.1117/12.878241
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
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 biological specimens. Three different histological sections are analyzed for morphometric as well as immunofluorescence protein expression properties within areas of tumor digitally masked by expert pathologists. The assay was developed on a multi-institution cohort of up to 9 images from each of 1027 patients. The variation in histological sections, staining, pathologist tumor masking and the region of image acquisition all have the potential to significantly impact imaging features and consequently the reproducibility of the assay's results for the same patient. This study analyzed the reproducibility of the assay in 50 patients who were re-processed within 3 months in a blinded fashion as de-novo patients. The key assay results reported were in agreement in 94% of the cases. The two independent endpoints of risk classification reproduced results in 90% and 92% of the predictions. This work presents one of the first assessments of the reproducibility of a commercial assay's results given the inherent variations in images and quantitative imaging characteristics in a commercial setting.
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Faisal M. Khan, Douglas Powell, Valentina Bayer-Zubek, Rui Soares, Allison Mott, Gerardo Fernandez, Ricardo Mesa-Tejada, and Michael J. Donovan "Reproducibility of an imaging based prostate cancer prognostic assay", Proc. SPIE 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, 79661O (3 March 2011); https://doi.org/10.1117/12.878241
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KEYWORDS
Image segmentation
Error analysis
Tumors
Prostate cancer
Image analysis
Prostate
Reliability
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