We seek to improve the predictive accuracy of the nomogram to predict survival for patients with castrate mets disease through the addition of pathological data, the results of automated machine vision based image analysis of H\&E stained tumor tissue developed at Aureon Biosciences,and molecular biomarker studies (25 markers) determined by immunohistochemistry on tissue microarrays prepared from paraffin-embedded tumor.
Study Type
OBSERVATIONAL
Enrollment
758
Memorial Sloan-Kettering Cancer Center
New York, New York, United States
The analysis for the progressive castrate mets disease population consists of two analytical steps. The first step involved the development of a predictive model of pt survival using supervised multivariate analytical (SMA)techniques
Time frame: 2 years
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