Yevgen Vengrenyuk

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We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning(More)
Prostate tissue characteristics play an important role in predicting the recurrence of prostate cancer. Currently, experienced pathologists manually grade these prostate tissues using the Gleason scoring system, a subjective approach which summarizes the overall progression and aggressiveness of the cancer. Using advanced image processing techniques, Aureon(More)
PURPOSE To our knowledge in patients with prostate cancer there are no available tests except clinical variables to determine the likelihood of disease progression. We developed a patient specific, biology driven tool to predict outcome at diagnosis. We also investigated whether biopsy androgen receptor levels predict a durable response to therapy after(More)
OBJECTIVE To investigate whether baseline (before treatment) clinical variables and tumour specimen characteristics (including the androgen receptor, AR) from patients with castrate-resistant metastatic prostate cancer can be used to predict the time to prostate cancer-specific mortality and overall survival, as AR levels in prostate cancer have been(More)
We present a new system for automated localization and quantification of the expression of protein biomarkers in immunofluorescence (IF) microscopic images. The system includes a novel method for discriminating the biomarker signal from background, where signal may be the expression of any of the many biomarkers or counterstains used in IF. The method is(More)
Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy images of(More)
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