Johns Hopkins University

University of Tulsa

Urocor, Inc.

Cytodiagnostics, Inc.

M Craig Miller

Robert W Veltri

Kaveh Ashenayi

Michael P Bacus

Alan W Partin

Donald P Coffey

Jonathan I Epstein

A method for screening individuals at risk for prostate cancer progression is disclosed. The method is useful for evaluating cells from patients at risk for recurrence of prostate cancer following surgery for prostate cancer. Specifically, the method uses specific Markovian nuclear texture factors, alone or in combination with other biomarkers, to determine whether the cancer will progress or lose organ confinement. In addition, methods of predicting the development of fatal metastatic disease by statistical analysis of selected biomarkers is also disclosed. The invention also contemplates a method that uses a neural network to analyze and interpret cell morphology data. Utilizing Markovian factors and other biomarkers as parameters, the network is first trained with a sets of cell data from known progressors and known non-progressors. The trained network is then used to predict prostate cancer progression in patient samples.

Prediction of prostate cancer progression by analysis of selected

A method for screening individuals at risk for the loss of organ confinement in prostate cancer is disclosed. The method is useful for evaluating cells from patients at risk for recurrence of prostate cancer following surgery for prostate cancer. Specifically, the method uses specific Markovian nuclear texture features, alone or in combination with other biomarkers, to determine whether the cancer will progress or lose organ confinement. In addition, methods of predicting the development of fatal metastatic disease by statistical analysis of selected biomarkers is also disclosed. The invention also contemplates a method that uses a neural network to analyze and interpret cell morphology data. Utilizing Markovian factors and other biomarkers as parameters, the network is first trained with a sets of cell data from known progressors and known non-progressors. The trained network is then used to predict the loss of organ confinement by evaluating patient samples.

Sextant core biopsy predictive mechanism for non-organ confined disease

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Guthrie, OK, United States of America

M Craig Miller