Fundação D. Anna De Sommer Champalimaud E Dr. Carlos Montez Champalimaud

Fundacao D. Anna Sommer Champalimaud E Dr. Carlos Montez Champalimaud

Yevgen Vengrenyuk

Carlos Cardon-Cardo

Carlos Cordon-Cardo

Gerardo Fernandez

Michael Donovan

Faisal Khan

Ricardo Mesa-Tejada

Jose Costa

Stephen Fogarasi

Ali Tabesh

Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts risk of prostate cancer progression in a patient, where the model is based on features including one or more (e.g., all) of preoperative PSA, dominant Gleason Grade, Gleason Score, at least one of a measurement of expression of AR in epithelial and stromal nuclei and a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of average edge length in the minimum spanning tree (MST) of epithelial nuclei, and a morphometric measurement of area of non-lumen associated epithelial cells relative to total tumor area. In some embodiments, the morphometric information is based on image analysis of tissue subject to multiplex immunofluorescence and may include characteristic(s) of a minimum spanning tree (MST) and/or a fractal dimension observed in the images.

Method and system for determining the risk of occurrence of prostate cancer

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Mamaroneck, NY, United States of America

Yevgen Vengrenyuk