Gerardo Fernandez

US Patent:

20100184093, Jul 22, 2010

Filed:

Aug 28, 2009

Appl. No.:

12/584048

Inventors:

Michael Donovan - Cambridge MA, US
Faisal Khan - Fishkill NY, US
Jason Alter - Stamford CT, US
Gerardo Fernandez - Yorktown Heights NY, US
Ricardo Mesa-Tejada - Pleasantville NY, US
Douglas Powell - Bronxville NY, US
Valentina Bayer Zubek - Yonkers NY, US
Stefan Hamann - New Rochelle NY, US
Carlos Cordon-Cardo - New York NY, US
Jose Costa - Guilford CT, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G01N 33/53
C12M 1/34

US Classification:

435 721, 4352871

Abstract:

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 whether a patient is likely to have a favorable pathological stage of prostate cancer, where the model is based on features including one or more (e.g., all) of preoperative PSA, Gleason Score, a measurement of expression of androgen receptor (AR) in epithelial and stromal nuclei and/or a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of a ratio of area of epithelial nuclei outside gland units to area of epithelial nuclei within gland units, and a morphometric measurement of area of epithelial nuclei distributed away from gland units. In some embodiments, quantitative measurements of protein expression in cell lines are utilized to objectively assess assay (e.g., multiplex immunofluorescence (IF)) performance and/or to normalize features for use within a predictive model.

US Patent:

20130080134, Mar 28, 2013

Filed:

Jun 4, 2012

Appl. No.:

13/488176

Inventors:

Michael Donovan - Newton MA, US
Faisal Khan - Fishkill NY, US
Jason Alter - Carlsbad CA, US
Gerardo Fernandez - San Jose CA, US
Ricardo Mesa-Tejada - Pleasantville NY, US
Douglas Powell - Gettysburg PA, US
Valentina Bayer Zubek - Yonkers NY, US
Stefan Hamann - Cambridge MA, US
Jose Costa - Guilford CT, US

Assignee:

Fundação D. Anna Sommer Champalimaud e Dr. Carlos Montez Champalimaud - Lisboa

International Classification:

G06F 19/00

US Classification:

703 11

Abstract:

In general, one aspect of the subject matter described in this specification can be embodied in methods for assessing risk associated with prostate cancer, the methods including the actions of receiving patient data, comparing, with a processor executing code, the patient data to one or more predictive models, the one or more predictive models comprising at least one of (a) a disease progression (DP) model, the DP model being configured to predicts a likelihood of developing significant disease progression, and (b) a favorable pathology (FP) model, the FP model being configured to predict a likelihood of having organ confined, low grade disease in a prostatectomy, and outputting one or more results of the comparison Other embodiments of the various aspects include corresponding systems, apparatus, and computer program products.

US Patent:

20100177950, Jul 15, 2010

Filed:

Jul 27, 2009

Appl. No.:

12/462041

Inventors:

Michael Donovan - Cambridge MA, US
Faisal Khan - Fishkill NY, US
Gerardo Fernandez - Yorktown Heights NY, US
Ali Tabesh - New York NY, US
Ricardo Mesa-Tejada - Pleasantville NY, US
Carlos Cordon-Cardo - New York NY, US
Jose Costa - Guilford CT, US
Stephen Fogarasi - Pawling NY, US
Yevgen Vengrenyuk - Mamaroneck NY, US

Assignee:

Aureon Laboratories, Inc. - Yonkers NY

International Classification:

G06K 9/00
G06F 19/00

US Classification:

382133, 702 19

Abstract:

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.