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

Aetna Inc.

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

Faisal Khan

Michael Donovan

Gerardo Fernandez

Peter Ajemba

Jack Zeineh

Richard Jonathan Scott

Qiuhua Liu

Janakiramanan Ramachandran

Carlos Cordon-Cardo

Ryan T Berns

Jamal Timsah

Gaurav Sharma

Ricardo Mesa-Tejada

Jose Costa

Hualiang Li

Radhika G Athalye

Yue Xiao

Sanjeev Bollam

William R Jones

Jingran Li

Carlos Cardon-Cardo

Stefan Hamann

Ali Tabesh

Stephen Fogarasi

Yevgen Vengrenyuk

Jason Alter

Doug Powell

Douglas Powell

Valentina Bayer Zubek

Douglas A Powell

Richard Scott

Embodiments of the disclosure provide a system and method of allocating a resource based on myriad input data. In some embodiments, the myriad input data include membership information, claims data, transactional data, etc. The myriad input data are sorted and organized in a meaningful association relationship before applied to a resource allocation modeling algorithm. The resource allocation modeling algorithm provides estimated resource necessary for the application chosen. For example, an insurance company may use membership information, claims data, transactional data, etc., to estimate how much reserves or funds it should hold to cover future claims within a certain timeframe.

Resource allocation

This present invention systems and methods of accessing/monitoring the responsiveness of a breast cancer to a therapeutic compound.

Systems and methods for treating, diagnosing and predicting the response to therapy of breast cancer

Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.

Systems and methods for segmentation and processing of tissue images and feature extraction from same for treating, diagnosing, or predicting medical conditions

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

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.

System for evaluating a pathological stage of prostate cancer

Growing community of inventors

Fishkill, NY, United States of America

Faisal Khan