Mayo Foundation for Medical Education and Research

Arizona State University

Other

Arizona Board of Regents, a Body Corporate

Kristin R Swanson

Teresa Wu

Jing Li

Leland S Hu

Nhan L Tran

J Ross Mitchell

Russell Rockne

Nathan Gaw

Hyunsoo Yoon

Andrea Hawkins-Daarud

Ellsworth C Alvord, Jr

James D Murray

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme ('GBM') are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

Systems and methods for quantifying multiscale competitive landscapes of clonal diversity in glioblastoma

Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.

Methods for using machine learning and mechanistic models for biological feature mapping with multiparametric MRI

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme ('GBM') are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor ('BAT') regions.

Embodiments of the present invention are directed to determining the spatial extent, aggressiveness, and other characteristics of various types of tumors, including glioma tumors that occur in brain tissue. Various embodiments of the present invention use parameterized computational models to characterize tumor growth and employ medical imaging technologies to generate images and other types of data from which values of parameters of the computational models are derived. Having obtained the parameters for a particular tumor, the extent of the tumor is estimated, with high accuracy, and other characteristics of the tumor are derived from the parameterized computational models.

Method and system for characterizing tumors

Growing community of inventors

Phoenix, AZ, United States of America

Kristin R Swanson