Mayo Foundation for Medical Education and Research

Arizona State University

Arizona Board of Regents, a Body Corporate

Leland S Hu

Teresa Wu

Jing Li

Kristin R Swanson

J Ross Mitchell

Nhan L Tran

Andrea Hawkins-Daarud

Nathan Gaw

Hyunsoo Yoon

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.

A system and method for characterizing tissues of a subject using multi-parametric imaging are provided. In some aspects, the method includes receiving a set of multi-parametric magnetic resonance ('MR') images acquired from a subject using an MR imaging system, and selecting at least one region of interest ('ROI') in the subject using one or more images in the set of multi-parametric MR images. The method also includes performing a texture analysis on corresponding ROIs in the set of multi-parametric MR images to generate a set of texture features, and applying a classification scheme, using the set of texture features, to characterize tissues in the ROI. The method further includes generating a report indicative of characterized tissues in the ROI.

Growing community of inventors

Phoenix, AZ, United States of America

Leland S Hu