Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Genetic and/or other biological marker prediction data are generated based on inputting medical image data to a suitably trained machine learning model, where the output genetic prediction data not only indicate a prediction of genetic features and/or other biological markers for a subject, but also a measure of uncertainty in each of those predictions. As an example, a transductive learning Gaussian process model is used to generate the genetic and/or other biological marker predication data and corresponding predictive uncertainty data. As another example, a knowledge-infused global-local data fusion model can be used for spatial predictive modeling.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.