The goal of this work is to assess the clinical value of voxel-wise predictive spatial maps of tumor heterogeneity that directly reflect histopathologically defined tumor biology. It is well known that tissue samples used for clinical diagnosis come from a relatively small portion of a vastly heterogenous lesion and are obtained infrequently during the course of the disease. Non-invasive imaging markers that are able to assess intratumoral heterogeneity and serially monitor biological properties of the tumor are critical for assessing response to therapy and directing patient care. The modalities that have shown the most promise in quantifying surrogate markers of malignant characteristics in patients with gliomas include diffusion-weighted MRI, perfusion-weighted MRI, and 1H MR spectroscopic imaging (MRSI). During previous cycles of our P01 and SPORE projects, we have accumulated multi-parametric physiologic and metabolic imaging data from pre-surgical scans in order to target over 2000 tissue samples from more than 750 patients with glioma. These samples are unique in that they have each been specifically selected to target heterogeneous regions of tumor biology, including: hypoxia, proliferation, cellularity, gliosis, and malignant transformation using a combination of anatomic, physiologic, and metabolic imaging. Using this well- characterized cohort, our novel approach will leverage multi-parametric imaging features from tissue samples obtained from known image coordinates as well as advanced statistical-, machine-, and deep-learning models to construct spatial maps that predict tumor biology. In a new cohort of 400 patients with glioma (200 newly- diagnosed and 200 at the time of suspected recurrence) we will prospectively acquire multi-modal MRI and 1200 tissue samples with known image coordinates that are targeted based on our predictive spatial maps to both validate the best performing models in this independent test set and generate enhanced spatial maps to assess clinical value at time points that are critical for making decisions about patient care.
In Aim 1, we will predict intra-tumoral heterogeneity and the extent of infiltrating tumor and in newly-diagnosed glioma using multi-parametric imaging from tissue samples with known imaging coordinates in order to identify areas of malignant characteristics that will direct tissue sampling for a more accurate diagnosis and predict the spatial location and characteristics of residual disease.
Aim 2 will define characteristics of treatment related changes vs recurrent tumor and malignant transformation within lower grade molecular sub-groups of glioma within patients undergoing surgery for suspected tumor progression. This innovative study will enhance and expand current strategies for evaluating patients with glioma and provide a framework for incorporating newly identified imaging, molecular, and genomic markers. This is imperative for intelligently combining novel imaging data and generating comprehensive predictive spatial maps that can be integrated with current response assessment criteria for evaluating standard and experimental treatments.
The goal of Project 1 is to assess the clinical value of combining multi-parametric imaging with novel advances in statistical modeling, machine learning, and artificial intelligence to evaluate tumor heterogeneity and identify regions at risk for tumor progression in patients with glioma. Predictive spatial maps of tumor biology will be generated using image-guided tissue samples to link anatomic, physiological and metabolic imaging parameters with histological characteristics. This approach will contribute to patient care by directing tissue sampling to make more accurate diagnoses, improving the characterization of residual disease, assisting in the planning of focal therapy, and detecting malignant progression.
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