Diagnosis of indeterminate brain lesions using MRI-based machine learning and polygenic risk models

PROJECT SUMMARY In 2017 an MRI was performed at a rate of over one for every 10 US residents. The majority of these were brain MRIs. Indeterminate mass lesions are present on over 1% of brain MRIs in individuals over 45 years old. Misinterpretation of brain MRI can lead to significant iatrogenic morbidity and mortality. For example, tumefactive Central Nervous System Inflammatory Demyelinating Disease (CNSIDD) is commonly misdiagnosed as a malignancy, even following pathological review. This results in inappropriate brain biopsies, debulking and radiation. While early tumor resection is associated with favorable outcome in patients with high- grade glioma, observation, biopsy at an alternate site or nonsurgical options are often more appropriate for other indeterminate mass lesions that can encompass low-grade primary brain tumor, CNSIDD, CNS lymphoma and brain metastasis. Thus, to prevent iatrogenic morbidity, there is a critical need for scalable and reproducible methods to distinguish CNSIDD from other brain lesions, and to accurately diagnose brain tumors prior to biopsy. We recently published a polygenic risk model demonstrating that the 25 known glioma germline risk variants can estimate absolute and lifetime glioma risk. The clinical significance of these models is driven by germline variants that are associated with >4-fold increased risk of glioma. We have also shown that the same 25 germline variants can predict glioma molecular subtype. As a complementary approach, we have shown that imaging characteristics differ across glioma, CNSIDD, CNS lymphoma and brain metastases. We have successfully utilized MRI-based machine learning to predict the molecular subtype in high-grade glioma. We hypothesize that both germline genotyping and MRI-based machine learning provide an opportunity to diagnose indeterminate mass lesions as well as predict glioma molecular subtype prior to surgery and thus personalized treatment. The project has the following three aims: Aim 1: Develop and validate a MRI-based machine learning model to differentiate adult diffuse glioma from tumefactive CNSIDD, CNS lymphoma and solitary brain metastases of unknown primary. Aim 2: Evaluate sensitivity and specificity of the polygenic glioma risk model to differentiate adult diffuse glioma from tumefactive CNSIDD, CNS lymphoma and solitary brain metastases. Aim 3: Integrate the polygenic glioma subtype model and MRI-based machine learning model to predict adult diffuse glioma molecular subtype and validate the integrated model using a prospective cohort. The proposed project will further enhance the care of patients by determining if an early MRI lesion is actually a glioma. Early definitive surgery in these patients could be curative.