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

项目总结 2017年，每10名美国居民中就有1人以上进行了核磁共振检查。其中大部分是 脑部核磁共振。在45岁以上的人中，超过1%的脑部磁共振成像存在不确定的肿块病变。 对脑MRI的误解可能会导致显著的医源性发病率和死亡率。例如, 肿瘤性中枢神经系统炎性脱髓鞘病(CNSIDD)是常见的 被误诊为恶性肿瘤，即使在病理检查后也是如此。这导致了不适当的脑活组织检查， 清理地下建筑和辐射。虽然早期肿瘤切除与高转移性卵巢癌患者的良好预后有关 胶质瘤分级、观察、替代部位活组织检查或非手术选择通常更适合于 可包括低级别原发脑肿瘤、CNSIDD、CNS的其他不确定的肿块 淋巴瘤和脑转移。因此，为了防止医源性发病率，迫切需要可扩展和 可重复的方法将CNSIDD与其他脑部病变区分开来，并准确诊断脑肿瘤 在活组织检查之前。我们最近发表了一个多基因风险模型，证明了已知的25个胶质瘤胚系 风险变量可以估计胶质瘤的绝对风险和终生风险。这些模型的临床意义是由 通过与&gt；相关的生殖系变异，神经胶质瘤的风险增加了4倍。我们还表明， 同样的25个胚系变异可以预测胶质瘤的分子亚型。作为补充，我们有 显示不同的脑胶质瘤、CNSIDD、CNS淋巴瘤和脑转移瘤的影像特征不同。我们 已经成功地利用基于MRI的机器学习来预测高级别胶质瘤的分子亚型。 我们假设，胚系基因分型和基于核磁共振的机器学习都提供了一个机会 在手术前诊断不确定的肿块病变并预测胶质瘤分子亚型 个性化治疗。该项目有以下三个目标：目标1：开发和验证基于MRI的 机器学习模型用于区分成人弥漫性胶质瘤与致瘤性CNSIDD、CNS淋巴瘤和 原发不明的孤立性脑转移瘤。目的2：评价多基因检测的敏感性和特异性 鉴别成人弥漫性胶质瘤与致瘤性CNSIDD、中枢神经系统淋巴瘤和孤立性脑胶质瘤的胶质瘤风险模型 脑转移瘤。目的3：将多基因胶质瘤亚型模型与基于MRI的机器学习相结合 预测成人弥漫性胶质瘤分子亚型的模型，并使用前瞻性的方法验证集成模型 一群人。拟议的项目将进一步加强对患者的护理，通过确定早期MRI病变是否 实际上是神经胶质瘤。对于这些患者，早期明确的手术可能是治愈的。

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.