Quantitative imaging phenotypic classifier for distinguishing radiation effects from tumor recurrence in Glioblastoma .

用于区分胶质母细胞瘤的放射效应和肿瘤复发的定量成像表型分类器。

基本信息

批准号：
10375650
负责人：
Manmeet Ahluwalia
金额：
$ 7.22万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-30 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10375650
关键词：
Acute Adoption Aftercare Anatomy Architecture Benign Biological Biophysics Biopsy Brain Cephalic Chemotherapy and/or radiation Clinic Clinical Clinical Treatment Clinical assessments Collaborations Detection Disease Ensure Entropy Female Freezing Functional disorder Gadolinium Gender Glioblastoma Human Image Intuition Lesion MRI Scans Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Malignant Neoplasms Modeling Morphology Operative Surgical Procedures Pathologic Pathology Patients Perfusion Phenotype Positron-Emission Tomography Prediction of Response to Therapy Prognosis Prospective cohort Reader Recurrence Recurrent tumor Reporting Reproducibility Research Resort Retrospective Studies Risk Sampling Sampling Errors Scanning Shapes Site Specimen Surface Texture Tissues Tumor Biology Tumor Tissue Universities University Hospitals Validation Vasodilation Visual Work base brain parenchyma cancer recurrence chemoradiation clinical care clinical research site clinically actionable cohort deep learning diagnostic accuracy diagnostic technologies diagnostic tool follow-up imaging modality improved innovation male neuro-oncology noninvasive diagnosis quantitative imaging radiation effect radiological imaging radiologist radiomics sex sexual dimorphism standard care success support tools treatment effect tumor tumor microenvironment

项目摘要

ABSTRACT: Over 14,000 Glioblastoma (GBM) patients annually in the US undergo a combination of cranial surgery, chemotherapy, and radiation as standard treatment for their aggressive cancer. Unfortunately, ~40% of these patients will be identified with a suspicious lesion on a post-chemo-radiation follow up MRI scan (T1w, T2w, FLAIR). A significant challenge in the management of GBM tumors is the differentiation of these lesions as tumor recurrence or benign treatment-related radiation effects (TRRE). These conditions mimic each other, clinically and radiographically. Unfortunately, in the absence of reliable diagnostic tools, patients with TRRE will undergo an unnecessary and avoidable invasive stereotactic brain biopsy (St-Bx) for confirmation of disease absence. However, even the invasive St-Bx has an accuracy of 85-90% due to sampling errors associated with obtaining a biopsy tissue which may not be representative of the underlying disease pathology. Consequently, building non-invasive decision support tools which yield a diagnostic accuracy that is non-inferior to St-Bx, represents an attractive solution for obviating unnecessary intra-cranial St-Bx in patients with benign radiation effects. Our group has developed a new Image-based Recurrence Risk Classifier (IRRisC) using routine MRI scans, that has demonstrated an accuracy of 85% in distinguishing tumor recurrence from TRRE, on n=58 studies. Our initial set of IRRisC features comprise disorder in gradient orientations on Gadolinium (Gd)-T1w MRI which have been shown to be significantly higher in tumor recurrence compared to TRRE. Interestingly, we have recently also demonstrated that construction of separate classifiers for males and females yielded significantly improved prognosis of GBM survival compared to an ‘all-comers’ model. In this R01 project, we seek to further improve and validate the accuracy of IRRisC by expanding our initial feature set (using Gd-T1w MRI) to include (1) additional features from anatomical (T2w, FLAIR) and functional MR sequences (perfusion), (2) a new class of biophysical deformation attributes from “normal” brain parenchyma, and (3) construction of sex-specific models to exploit sexual-dimorphism in GBM, for distinguishing tumor recurrence from TRRE. Overcoming limitations of previous work pertaining to small samples and lack of histopathological validation, our work will utilize the largest multi-institutional histopathologically confirmed cohort till date of n=470 studies of TRRE and tumor recurrence, to harmonize and validate IRRisC. Further we will establish the biological underpinning of our IRRisC features by evaluating their association with histopathological hallmarks of TRRE and tumor recurrence. Finally, IRRisC will be validated as decision support in a machine-reader study at 3 clinical sites. Criteria for success for IRRisC is that it will (a) be non-inferior to the accuracy of St-Bx (~85-90%), and (b) identify no more than 50% of patients with TRRE as having cancer. These criteria will ensure that IRRisC is clinically actionable as a robust and reliable classifier, by obviating at least 50% of unnecessary intra-cranial biopsies in patients with TRRE, while also maintaining a high true positive rate for cancer recurrence.

摘要：美国每年有超过 14,000 名胶质母细胞瘤 (GBM) 患者接受颅脑联合手术不幸的是，手术、化疗和放疗作为侵袭性癌症的标准治疗方法。这些患者将在化疗后随访 MRI 扫描（T1w、T2w、 GBM 肿瘤治疗中的一个重大挑战是将这些病变区分为肿瘤。复发或良性治疗相关放射效应 (TRRE) 在临床上这些情况彼此相似。不幸的是，在缺乏可靠的诊断工具的情况下，TRRE 患者将接受放射学检查。不必要且可避免的侵入性立体定向脑活检 (St-Bx)，以确认疾病不存在。然而，由于与获取数据相关的采样误差，即使是侵入式 St-Bx 的准确度也达到 85-90%。活检组织可能无法代表所测试的潜在疾病病理学。非侵入性决策支持工具的诊断准确性不低于 St-Bx，代表了这是一种有吸引力的解决方案，可避免对良性放射效应患者进行不必要的颅内 St-Bx 治疗。我们的团队使用常规 MRI 扫描开发了一种新的基于图像的复发风险分类器 (IRRisC)，在我们的 n=58 项研究中，该方法在区分肿瘤复发和 TRRE 方面的准确度为 85%。初始的 IRRisC 特征集，包括钆 (Gd)-T1w MRI 上梯度方向的紊乱，其具有与 TRRE 相比，肿瘤复发率显着更高。还表明，为男性和女性构建单独的分类器可以显着改善与“所有人”模型相比，GBM 生存的预后在这个 R01 项目中，我们寻求进一步改进。并通过扩展我们的初始特征集（使用 Gd-T1w MRI）来验证 IRRisC 的准确性，以包括 (1) 来自解剖学（T2w、FLAIR）和功能 MR 序列（灌注）的附加特征，(2) 一类新的来自“正常”脑实质的生物物理变形属性，以及（3）性别特异性模型的构建利用 GBM 中的性别二态性来区分肿瘤复发和 TRRE 的局限性。以前的工作涉及小样本和缺乏组织病理学验证，我们的工作将利用最大的样本迄今为止，经过多机构组织病理学证实的队列，共有 470 项 TRRE 和肿瘤研究复发，以协调和验证 IRRisC。此外，我们将建立 IRRisC 的生物学基础。通过评估它们与 TRRE 的组织病理学特征和肿瘤复发的关系来确定这些特征。 IRRiC 将在 3 个临床中心的机器阅读器研究中作为决策支持进行验证。对于 IRRisC 来说，它 (a) 不低于 St-Bx 的准确度 (~85-90%)，并且 (b) 识别不超过 50% TRRE 患者患有癌症的比例这些标准将确保 IRRiC 作为一种临床可行的方法。强大而可靠的分类器，通过消除至少 50% 的患者不必要的颅内活检 TRRE，同时还保持癌症复发的高真阳性率。