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：每年在美国每年有14,000多名胶质母细胞瘤（GBM）患者经历了颅骨的组合手术，化学疗法和放射线作为其侵袭性癌症的标准治疗方法。不幸的是，约有40％这些患者将在改性后随访中使用可疑病变确定MRI扫描（T1W，T2W，，T2W，天赋）。 GBM肿瘤管理的一个重大挑战是这些病变的分化为肿瘤复发或良性治疗相关的辐射效应（TRRE）。这些条件彼此模仿，临床上和射线照相。不幸的是，在没有可靠的诊断工具的情况下，TRRE患者将经历一种不必要且可避免的侵入性立体定向脑活检（ST-BX），用于确认疾病缺失。但是，由于与获得相关的抽样错误，即使是侵入性ST-BX的精度为85-90％可能无法代表潜在疾病病理的活检组织。因此，建筑物非侵入性决策支持工具，该工具产生了不属于ST-BX的诊断准确性，代表具有良性辐射效应患者的不必要的颅内ST-BX的有吸引力的解决方案。我们的小组已使用常规MRI扫描开发了一种新的基于图像的复发风险分类器（IRRISC），这表明在n = 58个研究中，将肿瘤复发与TRRE区分开，精度为85％。我们的最初的Inerisc特征包括gadolinium（GD）-T1W MRI的梯度取向的障碍与TRRE相比，肿瘤复发中我们的肿瘤复发明显更高。有趣的是，我们最近有还表明，男性和女性的单独分类器的构建得到了显着改善与“全科”模型相比，GBM生存的预后。在这个R01项目中，我们寻求进一步改善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（1）解剖学（T2W，FLAIR）和功能性MR序列（灌注）的其他特征，（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中的性二态性，以区分肿瘤复发与TRRE。克服的局限性以前与小样本有关的工作和缺乏组织病理学验证，我们的工作将利用最大的工作多机构组织病理学确认的队列n = 470 TRRE和肿瘤的研究复发，协调和验证Irrisc。此外，我们将建立我们的融合的生物学基础通过评估其与TRRE和肿瘤复发的组织病理学标志的关联。最后，在3个临床部位的机器阅读器研究中，将验证Irrisc作为决策支持。成功的标准因为IRSISC是（a）将不符合ST-BX的准确性（〜85-90％），并且（b）确定不超过50％患有癌症的患者。这些标准将确保Irrisc在临床上可以作为一个强大而可靠的分类器，通过避免至少50％的不必要的颅内活检 TRRE，同时还保持了癌症复发的真实正率。