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。

• 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
• 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

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 手术、化疗和放射治疗是他们侵袭性癌症的标准治疗方法。不幸的是，约40%的 这些患者将在化疗后的随访MRI扫描(T1w，T2w， 才华)。基底膜肿瘤治疗中的一个重大挑战是如何将这些病变区分为肿瘤。 复发或良性治疗相关的辐射效应(TRRE)。在临床上，这些情况彼此相似。 从放射学角度来看。不幸的是，在缺乏可靠的诊断工具的情况下，TRRE患者将接受 不必要和可避免的侵入性立体定向脑活检(ST-Bx)，以确认疾病的存在。 然而，即使是侵入性ST-Bx也具有85%-90%的准确率，这是由于与获取 活检组织可能不能代表潜在的疾病病理。因此，建设 非侵入性决策支持工具，其诊断准确率不亚于ST-Bx，代表着 在良性辐射效应患者中避免不必要的颅内ST-Bx的有吸引力的解决方案。 我们的团队开发了一种新的基于图像的复发风险分类器(IRRisC)，使用常规的MRI扫描， 在n=58项研究中，这表明在区分肿瘤复发和TRRE方面的准确率为85%。我们的 最初的一组IRRisC特征包括Gd-T1w磁共振成像上梯度方向的紊乱， 与TRRE相比，肿瘤复发率明显更高。有趣的是，我们最近 也证明了男性和女性的单独分类器的构建产生了显著的改进 GBM存活率的预后与“全能型”模型的比较。在这个R01项目中，我们寻求进一步的改进 并通过将我们的初始功能集(使用Gd-T1w MRI)扩展到包括(1)来验证IRRisC的准确性 来自解剖(T2w，FLAIR)和功能性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 探讨基底膜的性二型性，以鉴别肿瘤复发与TRRE。克服以下限制 以前关于小样本和缺乏组织病理学验证的工作，我们的工作将利用最大的 多机构组织病理学证实的队列研究，截至n=470关于TRRE和肿瘤的研究 重复，以协调和验证IRRisC。此外，我们将建立IRRisC的生物学基础 通过评估其与TRRE的组织病理学特征和肿瘤复发的关系来评估其特征。最后， IRRisC将在3个临床站点的机器阅读器研究中被验证为决策支持。成功的标准 对于IRRisC，它将(A)不逊于ST-Bx的准确度(~85-90%)，以及(B)识别不超过50% 患有TRRE的患者被认为患有癌症。这些标准将确保IRRisC作为一种 强大而可靠的分类器，通过消除至少50%的不必要的脑内活组织检查 TRRE，同时也保持了癌症复发的高真阳性率。