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:
    10656165
  • 负责人:
  • 金额:
    $ 76.13万
  • 依托单位:
  • 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 2027-05-31
  • 项目状态:
    未结题

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:在美国,每年有超过14000名胶质母细胞瘤(GBM)患者接受颅脑联合手术

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Manmeet Ahluwalia其他文献

Manmeet Ahluwalia的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Manmeet Ahluwalia', 18)}}的其他基金

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:
    10778776
  • 财政年份:
    2022
  • 资助金额:
    $ 76.13万
  • 项目类别:
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
  • 财政年份:
    2022
  • 资助金额:
    $ 76.13万
  • 项目类别:

相似海外基金

Investigating the Adoption, Actual Usage, and Outcomes of Enterprise Collaboration Systems in Remote Work Settings.
调查远程工作环境中企业协作系统的采用、实际使用和结果。
  • 批准号:
    24K16436
  • 财政年份:
    2024
  • 资助金额:
    $ 76.13万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
WELL-CALF: optimising accuracy for commercial adoption
WELL-CALF:优化商业采用的准确性
  • 批准号:
    10093543
  • 财政年份:
    2024
  • 资助金额:
    $ 76.13万
  • 项目类别:
    Collaborative R&D
Unraveling the Dynamics of International Accounting: Exploring the Impact of IFRS Adoption on Firms' Financial Reporting and Business Strategies
揭示国际会计的动态:探索采用 IFRS 对公司财务报告和业务战略的影响
  • 批准号:
    24K16488
  • 财政年份:
    2024
  • 资助金额:
    $ 76.13万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10107647
  • 财政年份:
    2024
  • 资助金额:
    $ 76.13万
  • 项目类别:
    EU-Funded
Assessing the Coordination of Electric Vehicle Adoption on Urban Energy Transition: A Geospatial Machine Learning Framework
评估电动汽车采用对城市能源转型的协调:地理空间机器学习框架
  • 批准号:
    24K20973
  • 财政年份:
    2024
  • 资助金额:
    $ 76.13万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10106221
  • 财政年份:
    2024
  • 资助金额:
    $ 76.13万
  • 项目类别:
    EU-Funded
De-Adoption Beta-Blockers in patients with stable ischemic heart disease without REduced LV ejection fraction, ongoing Ischemia, or Arrhythmias: a randomized Trial with blinded Endpoints (ABbreviate)
在没有左心室射血分数降低、持续性缺血或心律失常的稳定型缺血性心脏病患者中停用β受体阻滞剂:一项盲法终点随机试验(ABbreviate)
  • 批准号:
    481560
  • 财政年份:
    2023
  • 资助金额:
    $ 76.13万
  • 项目类别:
    Operating Grants
Our focus for this project is accelerating the development and adoption of resource efficient solutions like fashion rental through technological advancement, addressing longer in use and reuse
我们该项目的重点是通过技术进步加快时装租赁等资源高效解决方案的开发和采用,解决更长的使用和重复使用问题
  • 批准号:
    10075502
  • 财政年份:
    2023
  • 资助金额:
    $ 76.13万
  • 项目类别:
    Grant for R&D
Engage2innovate – Enhancing security solution design, adoption and impact through effective engagement and social innovation (E2i)
Engage2innovate — 通过有效参与和社会创新增强安全解决方案的设计、采用和影响 (E2i)
  • 批准号:
    10089082
  • 财政年份:
    2023
  • 资助金额:
    $ 76.13万
  • 项目类别:
    EU-Funded
Collaborative Research: SCIPE: CyberInfrastructure Professionals InnoVating and brOadening the adoption of advanced Technologies (CI PIVOT)
合作研究:SCIPE:网络基础设施专业人员创新和扩大先进技术的采用 (CI PIVOT)
  • 批准号:
    2321091
  • 财政年份:
    2023
  • 资助金额:
    $ 76.13万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了