Generalizable quantitative imaging and machine learning signatures in glioblastoma, for precision diagnostics and personalized treatment: the ReSPOND consortium

胶质母细胞瘤的通用定量成像和机器学习特征,用于精确诊断和个性化治疗:ReSPOND 联盟

基本信息

  • 批准号:
    10625442
  • 负责人:
  • 金额:
    $ 64.11万
  • 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:
    2022-06-01 至 2027-05-31
  • 项目状态:
    未结题

项目摘要

Abstract The current state of magnetic resonance imaging (MRI) methods in neurooncology offers great potential for providing rich characterizations of structural, physiological, and metabolic character- istics of brain tumors, especially gliomas, which are complex and highly heterogeneous cancers. Glioblastoma (GBM), in particular, has a grim prognosis, with median overall survival (OS) less than 15 months with relatively little improvement in the past 15 years since the Stupp protocol was introduced. Many experimental treatments are being pursued; however, OS has largely remained stagnant. Some of the obstacles in improving this outcome have been 1) disease heterogeneity, which both renders it difficult to detect treatment effects in Phase 1 or even Phase 2 trials, and calls for personalized, rather than one-size fits-all, treatment strategies; 2) methods used for tumor characterization based on size, enhancement, perfusion and diffusion properties are relatively crude and don't fully leverage the richness of imaging data or their spatial heterogeneity. Quanti- tative imaging and machine learning (QIML) methods developed in the past decade have shown great potential for dissecting the spatial, temporal and inter-patient heterogeneity of GBM; for discoveringrelationships between imaging and molecular characteristics ; foroffering personalized predictions of clinical outcome; and for leveraging subtle multi-parametric relationships in the data to detect peri-tumoral infiltration or distinguish treatment related changes, i.e., pseudo-progression (PsP), from true tumor recurrence. Our group has been at the forefront of QIML, with emphasis on a) obtaining rich imaging phenotypes relying on multi-parametric signals, texture parameters, shape properties, spatial patterns derived from atlas registration, and biophysical models of tumor growth, and b) integrating such imaging signatures using machine learning into predictors of clinical outcome, early recurrence from peri-tumoral infiltration, PsP, and radiologic subtypes of GBM. Despite their promise, QIML methods have a notorious limitation: they might overfit specific datasets from which they are derived, and might display poor reproducibility under real-life conditions of variable scanner types and imaging protocols. In this proposal we aim to leverage the recently formed ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium, to integrate, harmonize, and analyze 4,578 datasets from 14 centers around the world, and hence more appropriately train and cross-validate QIML tools for a wider generalizability. This consortium will generate an unprecedented database of diverse and carefully harmonized sets of MRI and clinical measures, and aims to provide the community with robust and reproducible QIML models contributing to precision diagnostics and personalize treatment for this dreaded brain cancer.
摘要 神经肿瘤学中的磁共振成像(MRI)方法的当前状态提供了很大的优势。 提供丰富的结构、生理和代谢特征表征的潜力- 脑肿瘤,特别是神经胶质瘤,是复杂和高度异质性的癌症。 特别是胶质母细胞瘤(GBM),预后很差,中位总生存期(OS)小于 15个月,自Stupp方案实施以来的过去15年中, 介绍许多实验性治疗正在进行中;然而,OS在很大程度上仍然存在。 停滞不前改善这一结果的一些障碍是:1)疾病异质性, 这使得在1期甚至2期试验中难以检测治疗效果, 用于个性化,而不是一刀切的治疗策略; 2)用于肿瘤的方法 基于大小、增强、灌注和扩散特性的表征相对 粗糙并且没有充分利用成像数据的丰富性或其空间异质性。定量 在过去十年中开发的动态成像和机器学习(QIML)方法已经表明, 解剖GBM的空间、时间和患者间异质性的巨大潜力; 影像学和分子特征之间的关系;为提供个性化 临床结果的预测;以及利用数据中微妙的多参数关系 为了检测肿瘤周围浸润或区分治疗相关的变化,即,假性进展 (PsP)肿瘤复发。我们的团队一直处于QIML的最前沿,重点是 a)获得依赖于多参数信号、纹理参数、形状 性质、从图谱配准导出的空间模式和肿瘤生长的生物物理模型, 以及B)使用机器学习将这样的成像特征整合到临床诊断的预测器中, 结果、肿瘤周围浸润的早期复发、PsP和GBM的放射学亚型。 尽管QIML方法很有前途,但它们有一个众所周知的局限性:它们可能过度拟合特定的 数据集,它们是从其中派生的,并可能显示在现实生活中的再现性差, 可变扫描仪类型和成像协议的条件。在本提案中,我们旨在利用 最近成立了ReSPOND(用于精确诊断的放射组学特征)联盟, 整合、协调和分析来自全球14个中心的4,578个数据集, 更适当地培训和交叉验证QIML工具,以获得更广泛的通用性。该联盟 将产生一个前所未有的数据库,其中包含各种精心协调的MRI集, 临床措施,旨在为社区提供稳健和可重复的QIML模型 有助于精确诊断和个性化治疗这种可怕的脑癌。

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Sequential and Hybrid PET/MRI Acquisition in Follow-Up Examination of Glioblastoma Show Similar Diagnostic Performance.
胶质母细胞瘤随访检查中的连续和混合 PET/MRI 采集显示出相似的诊断性能。
  • DOI:
    10.3390/cancers15010083
  • 发表时间:
    2022-12-23
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Ziegenfeuter, Julian;Delbridge, Claire;Bernhardt, Denise;Gempt, Jens;Schmidt-Graf, Friederike;Griessmair, Michael;Thomas, Marie;Meyer, Hanno S.;Zimmer, Claus;Meyer, Bernhard;Combs, Stephanie E.;Yakushev, Igor;Wiestler, Benedikt;Metz, Marie-Christin
  • 通讯作者:
    Metz, Marie-Christin
Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI.
  • DOI:
    10.3390/cancers15061894
  • 发表时间:
    2023-03-22
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Cepeda, Santiago;Luppino, Luigi Tommaso;Perez-Nunez, Angel;Solheim, Ole;Garcia-Garcia, Sergio;Velasco-Casares, Maria;Karlberg, Anna;Eikenes, Live;Sarabia, Rosario;Arrese, Ignacio;Zamora, Tomas;Gonzalez, Pedro;Jimenez-Roldan, Luis;Kuttner, Samuel
  • 通讯作者:
    Kuttner, Samuel
The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research.
Imaging the WHO 2021 Brain Tumor Classification: Fully Automated Analysis of Imaging Features of Newly Diagnosed Gliomas.
  • DOI:
    10.3390/cancers15082355
  • 发表时间:
    2023-04-18
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
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Christos Davatzikos其他文献

Christos Davatzikos的其他文献

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{{ truncateString('Christos Davatzikos', 18)}}的其他基金

Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods
使用机器学习方法解开重度抑郁症的解剖学、功能和临床异质性
  • 批准号:
    10714834
  • 财政年份:
    2023
  • 资助金额:
    $ 64.11万
  • 项目类别:
The Neuroimaging Brain Chart Software Suite
神经影像脑图软件套件
  • 批准号:
    10581015
  • 财政年份:
    2023
  • 资助金额:
    $ 64.11万
  • 项目类别:
Generalizable quantitative imaging and machine learning signatures in glioblastoma, for precision diagnostics and personalized treatment: the ReSPOND consortium
胶质母细胞瘤的通用定量成像和机器学习特征,用于精确诊断和个性化治疗:ReSPOND 联盟
  • 批准号:
    10421222
  • 财政年份:
    2022
  • 资助金额:
    $ 64.11万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10696100
  • 财政年份:
    2020
  • 资助金额:
    $ 64.11万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10263220
  • 财政年份:
    2020
  • 资助金额:
    $ 64.11万
  • 项目类别:
Benchmarking and Comparing AD-Related AI Methods Across Sites on a Standardized Dataset
在标准化数据集上跨站点对 AD 相关 AI 方法进行基准测试和比较
  • 批准号:
    10825403
  • 财政年份:
    2020
  • 资助金额:
    $ 64.11万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10475286
  • 财政年份:
    2020
  • 资助金额:
    $ 64.11万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10028746
  • 财政年份:
    2020
  • 资助金额:
    $ 64.11万
  • 项目类别:
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium
机器学习和大规模成像分析,用于衰老和临床前阿尔茨海默氏病大脑轨迹的维度表示:大脑衰老图表和 iSTAGING 联盟
  • 批准号:
    10839623
  • 财政年份:
    2017
  • 资助金额:
    $ 64.11万
  • 项目类别:
Biomedical Image Computing and Informatics Cluster
生物医学图像计算与信息学集群
  • 批准号:
    9273767
  • 财政年份:
    2017
  • 资助金额:
    $ 64.11万
  • 项目类别:

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A Phase 2 biomarker driven, Study of DB107, a Retroviral Replicating Vector, Combined With 5-FC in Patients with Recurrent Glioblastoma or Anaplastic Astrocytoma
由生物标志物驱动的 DB107(一种逆转录病毒复制载体)与 5-FC 联合治疗复发性胶质母细胞瘤或间变性星形细胞瘤患者的 2 期研究
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针对星形细胞瘤、IDH 突变、4 级的代谢脆弱性
  • 批准号:
    10306229
  • 财政年份:
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Targeting metabolic vulnerabilities in Astrocytoma, IDH-mutant, Grade 4
针对星形细胞瘤、IDH 突变、4 级的代谢脆弱性
  • 批准号:
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关注星形细胞瘤恶性转化的代谢组分析
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探索儿童高级星形细胞瘤 (HGA) 和骨巨细胞瘤 (GCTB) 中 H3F3A 突变的肿瘤发生。
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    Fellowship Programs
A Phase 1 Study of M032, a Genetically Engineered HSV-1 Expressing IL-12, in Patients with Recurrent/Progressive Glioblastoma Multiforme, Anaplastic Astrocytoma, or Gliosarcoma.
M032(一种表达 IL-12 的基因工程 HSV-1)在复发/进行性多形性胶质母细胞瘤、间变性星形细胞瘤或胶质肉瘤患者中的 1 期研究。
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定义组蛋白 3 (H3.3G34R) 突变在儿童高级星形细胞瘤发病机制中的作用
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毛细胞型星形细胞瘤微血管增殖的起源
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  • 项目类别:
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