Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation
用于预测治疗反应、治疗监测和治疗分配的神经胶质瘤多尺度建模
基本信息
- 批准号:10184938
- 负责人:
- 金额:$ 61.2万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-05-01 至 2026-04-30
- 项目状态:未结题
- 来源:
- 关键词:AdultAlgorithmsAreaBasic ScienceBiological AssayBiological MarkersBrain NeoplasmsCancer PatientClinicalClinical TrialsComputer ModelsComputing MethodologiesDNA MethylationDNA Repair EnzymesDataData SetData SourcesDevelopmentDiagnosisDiagnosticDiagnostic ImagingDiagnostic radiologic examinationDrug TargetingEpidermal Growth Factor ReceptorEvaluationEventEyeGene ExpressionGene Expression ProfileGenomeGliomaHigh-Throughput Nucleotide SequencingHumanImageInformaticsLinkMGMT geneMachine LearningMagnetic Resonance ImagingMalignant NeoplasmsMedical centerModalityModelingMolecularMolecular ProfilingMonitorOutcomePathway interactionsPatient imagingPatientsPatternPharmaceutical PreparationsPrediction of Response to TherapyPrognosisPrognostic MarkerPropertyResearchResistanceRoleSomatic MutationTechnologyTherapeuticTimeTranslatingTranslationsTreatment outcomeTumor SubtypeTumor TissueWorkbasecancer siteclinical applicationclinical careclinical practicecohortdata frameworkdata fusiondigital pathologyepigenetic silencingfollow-upgenome sequencingimaging biomarkerimaging modalityimaging studyin vivomethylation patternmolecular imagingmulti-scale modelingmultimodalitymultiple omicsmultiscale datamutational statusneuro-oncologynovelnovel strategiesnovel therapeuticspathology imagingpatient biomarkersprecision medicinepredict clinical outcomepredictive markerprospectiveprospective testquantitative imagingradiological imagingradiologistrecruitresponsesurvival outcomesurvival predictionsynergismtemozolomidetreatment responsetreatment strategytumorwhole slide imaging
项目摘要
Project summary
Computational multi-scale modeling is a growing area of research that aims to link whole slide images and
radiographic iamges with multi-omics molecular profiles of the same patients. Multi-scale modeling has shown
its potential through its ability to predict clinical outcomes e.g. prognosis, and through predicting actionable
molecular properties of tumors, e.g. the activity of EGFR, a major drug target in many cancers. Current
applications are limited to study associations between imaging and molecular data, and predicting long term
outcomes. No actionable information can be gained from multi-scale biomarkers yet.
We propose to develop a multi-scale modeling framework to support treatment response, treatment monitoring
and treatment allocation for patients with brain tumors, focusing on the most aggressive subtype of glioma, IDH
wild-type high grade glioma. In Aim 1, we will develop informatics algorithms that integrate multi-scale data for
treatment response. We will use our expertise in data fusion and develop novel approaches to integrate multi-
scale data to predict first line treatment response. In Aim 2, we will develop algorithms that allow combining
multi-scale data at diagnosis with multi-modal MR imaging data during treatment follow-up. We will focus on
predicting treatment response and progression and whether we can predict these events earlier than
radiologists can. In Aim 3, we will develop algorithms that use the multi-scale data to predict drug target
activities and also suggest novel drugs for patients that become resistant to first line treatment. We will use a
mixture of publicly available glioma multi-scale data sets totaling more than 1000 patients, and also 1600
retrospective and 150 prospective brain tumor patients from Stanford Medical Center.
Combining these complementary data sources in a multi-scale framework for data fusion can have profound
contributions toward predicting treatment outcomes by uncovering unknown synergies and relationships. More
specifically, developing computational models integrating quantitative image features and molecular data to
develop multi-scale signatures, holds the potential to translate in benefit to brain tumor patients by investigating
biomarkers that accurately predict treatment response. Readily, because whole slide images and radiographic
imaging is part of the routine diagnostic work-up of cancer patients and molecular data of brain tumors is
increasingly being used in clinical workflows, therefore if reliable multi-scale signatures can be found reflecting
treatment response, translation to clinical applications is feasible, including optimizing recruitment for clinical
trials.
项目概要
计算多尺度建模是一个不断发展的研究领域,旨在将整个幻灯片图像与
具有同一患者的多组学分子特征的放射线图像。多尺度建模表明
它的潜力在于它能够预测临床结果,例如预测,并通过预测可采取行动
肿瘤的分子特性,例如EGFR 的活性,EGFR 是许多癌症的主要药物靶点。当前的
应用仅限于研究成像和分子数据之间的关联以及预测长期
结果。目前还无法从多尺度生物标志物中获得可操作的信息。
我们建议开发一个多尺度建模框架来支持治疗反应、治疗监测
脑肿瘤患者的治疗分配,重点关注最具侵袭性的胶质瘤亚型 IDH
野生型高级别神经胶质瘤。在目标 1 中,我们将开发集成多尺度数据的信息学算法
治疗反应。我们将利用我们在数据融合方面的专业知识,开发新的方法来集成多
缩放数据来预测一线治疗反应。在目标 2 中,我们将开发允许组合的算法
诊断时的多尺度数据与治疗随访期间的多模态 MR 成像数据。我们将重点关注
预测治疗反应和进展以及我们是否可以更早地预测这些事件
放射科医生可以。在目标 3 中,我们将开发使用多尺度数据来预测药物靶标的算法
活动,并为对一线治疗产生耐药性的患者推荐新药。我们将使用一个
公开的神经胶质瘤多尺度数据集的混合物,总计超过 1000 名患者,还有 1600
来自斯坦福医学中心的 150 名脑肿瘤患者的回顾性和前瞻性研究。
将这些互补的数据源组合在多尺度数据融合框架中可以产生深远的影响
通过发现未知的协同作用和关系来预测治疗结果。更多的
具体来说,开发集成定量图像特征和分子数据的计算模型
开发多尺度特征,通过研究有可能为脑肿瘤患者带来益处
准确预测治疗反应的生物标志物。很容易,因为整个幻灯片图像和射线照相
成像是癌症患者常规诊断检查的一部分,脑肿瘤的分子数据是
越来越多地在临床工作流程中使用,因此,如果可以找到反映可靠的多尺度特征
治疗反应,转化为临床应用是可行的,包括优化临床招募
试验。
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)
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Olivier Gevaert其他文献
Olivier Gevaert的其他文献
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{{ truncateString('Olivier Gevaert', 18)}}的其他基金
Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation
用于预测治疗反应、治疗监测和治疗分配的神经胶质瘤多尺度建模
- 批准号:
10614974 - 财政年份:2021
- 资助金额:
$ 61.2万 - 项目类别:
Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation
用于预测治疗反应、治疗监测和治疗分配的神经胶质瘤多尺度建模
- 批准号:
10397589 - 财政年份:2021
- 资助金额:
$ 61.2万 - 项目类别:
Identification of Cooperative Genetic Alterations in the Pathogenesis of Oral Cancer
口腔癌发病机制中协同遗传改变的鉴定
- 批准号:
8916982 - 财政年份:2015
- 资助金额:
$ 61.2万 - 项目类别:
Radiogenomics framework for non-invasive personalized medicine
非侵入性个性化医疗的放射基因组学框架
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10005534 - 财政年份:2015
- 资助金额:
$ 61.2万 - 项目类别:
Radiogenomics Framework for Non-Invasive Personalized Medicine
非侵入性个性化医疗的放射基因组学框架
- 批准号:
8837360 - 财政年份:2015
- 资助金额:
$ 61.2万 - 项目类别:
Identification of Cooperative Genetic Alterations in the Pathogenesis of Oral Cancer
口腔癌发病机制中协同遗传改变的鉴定
- 批准号:
9084417 - 财政年份:2015
- 资助金额:
$ 61.2万 - 项目类别:
Radiogenomics Framework for Non-Invasive Personalized Medicine
非侵入性个性化医疗的放射基因组学框架
- 批准号:
9012822 - 财政年份:2015
- 资助金额:
$ 61.2万 - 项目类别:
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