Shared Resource Core 2: Clinical Artificial Intelligence Core
共享资源核心2:临床人工智能核心
基本信息
- 批准号:10712296
- 负责人:
- 金额:$ 14.19万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-19 至 2028-08-31
- 项目状态:未结题
- 来源:
- 关键词:Academic TrainingAccelerationAddressAdoptionAlgorithmic AnalysisApacheArtificial IntelligenceArtificial Intelligence platformBiological AssayCancer BiologyCancer Research ProjectCharacteristicsClinicClinicalClinical DataCommunitiesCommunity Clinical Oncology ProgramComplexComputer softwareComputerized Medical RecordDataData AnalysesData CommonsData ScienceData Science CoreData SetEnsureGoalsHeterogeneityImageIndustryInformaticsInfrastructureIntelligenceInvestigationInvestmentsKnowledgeLate EffectsMalignant Childhood NeoplasmMalignant NeoplasmsMeasuresMedicalMedical ImagingMedicineMethodsModelingMolecularMultiomic DataNatural Language ProcessingNeuroblastomaPatientsPatternPhenotypePrediction of Radiation ResponsePreparationRadiationRadiation OncologyRadiation therapyReproducibilityResearchResearch Project GrantsResearch SupportResource SharingResourcesScientific Advances and AccomplishmentsSemanticsStandardizationTechnologyTextThe Cancer Imaging ArchiveToxic effectTraininganticancer researchartificial intelligence algorithmartificial intelligence methodcancer therapyclinical phenotypeclinical translationdata streamselectronic dataexperienceimaging biomarkerimaging probeimprovedmultiple omicsopen sourceprogramsquantitative imagingradiation during childhoodradiation resistanceradiation responseradiological imagingradiomicsresponsesynergismtooltreatment responsetumortumor heterogeneityusability
项目摘要
PROJECT SUMMARY
Artificial intelligence (AI) algorithms have the potential to fundamentally change medicine through their ability to
recognize complex patterns in medical data. The Clinical Artificial Intelligence and Imaging Core (AI Core)
is an essential shared resource that will support the Aims of the Harvard/UCSF ROBIN Research Projects to
enable large-scale analysis of granular clinical data, allowing non-invasive characterization of tumoral and
patient heterogeneity and a path towards clinical translation. This will be achieved through the following
Specific Aims: i) retrieve, curate, and annotate digitized clinical data to support quantitative analyses and
AI/informatics pipelines for the ROBIN Molecular Characterization Trial and Research Projects, which will
produce one of the most comprehensive datasets for DMG and neuroblastoma patients in existence for AI-
based data analysis, ii) develop and evaluate task-specific AI pipelines using our well-established data
preprocessing, AI-derived imaging biomarkers, and natural language processing (NLP) platforms for tumor
heterogeneity, radiation resistance/response, and toxicity characterization in accordance with the Research
Projects and Data Science Core, and iii) standardize and release AI/informatics methods across data types
and applications in ways that ensure transparency, reproducibility, and access to advance scientific knowledge
within the wider research field, as well as accelerate clinical translation to the pediatric radiation oncology
clinic. Achieving these aims will be possible through synergy with the molecular mechanistic analyses in the
Data Science Core, as well as with the ROBIN-NEST Cross-Training Core and Administrative Core to
disseminate our methods and provide training to the greater ROBIN Network and the scientific community.
This Core is led by pioneers in the field of AI analysis of medical imaging (PI: Aerts) and clinical text (PI:
Savova), with significant experience building open access platforms for medical AI applications. For imaging
analysis, we developed and maintain PyRadiomics, one of the world’s most widely used and highly cited
radiomics pipelines, developed with support of NCI’s investments in infrastructure and data, including the
Informatics Technology for Cancer Research (ITCR), Imaging Data Commons (IDC), and Quantitative Imaging
Network (QIN) programs. For clinical text, we have developed Apache cTakes(™), a leading open access
natural language processing platform for extracting medical, grammatical, and semantic information from
clinical texts, and DeepPhe, an open-source software for cancer clinical phenotyping, also supported by the
NCI’s ITCR program (PI: Savova). We will use and build on our open access methods and state-of-the art AI-
based phenotyping methods developed in these NCI projects to support the Harvard/UCSF ROBIN
investigators to incorporate fundamental clinical -omics data into their investigation of intratumoral
heterogeneity and predictors of radiation response and late effects.
项目总结
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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Hugo Aerts其他文献
Hugo Aerts的其他文献
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{{ item.author }}
{{ truncateString('Hugo Aerts', 18)}}的其他基金
Quantitative Radiomics System Decoding the Tumor Phenotype
定量放射组学系统解码肿瘤表型
- 批准号:
8875289 - 财政年份:2015
- 资助金额:
$ 14.19万 - 项目类别:
Genotype and Imaging Phenotype Biomarkers in Lung Cancer
肺癌的基因型和影像表型生物标志物
- 批准号:
8799943 - 财政年份:2015
- 资助金额:
$ 14.19万 - 项目类别:
Quantitative Radiomics System Decoding the Tumor Phenotype
定量放射组学系统解码肿瘤表型
- 批准号:
9247166 - 财政年份:2015
- 资助金额:
$ 14.19万 - 项目类别:
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