Cancer imaging phenomics software suite: application to brain and breast cancer
Cancer imaging phenomics software suite: application to brain and breast cancer
批准号:
8967740
负责人:
Christos Davatzikos
金额:
$65.35万
依托单位国家:
美国
项目类别:
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-09-01 至 2020-08-31
关键词:
Algorithmic SoftwareAmerican College of Radiology Imaging NetworkAtlasesBiologicalBiological AssayBrainBrain NeoplasmsBreastCharacteristicsClinicClinicalClinical ResearchCollaborationsCommunitiesComputer AnalysisComputer softwareComputing MethodologiesDataDatabasesDecision Support SystemsDevelopmentDiagnosisDiagnosticDictionaryDiseaseDisease ProgressionEducational ActivitiesEvaluationExplosionFeedbackFiberFoundationsFunctional ImagingGenomicsGliomaGoalsHeterogeneityImageImage AnalysisImaging technologyIndividualInfiltrationInformaticsInformation TechnologyInstitutionKnowledgeLettersMachine LearningMalignant NeoplasmsMalignant neoplasm of brainMammary NeoplasmsMeasurementMeasuresMethodologyMethodsModelingMolecularOperative Surgical ProceduresOrganOutcomePatientsPhenotypePhysiologicalPopulations at RiskPropertyProtocols documentationResearchResearch PersonnelScanningShapesSignal TransductionSliceStatistical MethodsTestingTranscendWorkanalytical methodbasecancer diagnosiscancer imagingfollow-upimage registrationimaging modalityimaging probeindividualized medicinemalignant breast neoplasmmolecular imagingopen sourcephenomicspopulation basedprecision medicinepredictive modelingprognosticprogramsprototypepublic health relevancequantitative imagingresponsetooltreatment responsetumorweb site
中文摘要
描述(申请人提供):评估癌症结构、生理和分子特性的成像技术的爆炸性发展产生了大量和多样化的数据,从不同的分子成像探针到器官水平的结构和生理成像。肿瘤学影像从“工业时代”到现在的转变
“信息时代”要求开发分析方法,1)从这些数据中提取临床和生物相关的信息;2)通过严格的统计和计算方法整合成像、临床和基因组数据,以便推导出对了解癌症机制以及诊断、预后评估、反应评估和个性化(或“精确”)治疗管理有价值的模型;3)生物医学界可以方便地使用和应用,目的是了解、诊断和治疗癌症。构建和传播用于计算癌症表型组学的先进信息技术,主要由各种成像技术捕获,是拟议工作的重点。特别是,我们建议开发并广泛分发癌症表观组学工具包(CapTk),这是一个软件套件,集成了我们在宾夕法尼亚大学的团队开发的先进的肿瘤学图像计算和分析工具,并提供远远超过当前使用的方法的复杂的肿瘤学图像定量分析。重要的是,这些信息学工具是在积极的临床研究和合作的背景下开发的,因此受到实际临床需求的启发和测试。虽然我们的主要工作重点是CapTk,但我们认为CapTk是一种高级计算套件,可以整合到各种商业(如BrainLab、霍洛奇、GE、See Letters)和非商业(开源Slicer是我们的研究平台)工作站中,并进一步支持这些工作站,但我们也将使用该软件套件开发两个专注于研究的原型工作站,利用我们在宾夕法尼亚大学的工作和临床研究的独特优势:高级神经肿瘤计算成像工作站(ANCI)和高级乳房计算成像工作站(ABCI)。特别是,我们将追求以下具体目标:目标1)完善、广泛记录我们的图像分析算法和软件,并将其集成到CapTk中,CapTk将包括与成像测量和决策支持相关的三个主要组件:图像配准套件;成像特征提取套件;成像分析和预测建模套件。目的2)开发和测试ANCI和ABCI两个重点研究原型工作站,旨在为脑和乳腺癌的治疗提供诊断和治疗决策支持机制。目的3)传播软件和知识,通过a)将我们的软件部署到宾夕法尼亚大学的诊所以及选定的合作机构,以便根据反馈测试和进一步完善软件;b)向研究社区免费分发我们的软件;c)为临床医生和信息工作者组织各种教育活动,并建立和维护支持论坛。
英文摘要
DESCRIPTION (provided by applicant): The explosion of imaging technologies that evaluate structural, physiologic and molecular properties of cancer, has generated large and diverse amounts of data, ranging from diverse molecular imaging probes to structural and physiologic imaging at the organ level. This transition of oncologic imaging from its "industrial era" to it is
"information era" has necessitated the development of analytical methods that 1) extract from this data information that is clinically and biologically relevant; 2) integrate imaging, clinical nd genomic data via rigorous statistical and computational methodologies in order to derive models valuable for understanding cancer mechanisms, but also for diagnosis, prognostic assessment, response evaluation, and personalized (or "precision") treatment management; 3) are available to the biomedical community for easy use and application, with the aim of understanding, diagnosing, and treating cancer. Building and disseminating advanced information technology for computational cancer phenomics, largely captured by diverse imaging technologies, is the emphasis of the proposed work. In particular, we propose to develop and widely distribute the cancer phenomics toolkit (CapTk), a software suite integrating advanced oncologic image computing and analytics tools that have been developed by our groups here at Penn and offer sophisticated quantitative analytics of oncologic images well beyond currently used methods. Importantly, these informatics tools have been developed in the context of active clinical studies and collaborations and have therefore being inspired and tested by real clinical needs. Although the main focus of our work is CapTk, which we view as an advanced computational suite that can be incorporated into, and further enable, various commercial (e.g. BrainLab, Hologic's, GE's, see letters) and non-commercial (the open source Slicer is our research platform) workstations, we will also use this software suite to develop two focused research prototype workstations leveraging upon the unique strengths of our work and clinical studies at Penn: an Advanced Neuro-Oncologic Computational Imaging Workstation (ANCI) and the Advanced Breast Computational Imaging Workstation (ABCI). In particular, we will pursue the following specific aims: Aim 1) To refine, extensively document and integrate our image analysis algorithms and software into CapTk, which will have 3 major components pertaining to imaging measurements and decision support: Image Registration Suite; Imaging Feature Extraction Suite; Suite for Imaging Analytics and Predictive Modeling. Aim 2) To develop and test two focused research prototype workstations, ANCI and ABCI, aiming to provide diagnostic and treatment decision support mechanisms for brain and breast cancer management. Aim 3) Disseminate software and knowledge, via a) deploying our software to the clinic at Penn as well as to selected collaborating institutions, in order to test and further refine the software accordig to feedback; b) freely distributing our software to the research community; c) organizing various educational activities for both clinicians and informaticians, and setting up and maintaining support forums.
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