(PQC4) Habitats in Prostate Cancer

(PQC4) 前列腺癌的栖息地

• 批准号: 8930109
• 负责人: Robert J. Gillies
• 金额: $ 69.2万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-19 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8930109
• 关键词: Address; Area Under Curve; Automobile Driving; Benchmarking; Biopsy; Biopsy Specimen; Blinded; Cancer Center; Cancer Patient; Cancerous; Cell Extracts; Cells; Cellular Structures; Characteristics; Clinical Data; Clinical Trials; Communities; Complex; Conduct Clinical Trials; Data; Data Analyses; Data Set; Databases; Decision Support Systems; Deposition; Diagnosis; Diagnostic; Diffusion; Drosophila chb protein; Equilibrium; Functional disorder; Gene Expression; Goals; Habitats; Health; Hematoxylin and Eosin Staining Method; Histology; Histopathology; Human; Hypoxia; Image; Image Analysis; Immunohistochemistry; Individual; Informatics; Intention; Lesion; Life; Location; MCT-1 gene; MDM2 gene; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Medical; Metabolism; Methods; Mining; Modeling; Molecular; Molecular Structure; Monitor; Pathology; Pathology Report; Patients; Pattern; Population Surveillance; Positron-Emission Tomography; Predictive Value; Process; Prognostic Marker; Proteins; Radiology Specialty; Receiver Operating Characteristics; Research; Research Personnel; Resolution; Sample Size; Sampling; Scanning; Schedule; Shapes; Slide; Source; Staining method; Stains; System; T2 weighted imaging; Testing; Texture; Tissue Microarray; Training; Transrectal Ultrasound; Universities; Validation; Weight; Work; arm; base; cell type; clinically relevant; cohort; data acquisition; data mining; data reduction; digital; imaging modality; in vivo imaging; instrument; interest; material transfer agreement; meetings; men; model building; molecular pathology; patient population; predictive modeling; prognostic; progression marker; prospective; quantitative imaging; relational database; standard of care; statistics; tumor; Address; Area Under Curve; Automobile Driving; Benchmarking; Biopsy; Biopsy Specimen; Blinded; Cancer Center; Cancer Patient; Cancerous; Cell Extracts; Cells; Cellular Structures; Characteristics; Clinical Data; Clinical Trials; Communities; Complex; Conduct Clinical Trials; Data; Data Analyses; Data Set; Databases; Decision Support Systems; Deposition; Diagnosis; Diagnostic; Diffusion; Drosophila chb protein; Equilibrium; Functional disorder; Gene Expression; Goals; Habitats; Health; Hematoxylin and Eosin Staining Method; Histology; Histopathology; Human; Hypoxia; Image; Image Analysis; Immunohistochemistry; Individual; Informatics; Intention; Lesion; Life; Location; MCT-1 gene; MDM2 gene; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Medical; Metabolism; Methods; Mining; Modeling; Molecular; Molecular Structure; Monitor; Pathology; Pathology Report; Patients; Pattern; Population Surveillance; Positron-Emission Tomography; Predictive Value; Process; Prognostic Marker; Proteins; Radiology Specialty; Receiver Operating Characteristics; Research; Research Personnel; Resolution; Sample Size; Sampling; Scanning; Schedule; Shapes; Slide; Source; Staining method; Stains; System; T2 weighted imaging; Testing; Texture; Tissue Microarray; Training; Transrectal Ultrasound; Universities; Validation; Weight; Work; arm; base; cell type; clinically relevant; cohort; data acquisition; data mining; data reduction; digital; imaging modality; in vivo imaging; instrument; interest; material transfer agreement; meetings; men; model building; molecular pathology; patient population; predictive modeling; prognostic; progression marker; prospective; quantitative imaging; relational database; standard of care; statistics; tumor

DESCRIPTION (provided by applicant): This proposal will address PQC-4: "What in vivo imaging methods can be developed to portray the "cytotype" of a tumor defined as the identity, quantity, and location of each of the different cell types that make up a tumor and its microenvironment? An ideal system to address this question will have the following characteristics: 1) images and data should be obtained from human patients; 2) the relationship between imaging and cytotypes should have clinical relevance; 3) there should be a large amount and a balance in data obtained from within cancerous and non-cancerous volumes; 4) the image data should be of high quality and ideally multiparametric; and 5) registration of histology to radiographic images must be feasible. Such criteria are met in prostate cancer patients who are being monitored by active surveillance (AS). The University of Miami (UM) has a large AS population, and patients with prostate cancer are regularly and routinely imaged with multiparametric MRI (MP- MRI) that includes diffusion (DWI), dynamic contrast enhancement (DCE) and T2 weighted (T2w) imaging sequences as standard of care (SOC). These images are fused to a transrectal ultrasound (TRUS) guidance instrument for biopsy localization. The singular goal of the current work is to develop predictive models that define this interrelationshi based on profound image analyses ("radiomics") in combination with quantitative histology and immunohistochemistry from spatially co-registered volumes; thus defining the "cytotypes" giving rise to MR image data. Researchers at the Moffitt Cancer Center have pioneered the application of radiomics and predictive (classifier like) modeling to cancer. Thus, this work will proceed with two interrelated aims. In Aim 1, MR images, histology, gene expression and clinical data will be generated at UM via the MAST Trial: MRI- Guided Biopsy Selection for Active Surveillance versus Treatment. In Aim 2, informatics data analysis, databasing and classifier modeling will be undertaken at Moffitt. Analysis of MR images will use a "radiomics" approach, wherein 432 size, shape and texture features are extracted from image-identified habitats. These will be matched up to registered histology images analyzed with quantitative pathology wherein 32 features are extracted from each cell to form clusters of similar morphotypes, as well as IHC for known and putative progression markers. From these quantitative markers, training and test set classifier models will be developed to relate the MR-defined habitats to their underlying mixtures of cytotypes. Because this will be a large and invaluable data base, it is our explicit intention to share the complete dataset, with the research community through material transfer agreements, which will allow alternative data mining schema.

描述（由申请人提供）：该提案将解决PQC-4：“可以开发哪些体内成像方法来描绘肿瘤的“细胞型”，该肿瘤的“细胞型”定义为每种肿瘤类型的身份，数量和位置，这些肿瘤类型构成肿瘤及其微环境的构成肿瘤及其微环境的构成以下问题的理想系统？细胞型应具有临床相关性；作为人群和前列腺癌患者，定期且常规地用多参数MRI（MP-MRI）成像，包括扩散（DWI），动态对比度增强（DCE）和T2加权（T2W）成像序列作为护理标准（SOC）。这些图像融合了直肠超声（TRUS）进行活检定位的指导工具。当前工作的单一目标是开发预测模型，这些模型基于深刻的图像分析（“放射组学”）以及定量组织学和免疫组织化学的结合来定义这种相互关系。因此定义“细胞型”会产生MR图像数据。莫菲特癌症中心的研究人员开创了放射组学和预测性（类似分类器）建模的研究人员。因此，这项工作将继续 两个相互关联的目标。在AIM 1中，将通过MAST试验在UM生成MR图像，组织学，基因表达和临床数据：MRI-WEDISED活检选择以进行主动监测与治疗。在AIM 2中，将在Moffitt进行信息学数据分析，数据库和分类器建模。 MR图像的分析将使用“放射线学”方法，其中432个尺寸，形状和纹理特征是从图像识别的栖息地中提取的。这些将与使用定量病理学分析的注册组织学图像相匹配，其中从每个细胞中提取32个特征，以形成相似形态型的簇，以及用于已知和推定的进展标记的IHC。从这些定量标记中，将开发训练和测试集分类器模型，以将MR定义的栖息地与它们的细胞型混合物联系起来。因为这将是一个庞大且无价的数据库，所以我们明确的意图是通过物料转移协议与研究社区共享完整的数据集，这将允许替代数据挖掘模式。