(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

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）：该提案将讨论 PQC-4：“可以开发哪些体内成像方法来描绘肿瘤的“细胞类型”，定义为每种不同细胞类型的身份、数量和位置。解决这个问题的理想系统将具有以下特征：1）应从人类患者获得图像和数据；2）成像和细胞类型之间的关系应具有临床相关性；数额很大并且从癌性和非癌性体积内获得的数据的平衡；4) 图像数据应该是高质量的，并且最好是多参数的；5) 组织学与放射线图像的配准必须在前列腺癌患者中是可行的。迈阿密大学 (UM) 拥有大量的 AS 人群，并且定期对前列腺癌患者进行多参数 MRI (MP-MRI) 成像，其中包括扩散 (DWI)、动态对比增强。 (DCE) 和 T2 加权 (T2w) 成像序列作为护理标准 (SOC)。这些图像与经直肠超声 (TRUS) 引导仪器融合以进行活检定位。当前工作的唯一目标是开发预测模型，基于深刻的图像分析（“放射组学”）并结合来自空间共同配准体积的定量组织学和免疫组织化学来定义这种相互关系；从而定义产生 MR 图像数据的“细胞类型”。莫菲特癌症中心的研究人员率先将放射组学和预测（类似分类器）建模应用于癌症。因此，这项工作将继续进行 两个相互关联的目标。在目标 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 引导活检选择以进行主动监测与治疗。在目标 2 中，信息学数据分析、数据库和分类器建模将在莫菲特进行。 MR 图像分析将使用“放射组学”方法，其中从图像识别的栖息地中提取 432 个大小、形状和纹理特征。这些将与通过定量病理学分析的注册组织学图像相匹配，其中从每个细胞提取 32 个特征以形成相似形态类型的簇，以及已知和推定进展标记的 IHC。根据这些定量标记，将开发训练和测试集分类器模型，以将 MR 定义的栖息地与其潜在的细胞类型混合物联系起来。因为这将是一个庞大且宝贵的数据库，所以我们明确打算通过材料转让协议与研究界共享完整的数据集，这将允许替代的数据挖掘模式。