(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”，定义为构成肿瘤及其微环境的每种不同细胞类型的身份，数量和位置？”解决这一问题的理想系统应具有以下特征：1)图像和数据应来自人类患者；2)影像学与细胞分型的关系应具有临床相关性；3)从癌变和非癌变体积内获得的数据量大且平衡；4)图像数据应该是高质量的，最好是多参数的；5)组织与放射影像的配准必须是可行的。这些标准在接受主动监测（AS）的前列腺癌患者中得到满足。迈阿密大学（UM）有大量的AS患者，前列腺癌患者定期和常规接受多参数MRI （MP- MRI）成像，包括弥散（DWI）、动态对比增强（DCE）和T2加权（T2w）成像序列作为标准护理（SOC）。这些图像被融合到经直肠超声（TRUS）引导仪器中进行活检定位。当前工作的唯一目标是开发基于深度图像分析（“放射组学”）的预测模型，结合定量组织学和免疫组织化学，从空间共同注册的体积；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