Computer-based assessment of tumor microenvironment (TME) in Follicular Lymphoma

基于计算机的滤泡性淋巴瘤肿瘤微环境 (TME) 评估

• 批准号: 8758963
• 负责人: Metin Nafi Gurcan
• 金额: $ 32.54万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8758963
• 关键词: Address; Adverse effects; Affect; Age; American Cancer Society; Archives; Biometry; Clinical; Clinical Investigator; Color; Communities; Computer software; Computer-Assisted Diagnosis; Computer-Assisted Image Analysis; Computers; Consensus; Data; Development; Diagnosis; Disease; Follicular Lymphoma; Fostering; Funding; General Hospitals; Goals; Health; Hematoxylin and Eosin Staining Method; Hemoglobin; Histopathologic Grade; Image; Image Analysis; Indolent; International Prognostic Index; Lactate Dehydrogenase; Lead; Lymphoid Tissue; Lymphoma; Malignant neoplasm of prostate; Measures; Microscopic; Mission; Modeling; Nodal; Non-Hodgkin' s Lymphoma; Ohio; Oncologist; Outcome; Outcome Measure; Pathologic; Pathologist; Pathology; Patients; Performance; Performance Status; Public Health; Publications; Radiology Specialty; Reporting; Research; Research Design; Research Personnel; Risk; Serum; Site; Slide; Staining method; Stains; Stratification; System; Techniques; Time; Tissues; Translating; United States National Institutes of Health; Universities; Validation; Western World; base; clinically relevant; compare effectiveness; digital imaging; disorder risk; evidence base; experience; high risk; improved; indexing; innovation; malignant breast neoplasm; neoplastic; novel; oncology; outcome forecast; prognostic; public health relevance; tumor; tumor microenvironment

DESCRIPTION (provided by applicant): The overall goals of this proposal are to: 1) Measure the prognostic impact of histologic grade (without and with computer assistance) of follicular lymphoma cases by comparing it with outcome measures; and 2) to develop a computer-assisted image analysis (CaIA) system to quantitatively assess the FL tumor microenvironment (TME); 3) Compare the effectiveness of combined prognostic measure incorporating grade (without and with computer assistance), TME parameters and existing FLIPI score. The proposed research aims to develop a clinically relevant, pathology-based prognostic model in FL utilizing computer image analysis to incorporate grade, tumor microenvironment (TME), and immunohistochemical (IHC) markers. Due to the variable clinical course in FL and increasing treatment options, a prognostic index would allow therapies to be tailored to the patient. Patients with high risk disease may benefit form more intensive therapy, while patients with low risk disease may be appropriate for lower intensity therapy with a more favorable side effect profile. Furthermore, a prognostic index, which includes pathologic features, may ultimately become more relevant in the era of biologically targeted therapies. Our objective is to use advanced image analysis techniques to perform a quantitative and topographical study of the normal and tumor microenvironment and use this study as well as improved and consistent grading options in improving the current prognostic index. Our long-term goal is to translate the improved prognostic index results as better treatment options to FL patients. We plan to pursue the following three specific aims for this project: Specific Aim 1: Measure the prognostic impact of histologic grade (without and with computer assistance) of follicular lymphoma cases by comparing it with outcome measures; Specific Aim 2: Measure the impact of FL tumor microenvironment by comparing TME parameters with outcome measures; Specific Aim 3: Compare the effectiveness of combined prognostic measure incorporating grade (without and with computer assistance), TME parameters and existing FLIPI score. We have formed an experienced team with expertise in FL pathology and oncology, imaging and image analysis, observer studies and biostatistics. Successful completion of this project will exert a sustained and powerful impact on the field by the virtue of its development of a platform for researchers and clinicians to quantitatively and objectively evaluate FL TME, to improve the FL grading, and to incorporate these developments to form a better prognostic index. Microscopic image analysis software, which will be developed for quantification, will be usable for other diseases such as breast cancer, for which TME is also known to be an important predictor of clinical status. The software and data to carry out this project will be made freely available to the research community.

描述（由申请人提供）：该提案的总体目标是： 1) 通过与结果指标进行比较，衡量滤泡性淋巴瘤病例的组织学分级（有和没有计算机辅助）的预后影响； 2）开发计算机辅助图像分析（CaIA）系统来定量评估FL肿瘤微环境（TME）； 3) 比较结合分级（无计算机辅助和有计算机辅助）、TME 参数和现有 FLIPI 评分的综合预后测量的有效性。拟议的研究旨在利用计算机图像分析来整合分级、肿瘤微环境 (TME) 和免疫组织化学 (IHC) 标记，开发临床相关的、基于病理学的 FL 预后模型。由于 FL 临床病程的可变性和治疗选择的增加，预后指数将允许针对患者量身定制治疗方案。患者 患有高风险疾病的患者可能会受益于更强化的治疗，而患有低风险疾病的患者可能适合较低强度的治疗，且副作用更有利。此外，包括病理特征在内的预后指数最终可能在生物靶向治疗时代变得更加重要。我们的目标是使用先进的图像分析技术对正常和肿瘤微环境进行定量和地形研究，并利用这项研究以及改进和一致的分级选项来改善当前的预后指数。我们的长期目标是将改善的预后指数结果转化为 FL 患者更好的治疗选择。我们计划实现该项目的以下三个具体目标： 具体目标 1：通过将滤泡性淋巴瘤病例的组织学分级（无计算机辅助和有计算机辅助）与结果指标进行比较来衡量其预后影响；具体目标 2：通过比较 TME 参数与结果指标来衡量 FL 肿瘤微环境的影响；具体目标 3：比较包含分级（无计算机辅助和有计算机辅助）、TME 参数和现有 FLIPI 评分的综合预后测量的有效性。我们组建了一支经验丰富的团队，在 FL 病理学和肿瘤学、成像和图像分析、观察者研究和生物统计学方面拥有专业知识。该项目的成功完成将为研究人员和临床医生开发一个平台来定量、客观地评估 FL TME，提高 FL 分级，并将这些进展纳入到更好的预后指数。将为量化而开发的显微图像分析软件将可用于其他疾病，例如乳腺癌，TME 也被认为是临床状态的重要预测因子。执行该项目的软件和数据将免费提供给研究界。