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: 9611415
• 负责人: Metin Nafi Gurcan
• 金额: $ 21.38万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 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 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9611415
• 关键词: Computer; based; assessment; 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)：这项建议的总体目标是：1)通过将组织学分级(无计算机辅助和有计算机辅助)与结果测量进行比较来衡量滤泡性淋巴瘤病例的组织学分级对预后的影响；2)开发计算机辅助图像分析(CAIA)系统以定量评估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参数和现有的FLIPI评分的联合预后测量的有效性。这项拟议的研究旨在利用计算机图像分析，结合分级、肿瘤微环境(TME)和免疫组织化学(IHC)标记物，开发一个临床相关的、基于病理的FL预后模型。由于FL的临床病程变化和治疗选择的增加，预后指数将允许为患者量身定做治疗方案。病人 高风险疾病患者可能受益于更密集的治疗，而低风险疾病患者可能适合副作用更有利的低强度治疗。此外，在生物靶向治疗的时代，包括病理特征的预后指数最终可能变得更加相关。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图研究，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。我们计划为这个项目追求以下三个具体目标：具体目标1：通过比较滤泡性淋巴瘤的组织学分级(无计算机辅助和有计算机辅助)与结果指标来衡量其对预后的影响；具体目标2：通过比较TME参数和结果指标来衡量FL肿瘤微环境的影响；具体目标3：比较结合分级(无计算机辅助和有计算机辅助)、TME参数和现有的FLIPI评分的联合预后指标的有效性。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具有专业知识。该项目的成功完成将对该领域产生持续而强大的影响，因为它为研究人员和临床医生提供了一个平台，可以定量和客观地评估FL TME，提高FL分级，并结合这些进展形成更好的预后指标。将开发用于量化的显微图像分析软件将适用于其他疾病，如乳腺癌，对这些疾病来说，TME也被认为是临床状态的重要预测指标。开展这一项目的软件和数据将免费提供给研究界。

项目成果

Computerized classification of intraductal breast lesions using histopathological images.

• DOI: 10.1109/tbme.2011.2110648
• 发表时间: 2011-07
• 期刊: IEEE transactions on bio-medical engineering
• 作者: Dundar MM;Badve S;Bilgin G;Raykar V;Jain R;Sertel O;Gurcan MN
• 通讯作者: Gurcan MN

Content-based microscopic image retrieval system for multi-image queries.

• DOI: 10.1109/titb.2012.2185829
• 发表时间: 2012-07
• 期刊: IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society
• 作者: Akakin HC;Gurcan MN
• 通讯作者: Gurcan MN

Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.

• DOI: 10.1371/journal.pone.0195621
• 发表时间: 2018
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Niazi MKK;Tavolara TE;Arole V;Hartman DJ;Pantanowitz L;Gurcan MN
• 通讯作者: Gurcan MN

Biomedical imaging ontologies: A survey and proposal for future work.

• DOI: 10.4103/2153-3539.159214
• 发表时间: 2015
• 期刊: Journal of pathology informatics
• 作者: Smith B;Arabandi S;Brochhausen M;Calhoun M;Ciccarese P;Doyle S;Gibaud B;Goldberg I;Kahn CE Jr;Overton J;Tomaszewski J;Gurcan M
• 通讯作者: Gurcan M

Feature-based registration of histopathology images with different stains: an application for computerized follicular lymphoma prognosis.

基于特征的组织病理学图像具有不同污渍的图像：计算机化卵泡淋巴瘤预后的应用。

• DOI: 10.1016/j.cmpb.2009.04.012
• 发表时间: 2009-12
• 期刊: Computer methods and programs in biomedicine
• 影响因子: 6.1
• 作者: Cooper L;Sertel O;Kong J;Lozanski G;Huang K;Gurcan M
• 通讯作者: Gurcan M