Quantitative Image Modeling for Brain Tumor Analysis and Tracking

用于脑肿瘤分析和跟踪的定量图像建模

• 批准号: 9053035
• 负责人: Khan M Iftekharuddin
• 金额: $ 40万
• 依托单位: OLD DOMINION UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9053035
• 关键词: Address; Adult; Algorithms; Atlases; Base of the Brain; Bayesian Analysis; Biology; Brain; Brain Neoplasms; Brain scan; Central Nervous System Neoplasms; Child; Childhood; Classification; Clinical; Clinical Trials; Cognitive; Communities; Computational algorithm; Computer Simulation; Computer software; Cyst; Data; Data Set; Diagnosis; Disease; Early Diagnosis; Early treatment; Edema; Eligibility Determination; Ensure; Equipment; Excision; Family; Fractals; Goals; Grant; Growth; Histocompatibility Testing; Histopathology; Image; Image Analysis; Knowledge; Label; Lesion; Literature; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of central nervous system; Medical Imaging; Methods; Modeling; National Cancer Institute; Necrosis; Noise; Normal tissue morphology; Operative Surgical Procedures; Outcome; Patients; Pediatric Hospitals; Performance; Philadelphia; Protocols documentation; Radiation therapy; Research; Research Project Grants; Residual Tumors; Resources; Risk; Sensitivity and Specificity; Signal Transduction; Slice; Slide; Stratification; Testing; Texture; Time; Tissues; Treatment outcome; Tumor Tissue; Tumor Volume; United States National Institutes of Health; Variant; base; brain tissue; clinical application; follow-up; image guided radiation therapy; image guided therapy; improved; mortality; multimodality; neuroimaging; novel; patient population; programs; public health relevance; quantitative imaging; tool; tumor

 DESCRIPTION (provided by applicant):Mortality rates related to brain and other Central Nervous System (CNS) cancers have held steady over the last three or four decades, despite tremendous advancements in our knowledge about the biology, diagnosis, and treatment of brain cancer. Further progress in early diagnosis and treatment is likely to be associated, in part, with improving computational models that are used ubiquitously for analyzing and segmenting brain tumors. Clinical applications continue to necessitate improved segmentation of hard-to- detect poorly enhanced, multi-foci and small tumors that are surrounded by multiple abnormal tissues such as edema, necrosis and cysts. In addition, computational models need to be improved for handling diffusive boundaries among different tissue types for robust Brain Tumor Segmentation (BTS). Furthermore, in an effort to reduce cognitive sequelae, contemporary protocols employ risk-adapted therapy in which risk stratification is based on the volume of residual tumor after surgical resection and the presence of metastatic disease at diagnosis. Therefore, further improvement in cancer outcomes, particularly among children, is unlikely to be achieved without improved quantitation of tumor volume. Furthermore, replicating advanced computer algorithms across different imaging centers, studies, patient populations (adult and pediatric) and equipment is a persistent problem for the entire field of computational medical imaging. Consequently, the overall hypothesis of this proposed research project is that a robust automatic BTS and other abnormal and normal brain tissue segmentation can be developed for quantitation and tracking of tumor volume which, in turn, will help improve early diagnosis, follow- up and treatment of CNS tumors. The proposed project aims to focus on principled computational modeling using a huge amount of neuroimaging datasets for BTS that are becoming prevalent, especially from the National Cancer Institute's sponsored Brain Tumor Segmentation (BRATS) challenges (http://www.braintumorsegmentation.org). This goal will be accomplished via the following aims: (1) Identify novel features, multiclass (tissue) feature selection and segmentation of hard-to-detect tumors and associated abnormalities using multimodal MRIs from different imaging centers; (2) Enable robust segmentation of tumor, other abnormal and normal tissues and tacking of brain tumor by fusing atlas-based tumor segmentation (ABTS) and feature-based BTS (FBTS); (3) implement software integration into a widely available tool (3D Slicer) available via multiple NIH sponsored Resource Centers such as the Neuroimaging Analysis Center (NAC), the National Alliance for Medical Image Computing (NA-MIC), and the National Center for Image Guided Therapy (NCIGT), for wider dissemination of BTS tool; and (4) Validate and evaluate our integrated BTS tool to quantify improvements in the detectability, sensitivity and specificity, and corresponding errors.

 描述(申请人提供)：尽管我们在脑癌的生物学、诊断和治疗方面的知识取得了巨大的进步，但在过去的三四十年里，与脑和其他中枢神经系统(CNS)癌症相关的死亡率一直保持稳定。在早期诊断和治疗方面的进一步进展可能在一定程度上与改进计算模型有关，这些计算模型普遍用于分析和分割脑瘤。临床应用仍然需要对难以检测的低强化、多病灶和小肿瘤进行改进分割，这些肿瘤周围有多个异常组织，如水肿、坏死和囊肿。此外，需要改进计算模型来处理不同组织类型之间的扩散边界，以实现稳健的脑肿瘤分割(BTS)。此外，为了减少认知后遗症，当代的治疗方案采用了风险适应疗法，其中风险分层是基于手术切除后残留肿瘤的体积和诊断时是否存在转移疾病。因此，如果不改进肿瘤体积的量化，癌症预后的进一步改善，特别是在儿童中，不太可能实现。此外，在不同的成像中心、研究、患者群体(成人和儿童)和设备之间复制先进的计算机算法是整个计算医学成像领域的一个长期问题。因此，这项拟议研究项目的总体假设是，可以开发一个健壮的自动BTS和其他异常和正常的脑组织分割来量化和跟踪肿瘤体积，这反过来将有助于改善中枢神经系统肿瘤的早期诊断、随访和治疗。拟议的项目旨在使用正在变得普遍的大量神经成像数据集，特别是来自国家癌症研究所赞助的脑瘤分割(BRATS)挑战(http://www.braintumorsegmentation.org).)的数据，重点研究针对BTS的原则性计算建模这一目标将通过以下目标来实现：(1)使用来自不同成像中心的多模式磁共振成像来识别难以检测的肿瘤和相关异常的新特征、多类(组织)特征选择和分割；(2)通过融合基于图谱的肿瘤分割(ABTS)和基于特征的BTS(FBTS)，实现对肿瘤、其他异常和正常组织的稳健分割和脑肿瘤的跟踪；(3)将软件集成到NIH赞助的多个资源中心(如神经成像分析中心(NAC)、国家医学图像计算联盟(NA-MIC)和国家图像引导治疗中心(NCIGT))提供的广泛可用的工具(3D Slicer)中，以更广泛地传播BTS工具；以及(4)验证和评估我们集成的BTS工具，以量化在可检测性、敏感性和特异性以及相应错误方面的改善。