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: 9278165
• 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/9278165
• 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; Diffuse; Disease; Early Diagnosis; Early treatment; Edema; Eligibility Determination; Ensure; Equipment; Excision; Family; Fractals; Goals; Grant; Growth; 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; Multiple Abnormalities; 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 volume; Resources; Risk; Risk stratification; Sensitivity and Specificity; Signal Transduction; Slide; Testing; Texture; Time; Tissues; Treatment outcome; Tumor Tissue; Tumor Volume; United States National Institutes of Health; Variant; base; brain abnormalities; 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（由适用提供）：与大脑和其他中枢神经系统（CNS）癌症有关的死亡率在过去的三到四十年中一直保持稳定，目的地在我们了解生物学，诊断和治疗脑癌的知识方面取得了巨大进步。早期诊断和治疗方面的进一步进展可能部分与改进的计算模型相关联，这些计算模型被普遍存在用于分析和分割脑肿瘤。临床应用继续有必要改善对难以检测的不良增强，多环球和小肿瘤的分割，这些肿瘤被多种异常组织（例如水肿，坏死和囊肿）所包围。此外，需要改进计算模型以处理不同的组织类型之间的不同边界以进行稳健的脑肿瘤分割（BTS）。此外，为了减少认知后遗症，当代方案采用了适应风险的治疗，其中风险分层基于手术切除后残留肿瘤的体积和诊断时的转移性疾病的存在。因此，如果不改善肿瘤体积的定量，则不太可能实现癌症结局，尤其是在儿童中的进一步改善。此外，在不同成像中心，研究，患者人群（成人和儿科）和设备上复制高级计算机算法是整个计算医学成像领域的持续问题。因此，该提出的研究项目的总体假设是，可以开发出强大的自动BTS和其他异常和正常的脑组织分割，以定量和跟踪肿瘤体积，这反过来又有助于改善早期诊断，随访，随访和治疗CNS肿瘤。拟议的项目旨在使用大量的BT神经影像学数据集专注于越来越普遍的神经影像模型，尤其是来自国家癌症研究所赞助的脑肿瘤细分（BRATS）挑战（http://wwww.braintumorsementation.org）。该目标将通过以下目的来实现：（1）使用来自不同成像中心的多模式MRIS确定新的特征，多类（组织）特征选择并分割难以检测的肿瘤和相关的异常； （2）通过融合基于ATLAS的肿瘤分割（ABT）和基于特征的BTS（FBTS），可以对肿瘤，其他异常和正常肿瘤以及脑肿瘤进行稳健分割； （3）通过多个NIH赞助的资源中心（例如神经影像学分析中心（NAC），全国医学图像计算联盟（NA-MIC）和国家图像指导疗法中心（NCIGT），将软件集成到广泛可用的工具（3D切片机（3D切片机）），用于BTS工具的更广泛散布； （4）验证和评估我们的集成BTS工具，以量化可检测性，灵敏度和特异性以及相应误差的改善。