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（由申请人证明）：尽管我们对生物学，诊断和治疗脑癌的进一步进步，但与大脑和其他中央系统（CNS）癌症有关的死亡率稳定。治疗可能与临床肿瘤的部分相关，并分割了临床应用。在不同的组织组织中处理扩散的边界，以减少脑肿瘤分割（BTS）。诊断时的转移性。其他用于肿瘤体积的正常脑组织，进而有助于改善提出的肿瘤的早期诊断，随访和治疗。来自国家癌症研究所赞助的脑肿瘤分割（Brats）挑战的Y（http://www.braintumorsegentation.org）将通过以下AMS完成使用来自不同成像中心的多模式MRI的T肿瘤和相关异常，并通过融合基于ATLAS的肿瘤分割（ABTS）和基于特征的BTS（FBTS）ENT软件集成到广泛可用的工具（3D SliCer） ）通过多个NIH赞助的资源中心，例如神经影像分析中央（NAC），国家医学图像计算联盟（NA-MIC）ED疗法（NCIGT），用于更广泛的BTS工具；我们集成的BTS工具量化了可检测性，灵敏度和特异性以及相关性错误的改进。