Quantitative characterization and predictive modeling of cranial bone development in patients with craniosynostosis

颅缝早闭患者颅骨发育的定量特征和预测模型

• 批准号: 9889106
• 负责人: Antonio Reyes Porras Perez
• 金额: $ 12.38万
• 依托单位: CHILDREN'S RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9889106
• 关键词: 22q11.2; 3-Dimensional; Accounting; Affect; Age; Award; Biomedical Engineering; Bone Development; Bone Growth; Bone Resorption; Brain; Cephalic; Child; Childhood; Clinic; Clinical; Clinical Management; Coupled; Craniofacial Abnormalities; Craniosynostosis; Data Set; Deposition; Development; Diagnosis; Doctor of Philosophy; Dysmorphology; Evaluation; Evolution; Face; Fostering; Genetic; Goals; Growth; Health system; Image; Image Analysis; Imaging Device; Imaging technology; Impairment; Incidence; Institution; Intervention; Intracranial Hypertension; Joint structure of suture of skull; Knowledge; Lead; Live Birth; Medical Genetics; Medical Imaging; Mentors; Mentorship; Methods; Modeling; Muenke Syndrome; Operative Surgical Procedures; Outcome; Pathologic; Pathology; Patient-Focused Outcomes; Patients; Pattern; Phase; Phenotype; Population; Population Heterogeneity; Reconstructive Surgical Procedures; Reporting; Research Personnel; Shapes; Statistical Models; Surface; Surgical sutures; Syndrome; Techniques; Technology; Time; Training; Treatment outcome; United States National Institutes of Health; Work; X-Ray Computed Tomography; biomechanical model; bone; career; computer science; craniofacial; evidence base; improved; malformation; model development; multidisciplinary; neurosurgery; outcome forecast; population based; predictive modeling; quantitative imaging; skills; suture fusion; targeted imaging; tool

Project Summary Craniosynostosis is the early fusion of one or more cranial sutures and it affects 1 in 2,100-2,500 live births. It may lead to craniofacial malformations and brain growth constraints during childhood, resulting in elevated intra-cranial pressure and subsequent impaired brain growth. The decision to perform surgery, the right time of surgical treatment, and the interventional approach are determined by the subjective evaluation of cranial malformations, the estimation of their temporal evolution, and the subjective prediction of cranial bone development after surgery. This subjectivity results in late diagnosis and suboptimal treatments, with a high variability of incidence reports, treatment approaches, and outcomes among institutions. Since there are no clinical tools available to predict healthy or pathological cranial growth, there are no objective techniques to optimize the long-term outcome of the treatment of patients with craniosynostosis. The aims of this proposal are: (1) to create a personalized computational predictive model of normal cranial bone development, and (2) to create a cranial bone development model in presence of craniosynostosis. The PI will also quantify the coupled growth patterns of the bones and the brain, using a brain growth model available from his collaborators. In addition, he will contrast and illustrate the developed techniques in patients with craniosynostosis caused by Muenke syndrome, which is the most common cause of syndromic craniosynostosis. The PI, Dr. Antonio R. Porras, with a PhD in Medical Image Analysis and a strong background in both Computer Science and Biomedical Engineering, is uniquely suited to advance the aims of this proposal. During the training phase, Dr. Porras will foster his expertise in quantitative imaging and statistical modeling, and he will obtain specific training in: (1) cranial development during childhood, (2) medical genetics, and (3) clinical management of patients with cranial malformations. Dr. Porras has assembled a strong multi-disciplinary mentoring team of leaders in in Quantitative Imaging and Statistical Modeling (Dr. Marius George Linguraru – primary mentor), Medical Genetics and craniosynostosis (Dr. Maximilian Muenke, co-mentor), and cranial/brain development and clinical management of patients with craniosynostosis (Dr. Robert Keating, co-mentor). After the period of this award, Dr. Porras will have the expertise and the tools to promote his ultimate goal of transitioning to an independent career focused on computational phenotyping, modeling of childhood development, and quantitative assessment of developmental abnormalities during childhood.

项目摘要 颅缝早闭是一个或多个颅缝的早期融合，它影响2，100 - 2，500活 出生它可能导致颅面畸形和儿童时期大脑发育受限， 颅内压升高和随后的大脑发育受损。做手术的决定， 根据患者的主观评价，确定手术时机和介入方法。 颅骨畸形、其时间演变的估计以及颅骨的主观预测 手术后的发展。这种主观性导致诊断延迟和次优治疗， 不同机构之间的发病率报告、治疗方法和结局的差异。由于没有 临床工具可用于预测健康或病理性颅骨生长，没有客观的技术， 优化颅缝早闭患者治疗的长期结果。 本研究的目的是：（1）建立一个个性化的正常人的计算预测模型， 颅骨发育，以及（2）在存在下创建颅骨发育模型， 颅缝早闭PI还将量化骨骼和大脑的耦合生长模式，使用 大脑生长模型。此外，他还将对比和说明发达国家 Muenke综合征是最常见的原因， 颅缝早闭综合征PI，Antonio R博士。Porras拥有医学图像分析博士学位和 在计算机科学和生物医学工程的强大背景，是唯一适合推进 这一提议的目的。在培训阶段，Porras博士将培养他在定量成像和 统计建模，他将获得以下方面的具体培训：（1）儿童时期的颅骨发育，（2）医学 遗传学;（3）颅畸形患者的临床处理。波拉斯医生已经组织了一个 强大的多学科指导团队，由定量成像和统计建模领域的领导者组成（Dr. Marius乔治Linguraru -初级导师），医学遗传学和颅缝早闭（马克西米利安Muenke博士， 共同导师），颅/脑发育和颅缝早闭患者的临床管理（博士。 罗伯特·基廷，共同导师）。在此奖项之后，Porras博士将拥有专业知识和工具， 促进他过渡到一个独立的职业生涯专注于计算表型的最终目标， 儿童期发育建模，以及发育异常的定量评估， 童年.