Developing Enabling PET-CT Image Analysis Tools for Predicting Response in Radiation Cancer Therapy

开发用于预测癌症放射治疗反应的 PET-CT 图像分析工具

基本信息

批准号：
9185750
负责人：
Xiaodong Wu
金额：
$ 16.58万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-06 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9185750
关键词：
Address Algorithms Automobile Driving Binding Biological Neural Networks Clinic Clinical Clinical Trials Data Dependence Development Diagnostic Neoplasm Staging Evaluation Excision Graph Hand Health Heterogeneity Image Image Analysis Individual Informatics Inherited Intraobserver Variability Learning Manuals Medical Methodology Methods Modality NIH Program Announcements Nature Outcome PET/CT scan Pathology Performance Physics Physiological Physiology Positron-Emission Tomography Prediction of Response to Therapy Predictive Value Protocols documentation Radiation Oncology Radiation therapy Reporting Research Sampling Scanning Scheme Slice Source Techniques Testing Therapeutic Time Tomography, Computed, Scanners Tumor Volume Tumor stage Uncertainty Work X-Ray Computed Tomography anticancer research base cancer radiation therapy cancer therapy clinical care clinical practice design fluorodeoxyglucose positron emission tomography image processing image reconstruction image registration imaging Segmentation imaging modality improved improved outcome innovation novel outcome forecast predicting response prognostic prognostic value radiation response response tool treatment planning treatment response tumor uptake usability

项目摘要

ABSTRACT The integrated Positron Emission Tomography and Computed Tomography (PET-CT) has become an indispensable tool in modern cancer therapy. Accurate target delineation is an inevitable first step towards fully making use of the potentials of PET-CT. However, in current clinical practice, this important task is typically performed visually on a slice-by-slice basis with very limited support of automated segmentation tools. The state-of-the-art PET-CT segmentation techniques rely on either a single modality or the fused PET-CT data, which may not fully take advantage of both modalities, thus compromising the segmentation accuracy. In addition, the state-of-the-art therapeutic response prediction methods highly rely on the handcrafted image features and parameters, which poses a limiting factor for their wide use in clinic. This research proposes to develop fast and objective PET-CT analysis methods to facilitate the utilization of the dual modality imaging for both large-scale clinical trial research and daily clinical care. The novel feature of the proposed methods is the first time to introduce co-segmentation for PET-CT tumor delineation, which recognizes the contour difference of tumors in PET from those in CT. New PET-CT specific priors will be explored and incorporated into the segmentation framework, further improving the accuracy of segmentation. The proposed response prediction method is built on the accurate tumor definition from our PET-CT co- segmentation approach, with an innovative design of a convolutional neural network for automatically learning hierarchical features directly from the PET-CT scans, leading to highly accurate prediction of response. The developed methods will be tested in comparison with state-of-the-art methods utilized today. The performance of the methods will be statistically assessed in data samples of sufficient sizes.

抽象的集成正电子发射断层扫描和计算机断层扫描（PET-CT）已成为一种现代癌症治疗中必不可少的工具。准确的目标描述是不可避免的第一步充分利用PET-CT的潜力。但是，在当前的临床实践中，重要任务通常是按切片进行视觉执行的，而对自动分割工具。最先进的PET-CT细分技术依赖于任何一个单一模态或融合的PET-CT数据，可能无法完全利用这两种模态，因此损害分割精度。此外，最先进的治疗反应预测方法高度依赖于手工制作的图像特征和参数，这构成了在诊所中广泛使用的限制因素。这项研究建议开发快速和客观的PET-CT 分析方法促进两种大规模临床的双重模态成像的利用试用研究和日常临床护理。提出方法的新颖特征是首次引入PET-CT肿瘤描述的共段，以确认的轮廓差异宠物中的肿瘤来自CT中的肿瘤。新的PET-CT特定先验将被探索并纳入分割框架，进一步提高了细分的准确性。提议响应预测方法建立在我们的PET-CT共同的准确肿瘤定义上分割方法，具有自动卷积神经网络的创新设计直接从PET-CT扫描中学习层次结构特征，从而高度准确地预测回复。将根据使用的最新方法对开发的方法进行测试今天。这些方法的性能将在充分的数据样本中进行统计评估尺寸。