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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.

摘要