PET-GUIDED TUMOR VOLUME DELINEATION FOR RADIATION THERAPY

用于放射治疗的 PET 引导肿瘤体积描绘

• 批准号: 7647103
• 负责人: Wei Lu
• 金额: $ 2.22万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2010-02-12
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7647103
• 关键词: Algorithms; Anatomy; Cancer Intervention; Cancer Patient; Caring; Complex; Data; Data Set; Disorder by Site; Dose; Evaluation; Exploratory/Developmental Grant; Future; Gold; Head and Neck Cancer; Heterogeneity; Image; Image Analysis; Intervention; Lead; Magnetic Resonance Imaging; Manuals; Metabolic; Methods; Modeling; Molecular Probes; NIH Program Announcements; Operative Surgical Procedures; Outcome; Pathologic; Pathologist; Pathology; Patients; Physicians; Pilot Projects; Positron-Emission Tomography; Process; Radiation; Radiation Oncologist; Radiation therapy; Radioisotopes; Resolution; Retrospective Studies; Running; Shapes; Simulate; Slice; Specimen; Staging; Structure; Surface; Techniques; Time; Tumor Pathology; Tumor Volume; Validation; Variant; Work; base; cancer imaging; clinically relevant; head and neck cancer patient; image processing; improved; in vivo; interest; novel; prospective; public health relevance; reconstruction; research clinical testing; research study; simulation; tool; treatment planning; tumor; uptake; validation studies

DESCRIPTION (provided by applicant): PET-guided tumor volume delineation for radiation therapy Positron-emission tomography (PET), especially 18F-FDG, is increasingly being used in radiation therapy to assist the radiation oncologist in delineating the three-dimensional tumor shape for treatment planning. While PET imaging provides access to very sensitive molecular probes, the PET imaging process suffers from relatively poor spatial resolution. This, along with the PET image acquisition and reconstruction process, causes the apparent PET radioisotope uptake distribution to spread well beyond the physical radioisotope uptake distribution. Existing methods to segment the tumor boundary using PET images have focused on intensity-based thresholding, which is limited by ambiguity with respect to the threshold selection. The reason that intensity-based thresholding methods do not work well with PET images is because the intensity histograms are not bimodal in shape, so there is no stable threshold that can be automatically and objectively detected. We propose a novel two-stage image analysis process that overcomes the difficulties with the existing segmentation methods. The first stage, adaptive region growing (ARG), analyzes the PET image such that a reliable and reproducible landmark is produced that defines a "preliminary" tumor volume, which is larger than the actual tumor volume. The second stage employs a dual-front active contour (DFAC) model to conform the preliminary tumor surface to the PET radioisotope defined tumor volume. We will develop and optimize this two-stage model on phantom experiments, Monte Carlo simulated data, and finally on head-and-neck cancer patient studies, culminating in a prospective pilot study with spatially co-registered PET/CT images and pathology-based tumor volume correlation. Head- and neck cancer is ideal for developing and validating the segmentation process because it is anatomically complex, curable, it is possible to correlate anatomic and metabolic imaging findings with pathology, the tumors readily metabolize 18F-FDG, and accurate tumor segmentation is highly desirable due to the numerous surrounding critical structures. This proposal responds to the Program Announcement PA-06-371 titled "In vivo Cancer Imaging Exploratory/Developmental Grants (R21)", particularly, the two topics "in vivo cancer image displays and analyses" and "in vivo image-guided cancer interventions". We will use the R21 mechanism to develop this promising technique and provide preliminary data for a subsequent R01 application. If this process works, it will provide to the radiation oncologist, for the first time, an objective, reliable, and accurate PET segmentation tool to aid them in delineating the tumor, improving the accuracy and consistency of radiation therapy treatment plans and ultimately improving the quality of radiation therapy care. PUBLIC HEALTH RELEVANCE: We propose to develop and validate reliable methods for delineating tumors in Positron Emission Tomography (PET) for patients with head and neck cancer. Improved tumor delineation may lead to improved quality and accuracy of radiation dose delivery. This may improve the local control, and eventually the survival of these cancer patients.

正电子发射断层扫描（PET），特别是18F-FDG，越来越多地用于放射治疗，以协助放射肿瘤学家描绘三维肿瘤形状，以制定治疗计划。虽然PET成像提供了非常敏感的分子探针，但PET成像过程的空间分辨率相对较差。这与PET图像采集和重建过程一起，导致表观PET放射性同位素吸收分布远远超出物理放射性同位素吸收分布。利用PET图像分割肿瘤边界的现有方法主要集中在基于强度的阈值分割上，这受到阈值选择的模糊性的限制。基于强度的阈值分割方法对PET图像效果不佳的原因是由于PET图像的强度直方图形状不是双峰的，没有稳定的阈值可以自动、客观地检测出来。我们提出了一种新的两阶段图像分析方法，克服了现有分割方法的困难。第一阶段，自适应区域生长（ARG），分析PET图像，从而产生可靠且可重复的标记，定义“初步”肿瘤体积，该肿瘤体积大于实际肿瘤体积。第二阶段采用双前沿活动轮廓（DFAC）模型，使初步肿瘤表面符合PET放射性同位素定义的肿瘤体积。我们将在模拟实验、蒙特卡罗模拟数据以及头颈癌患者研究上开发和优化这一两阶段模型，最终在空间共注册PET/CT图像和基于病理的肿瘤体积相关性的前瞻性试点研究中达到顶点。头颈癌是开发和验证分割过程的理想选择，因为它在解剖学上是复杂的，可治愈的，有可能将解剖和代谢成像结果与病理相关联，肿瘤容易代谢18F-FDG，由于周围有许多关键结构，精确的肿瘤分割是非常可取的。本提案响应项目公告PA-06-371题为“体内癌症成像探索/发展资助（R21）”，特别是“体内癌症图像显示和分析”和“体内图像引导癌症干预”两个主题。我们将使用R21机制来开发这种有前途的技术，并为后续的R01应用提供初步数据。如果这个过程有效，它将首次为放射肿瘤学家提供一个客观、可靠和准确的PET分割工具，以帮助他们描绘肿瘤，提高放射治疗计划的准确性和一致性，并最终提高放射治疗护理的质量。公共卫生相关性：我们建议开发和验证可靠的方法来描绘头颈部癌症患者的正电子发射断层扫描（PET）肿瘤。改善肿瘤的描绘可以提高放射剂量传递的质量和准确性。这可能会改善局部控制，并最终提高这些癌症患者的生存率。