A fully automated PET radiomics framework

全自动 PET 放射组学框架

• 批准号: 10458241
• 负责人: Abhinav K Jha
• 金额: $ 49.29万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-06 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10458241
• 关键词: Address; Affect; American College of Radiology Imaging Network; Anatomy; Biological Markers; Biology; Cause of Death; Characteristics; Clinical; Clinical Trials; Computer software; Data; Data Set; Discipline of Nuclear Medicine; Disease; Engineering; Evaluation; Goals; Gold; Heterogeneity; Image; Knowledge; Lead; Malignant Neoplasms; Malignant neoplasm of thorax; Manuals; Measurement; Measures; Medical; Medical Oncologist; Metabolic; Methods; Molecular; Multicenter Trials; Noise; Non-Small-Cell Lung Carcinoma; Outcome; PET/CT scan; Patients; Physics; Positron-Emission Tomography; Prediction of Response to Therapy; Procedures; Process; Progression-Free Survivals; Property; Protocols documentation; Quality of life; Radiation Therapy Oncology Group; Reader; Regimen; Reproducibility; Resolution; Retrospective Studies; Role; Smoking History; Techniques; Time; Toxic effect; Training; Translating; Treatment Cost; base; biomarker development; biomarker discovery; cancer imaging; chemoradiation; clinical application; clinical translation; deep learning; early detection biomarkers; efficacy evaluation; fluorodeoxyglucose positron emission tomography; imaging modality; imaging scientist; improved; in vivo; mortality; multidisciplinary; novel; optimal treatments; patient oriented; personalized medicine; precision medicine; prognostic value; quantitative imaging; radiologist; radiomics; reconstruction; response; simulation; theories; tumor

Summary The overall goal of this proposal is to develop a fully automated PET radiomics framework and evaluate the efficacy of PET radiomic features (RFs) derived from this framework in predicting therapy response in patients with stage III non-small cell lung cancer (NSCLC). Radiomics is showing exciting promise in deriving biomarkers for several diseases. The potential to measure and evaluate the efficacy of radiomic features derived from PET for early prediction of therapy response is highly impactful since PET probes the functional characteristics of the tumor, where changes are manifested sooner in comparison to anatomical changes. However, PET images have high noise and limited resolution, which leads to inaccurate and imprecise RF measurements that then have limited clinical value. Previously we have developed techniques to optimize quantitative imaging methods and shown that these can help estimate more reliable quantitative metrics leading to better predictive ability with these metrics. Building on these past studies and by combining concepts from imaging physics, statistical inference theory, deep learning, we propose to develop methods that accurately and precisely estimate RFs from PET. These methods will include a fully automated PET segmentation method that will enable reliable delineation of tumor boundaries using a practical approach. Next, a no-gold-standard (NGS) evaluation technique will be developed to optimize RF quantification protocols. This technique will provide a mechanism for precise measurement of RFs from PET images without access to the ground truth RF value. The methods will be rigorously validated in the context of measuring radiomics features in patients with NSCLC using a combination of realistic simulations, physical phantom studies and existing patient data. Select RFs will then be retrospectively evaluated on predicting therapy response using existing data the ACRIN 6697 longitudinal clinical trial in patients with stage III NSCLC. A strong multidisciplinary team has been assembled for this project, consisting of an imaging scientist, clinical nuclear-medicine radiologists, medical oncologist with expertise in biomarker development for thoracic malignancies and biology of NSCLC, biostatistician, and a medical physicist. The proposed methods are poised to have a strong impact on PET radiomics by enabling measurement of precise and accurate RFs, and by facilitating the clinical translation of PET radiomics. The impact is strengthened as we investigate the predictive ability of the PET RFs in patients with stage III NSCLC, a leading cause of death with low overall survival, and with an important and timely need for improved personalized therapy regimens. Further, the methods developed in this project are general and potentially impact precision-medicine approaches for other cancers as well as other diseases where PET imaging has a clinical role.

概括 该提案的总体目标是开发一个完全自动化的宠物放射线框架并评估 从该框架预测患者治疗反应的PET放射素特征（RFS）的功效 III期非小细胞肺癌（NSCLC）。放射线学在推导生物标志物方面表现出令人兴奋的希望 对于几种疾病。测量和评估源自PET的放射性特征的功效的潜力 为了早期预测治疗反应是高度影响的，因为PET探究了该功能特征 肿瘤，与解剖学变化相比，变化更快。但是，宠物图像有 高噪声和有限的分辨率，导致不准确和不精确的RF测量结果 有限的临床价值。以前，我们已经开发了技术来优化定量成像方法和 表明这些可以帮助估计更可靠的定量指标，从而通过 这些指标。基于这些过去的研究并结合成像物理学的概念，统计 推论理论，深度学习，我们建议开发准确，精确估计RFS的方法 来自宠物。这些方法将包括一种全自动的宠物分割方法，该方法将启用可靠 使用实用方法描述肿瘤边界。接下来，无金牌标准（NGS）评估 将开发技术来优化RF定量协议。该技术将为 从PET图像中精确测量RF，而无需访问地面真相RF值。方法将 在使用A的NSCLC患者中测量放射线特征的背景下，请严格验证 现实模拟，物理幻影研究和现有患者数据的结合。选择RF将是 回顾性评估了使用现有数据ACRIN 6697纵向临床评估的评估 III期NSCLC患者的试验。一个强大的多学科团队已经为这个项目组成了 由成像科学家，临床核药物放射学家，具有专业知识的医学肿瘤学家组成 NSCLC，生物统计学家和医学物理学家的胸腔恶性肿瘤和生物学的生物标志物开发。 提出的方法有望通过启用测量来对PET放射线学产生强大影响 精确，准确的RF，并通过促进PET放射素学的临床翻译。影响得到加强 当我们研究PET RFS在III期NSCLC患者中的预测能力时，死亡的主要原因 总体生存率较低，并且需要改善个性化治疗方案的重要和及时的需求。 此外，该项目中开发的方法是一般的，并且潜在地影响了精度中等医学方法 对于其他癌症以及宠物成像具有临床作用的其他疾病。