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全自动 PET 放射组学框架并评估 源自该框架的 PET 放射组学特征 (RF) 在预测患者治疗反应方面的功效 III 期非小细胞肺癌 (NSCLC)。放射组学在衍生生物标志物方面显示出令人兴奋的前景 对于多种疾病。测量和评估 PET 放射组学特征功效的潜力 由于 PET 可以探测细胞的功能特征，因此对于治疗反应的早期预测非常有影响力。 肿瘤，与解剖学变化相比，肿瘤的变化表现得更快。然而，PET 图像有 高噪声和有限的分辨率，这会导致射频测量不准确且不精确，从而导致 临床价值有限。之前我们已经开发了优化定量成像方法的技术和 研究表明，这些可以帮助估计更可靠的定量指标，从而提高预测能力 这些指标。以这些过去的研究为基础，结合成像物理学、统计学的概念 推理理论、深度学习，我们建议开发能够准确估计 RF 的方法 来自PET。这些方法将包括全自动 PET 分割方法，该方法将实现可靠的 使用实用方法描绘肿瘤边界。接下来，进行非黄金标准 (NGS) 评估 将开发技术来优化射频量化协议。该技术将提供一种机制 从 PET 图像精确测量 RF，无需获取地面真实 RF 值。这些方法将 在使用测量 NSCLC 患者的放射组学特征的背景下进行严格验证 现实模拟、物理模型研究和现有患者数据的结合。然后选择 RF 使用 ACRIN 6697 纵向临床数据对预测治疗反应进行回顾性评估 III 期 NSCLC 患者的试验。该项目已组建了一支强大的多学科团队， 由具有以下专业知识的影像科学家、临床核医学放射科医生、肿瘤内科医生组成 胸部恶性肿瘤生物标志物开发和非小细胞肺癌生物学、生物统计学家和医学物理学家。 所提出的方法有望通过测量 PET 放射组学产生重大影响 精确准确的 RF，并促进 PET 放射组学的临床转化。影响力进一步加强 当我们研究 PET RF 对 III 期非小细胞肺癌（死亡的主要原因）患者的预测能力时 总体生存率较低，并且迫切需要改进的个性化治疗方案。 此外，该项目开发的方法是通用的，并且可能影响精准医疗方法 对于其他癌症以及 PET 成像具有临床作用的其他疾病。