A fully automated PET radiomics framework

总结 该提案的总体目标是开发一个完全自动化的PET放射组学框架，并评估 从该框架中推导出的PET放射组学特征（RF）在预测患者治疗反应方面的有效性 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 几种疾病。测量和评价PET放射组学特征有效性的潜力 早期预测治疗反应是非常有影响力的，因为PET探测的功能特征， 肿瘤，与解剖学变化相比，变化更快地表现出来。然而，PET图像具有 高噪声和有限的分辨率，这导致不准确和不精确的RF测量， 临床价值有限。以前，我们已经开发了优化定量成像方法的技术， 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。基于这些过去的研究，并结合成像物理学的概念，统计 推理理论，深度学习，我们建议开发准确和精确估计RF的方法 从PET这些方法将包括全自动PET分割方法， 使用实用方法描绘肿瘤边界。其次，非黄金标准（NGS）评估 将开发一种技术来优化射频量化协议。这项技术将提供一种机制， 精确测量PET图像中的RF，而无需访问真实RF值。述方法将 在测量NSCLC患者放射组学特征的背景下，使用 结合真实模拟、物理体模研究和现有患者数据。选择RF，然后 回顾性评价了使用现有数据预测治疗反应的ACRIN 6697纵向临床研究 在III期NSCLC患者中进行的试验。为这个项目组建了一个强大的多学科小组， 包括一名成像科学家，临床核医学放射科医生，医学肿瘤学家， 胸部恶性肿瘤生物标志物开发和NSCLC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 精确和准确的RF，并通过促进PET放射组学的临床翻译。影响力加强 当我们研究PET RF在III期NSCLC患者中的预测能力时， 总存活率低，并且对改进的个性化治疗方案具有重要和及时的需求。 此外，该项目中开发的方法是通用的，可能会影响精确医学方法 用于其他癌症以及PET成像具有临床作用的其他疾病。

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.