Quantitative Imaging Clinical Validation Center at Moffitt Cancer Center

项目摘要/摘要 癌症筛查的首要目标是在癌症定位、治疗和治疗的早期阶段发现癌症。 是可以治愈的。然而，癌症筛查与假阳性、不确定发现的高比率、 过度诊断和过度治疗，这些都是需要及早改善的严重限制 侦测工作。因为医学成像是许多癌症早期检测的关键组成部分，量化 成像/放射组学可以提供生物标记物，通过早期检测来解决其中许多限制。我们这群人， 莫菲特癌症中心定量成像临床验证中心(QICVC-MCC)帮助开创了形象 在之前的资助周期中利用BioMarker方法创建了第一个也是唯一一个EDRN临床验证 中心(CVC)致力于图像生物标记物的验证。对于乳腺癌，我们验证了几个乳房 在被归类为BI-RADS 4的妇女中的密度型风险标记和诊断模型，注意到三个 这一分类中的子类别是强诊断标记，并构建了生物图像库 对于这个小组来说。对于肺癌，我们进行了广泛的研究，应用传统的放射组学研究风险。 预测、区分良恶性结节、区分惰性结节和良性结节 侵袭性肺癌、预测肿瘤突变和预测治疗反应。在这次更新中，我们将 将我们的CVC从经过验证的基于功能的放射组学扩展为基准，以进行端到端的比较 深度学习(DL)方法，扩展到其他人群，并实施用于分析乳房、肺部、 和其他器官部位的图像。在乳房成像(目标1)中，我们将从参数建模扩展到 机器学习/数据仓库，用于改进风险、早期检测和诊断预测，并继续我们的数据 存储库开发。在肺部成像方面(目标2)，我们将从肺癌筛查扩展到 偶然发现的结节和手术切除的早期肺癌。此外，在目标3中，我们将寻求 在EDRN内提供更多机会，在其他器官部位进行图像生物标记物研究 乳房和肺(例如，前列腺、胰腺和皮肤)，以满足新兴的网络目标。EDRN 已经证明它大于单个项目的总和。因此，在目标4中，我们建议建立一个 用于存储和共享图像、算法、放射组学、临床数据和以下信息的存储库 生物群落。在这次CVC更新中，我们将系统地验证放射学特征和新的图像度量 在癌症的早期发现方面。这项研究意义重大，因为这样的信息可能能够补充 现有的临床指南并导致了应用非侵入性图像生物标记物的新策略。关于网络安全问题的研究 QICVC-MCC在NCI指定的综合癌症中心进行，这是一个杰出的 考虑到可以接触到大量的患者群体和优秀的资源，进行此类研究的环境， 以及部署这种生物标记物以改进个性化癌症护理的临床环境。

PROJECT SUMMARY/ABSTRACT An overarching goal of cancer screening is to detect cancer at an early stage while it is localized, treatable, and curable. However, cancer screening is associated with false positives, high rates of indeterminate findings, overdiagnosis, and overtreatment, which are serious limitations that need to be addressed to improve early detection efforts. Because medical imaging is a key component of early detection for many cancers, quantitative imaging/radiomics can provide biomarkers to address many of these limitations with early detection. Our group, Quantitative Imaging Clinical Validation Center at Moffitt Cancer Center (QICVC-MCC), helped pioneer image biomarker approaches leveraged in the prior funding cycle to create the first and only EDRN Clinical Validation Center (CVC) dedicated to the validation of image biomarkers. For breast cancer, we validated several breast density-type risk markers and diagnostic models in women classified as BI-RADS 4, noting the three subcategories within this classification were strong diagnostic markers, and constructed a bio-image repository for this subgroup. For lung cancer, we conducted extensive studies applying conventional radiomics for risk prediction, discrimination between malignant and benign nodules, distinguishing between indolent and aggressive lung cancers, predicting tumor mutations, and predicting treatment response. In this renewal, we will expand our CVC from validated conventional feature-based radiomics as a benchmark to compare end-to-end deep learning (DL) methods, expand to other populations, and implement AI platforms for analyzing breast, lung, and other organ site images. In breast imaging (Aim 1), we will expand our efforts from parametric modeling to machine learning/DL for improved risk, early detection, and diagnostic predictions and continue our data repository developments. In lung imaging (Aim 2), we will expand our efforts from lung cancer screening to incidentally detected nodules and surgically resected early-stage lung cancer. Additionally, in Aim 3 we will seek out additional opportunities within the EDRN to conduct studies of image biomarkers in other organ sites beyond breast and lung (e.g., prostate, pancreas, and cutaneous) to address emerging Network objectives. The EDRN has proven that it is greater than the sum of the individual projects. As such, in Aim 4 we propose to build a repository for the housing and sharing of images, algorithms, radiomics, clinical data, and information on biospecimens. In this CVC renewal, we will systematically validate radiomic features and novel image metrics in the early detection of cancer. This research is significant because such information may be able to complement existing clinical guidelines and lead to new strategies to apply noninvasive image biomarkers. The research of the QICVC-MCC is performed at an NCI-Designated Comprehensive Cancer Center, which is an outstanding environment to conduct such studies given the access to large patient populations and outstanding resources, and the clinical setting to deploy such biomarkers for improved personalized cancer care.