Quantitative Imaging Clinical Validation Center at Moffitt Cancer Center

莫菲特癌症中心定量成像临床验证中心

• 批准号: 10706028
• 负责人: JOHN J HEINE
• 金额: $ 88.16万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-22 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706028
• 关键词: Address; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Benchmarking; Benign; Biological Markers; Biopsy; Biostatistical Methods; Blood; Breast; Cancer Center; Cancer Detection; Classification; Clinical; Clinical Data; Collaborations; Colon; Communication; Complement; Comprehensive Cancer Center; Cutaneous; Data; Dedications; Development; Diagnosis; Diagnostic; Discrimination; Disease; Early Detection Research Network; Early Diagnosis; Electronic Medical Records and Genomics Network; Environment; Funding; Goals; Guidelines; Housing; Image; Individual; Indolent; Institution; Lung; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Mammography; Measurement; Medical Imaging; Modality; Modeling; Mutation; Nodule; Operative Surgical Procedures; Organ; Ovary; Pancreas; Performance; Play; Population; Prediction of Response to Therapy; Process; Productivity; Prostate; Recurrence; Research; Resected; Resources; Risk; Risk Marker; Role; Sampling; Screening for cancer; Shapes; Site; Subcategory; Subgroup; Sum; Techniques; Technology; Texture; Tissues; Validation; Vascularization; Woman; bioimaging; breast density; breast imaging; cancer biomarkers; cancer diagnosis; clinical imaging; data repository; deep learning; diagnostic biomarker; experience; image archival system; imaging biomarker; improved; interest; learning strategy; lung cancer screening; lung imaging; malignant breast neoplasm; non-invasive imaging; novel; overtreatment; patient population; personalized cancer care; prognostication; quantitative imaging; radiomics; repository; risk prediction; screening; standard of care; success; treatment response; tumor; working group

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.

项目摘要/摘要 癌症筛查的总体目标是在癌症局部，可治疗和 可以治愈。但是，癌症筛查与假阳性，高度不确定的发现率有关， 过度诊断和过度治疗，这是需要解决的严重局限性以提早改善 检测工作。因为医学成像是许多癌症早期检测的关键组成部分，所以定量 成像/放射组学可以提供生物标志物，以通过早期检测来解决许多此类局限性。我们的小组， Moffitt癌症中心（QICVC-MCC）的定量成像临床验证中心，帮助先锋图像 生物标志物在先前的资金周期中利用的方法来创建第一个也是唯一的EDRN临床验证 中心（CVC）致力于验证图像生物标志物。对于乳腺癌，我们验证了几个乳房 分类为Bi-Rads 4的女性中的密度型风险标记和诊断模型，指出三个 此分类中的子类别是强诊断标记，并构建了生物图像存储库 对于此子组。对于肺癌，我们进行了广泛的研究，应用了常规放射线学风险 预测，恶性结节和良性结节之间的歧视，区分懒惰和 侵略性肺癌，预测肿瘤突变并预测治疗反应。在此续约中，我们将 将我们的CVC从经过验证的常规基于功能的放射线学作为基准来比较端到端的基准 深度学习（DL）方法，扩展到其他人群，并实现AI平台，以分析乳房，肺， 和其他器官位点图像。在乳房成像（AIM 1）中，我们将把精力从参数建模扩展到 机器学习/DL可改善风险，早期检测和诊断预测，并继续我们的数据 存储库发展。在肺成像（AIM 2）中，我们将从肺癌筛查扩大到 偶然检测到结节和手术切除的早期肺癌。此外，在AIM 3中，我们将寻求 在EDRN内提供其他机会，以对其他器官的其他器官站点进行图像生物标志物进行研究 乳腺癌和肺（例如前列腺，胰腺和皮肤），以解决新兴的网络目标。 EDRN 已经证明，它大于单个项目的总和。因此，在目标4中，我们建议建造一个 有关图像，算法，放射组学，临床数据以及有关信息的房屋的存储库 生物测量。在此CVC续订中，我们将系统地验证放射线特征和新型图像指标 在癌症的早期发现中。这项研究很重要，因为这些信息可能能够补充 现有的临床指南，并导致采用非侵入性图像生物标志物的新策略。研究 QICVC-MCC在NCI指定的综合癌症中心进行，这是一个出色的 鉴于获得大量患者人群和出色资源的环境， 以及部署此类生物标志物以改善个性化癌症护理的临床环境。

项目成果

Breast density analysis of digital breast tomosynthesis.

• DOI: 10.1038/s41598-023-45402-x
• 发表时间: 2023-10-31
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Heine, John;Fowler, Erin E. E.;Weinfurtner, R. Jared;Hume, Emma;Tworoger, Shelley S.
• 通讯作者: Tworoger, Shelley S.