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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要癌症筛查的首要目标是在癌症处于局部、可治疗和早期阶段时发现癌症。可治愈的。然而，癌症筛查与假阳性、不确定结果的高发生率、过度诊断和过度治疗是严重的局限性，需要解决以早期改善检测工作。由于医学成像是许多癌症早期检测的关键组成部分，因此定量成像/放射组学可以提供生物标志物，通过早期检测来解决许多这些限制。我们组，莫菲特癌症中心 (QICVC-MCC) 的定量成像临床验证中心帮助开创了影像在之前的融资周期中利用生物标志物方法创建第一个也是唯一一个 EDRN 临床验证中心（CVC）致力于图像生物标志物的验证。对于乳腺癌，我们验证了几种乳腺癌被分类为 BI-RADS 4 的女性的密度型风险标记和诊断模型，注意到这三个该分类中的子类别是强有力的诊断标记，并构建了生物图像存储库对于这个子组。对于肺癌，我们应用传统放射组学进行了广泛的风险研究预测、区分恶性和良性结节、区分惰性和惰性结节侵袭性肺癌，预测肿瘤突变，并预测治疗反应。在这次更新中，我们将将我们的 CVC 从经过验证的传统基于特征的放射组学中扩展出来，作为端到端比较的基准深度学习（DL）方法，扩展到其他人群，并实施人工智能平台来分析乳房、肺部、和其他器官部位图像。在乳腺成像（目标 1）中，我们将把工作范围从参数化建模扩展到机器学习/深度学习以提高风险、早期检测和诊断预测并继续我们的数据存储库的开发。在肺部成像（目标2）方面，我们将把工作范围从肺癌筛查扩展到偶然发现结节并手术切除早期肺癌。此外，在目标 3 中，我们将寻求在 EDRN 内提供更多机会对其他器官部位的图像生物标志物进行研究乳房和肺部（例如前列腺、胰腺和皮肤），以解决新出现的网络目标。欧洲发展研究网络事实证明，它大于各个项目的总和。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、临床数据和信息的存储库生物样本。在本次 CVC 更新中，我们将系统地验证放射组学特征和新颖的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应用非侵入性图像生物标志物的新策略。研究的 QICVC-MCC 在 NCI 指定的综合癌症中心进行，该中心是一个出色的鉴于可以获得大量患者群体和丰富的资源，因此有条件进行此类研究，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

期刊论文数量（28）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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.