Potential of Radiomics and AI in the prediction of breast cancer risk and mutation status in high risk patients with confirmed mutation or calculated high risk status (PRo-mics-BrCa)

放射组学和人工智能在预测已确认突变或计算出的高风险状态的高风险患者的乳腺癌风险和突变状态方面的潜力 (PRo-mics-BrCa)

• 批准号: 428224258
• 负责人: Privatdozent Dr. Christoph Engel
• 金额: --
• 依托单位: Institut für Medizinische Informatik, Statistik und Epidemiologie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/428224258?language=en
• 关键词: Potential; Radiomics; AI; prediction; breast

Pathogenic gene mutations were identified that significantly increase the lifetime risk of developing breast cancer, most prominently in either of the genes BRCA1 and BRCA2. Harmful mutations in these genes produce a hereditary breast-ovarian cancer syndrome in affected families. Mutations in BRCA1/2 are uncommon, and breast cancer is relatively common, so these mutations account for only five to ten percent of all breast cancer cases in women. As full-digital breast imaging, both mammography and MRI, has been introduced, an extended imaging based phenotyping and subsequently multivariate extended phenotype-genotype correlation studies come into reach in order to reveal new specific imaging based indicators for breast cancer risk. This application aims at providing a powerful set of tools for extracting multiscale tissue characterization based on breast imaging with particular focus on temporal change and asymmetry analysis, applied to a subpopulation of a large-scale breast imaging dataset acquired over the last ten years in a high-risk population in Germany covering individual imaging volumes (>6,000 women with on average >4 examinations). All of these women were offered DNA testing and a part received additional clinical testing due to suspicious findings. We will access and statistically analyze all of this data in collaboration with the German Consortium for Hereditary Breast and Ovarian Cancer. Since DNA testing cannot be completely realized in a larger screening population, and the known breast cancer risk genes only explain 1 in 4 of the identified hereditary breast cancer risk cases, we will test a number of quantitative imaging biomarkers from dynamic contrast-enhanced breast MRI and from full-field digital mammography (FFDM), and correlate those imaging biomarkers and their temporal developments with breast cancer incidence, with the presence of pathogenic gene mutations as well as with the identified hereditary breast cancer risk. This correlation will produce an imaging-based subphenotype classification with potential implications for extended stratification in future surveillance programs. Imaging biomarkers will include, among other parameters, morphological descriptors, volumetric breast tissue composition and density, tissue heterogeneity and asymmetry, contrast enhancement patterns, as well as the longitudinal changes thereof. We will perform the Radiomics analysis as well as state-of-the-art convolution neural network deep learning approaches extraction for morphological features of breast tissue composition and density as well as background enhancement and the relationship to cancer incidence in patients with BRCA 1 or 2 mutation, less frequent mutations and calculated elevated risk for breast cancer. Overall, we aim at developing an advanced toolset for improved early detection of breast cancer and at deriving novel hypotheses with respect to extended breast cancer risk.

发现致病性基因突变显著增加了患乳腺癌的终生风险，其中最显著的是BRCA1和BRCA2基因突变。这些基因的有害突变在受影响的家庭中产生遗传性乳腺癌-卵巢癌综合征。BRCA1/2的突变并不常见，而乳腺癌相对常见，因此这些突变只占女性乳腺癌病例的5%到10%。随着全数字乳房成像（包括乳房x线照相术和MRI）的引入，基于扩展成像的表型和随后的多变量扩展表型-基因型相关性研究得以实现，以揭示新的基于特定成像的乳腺癌风险指标。该应用程序旨在提供一套强大的工具，用于提取基于乳房成像的多尺度组织特征，特别关注时间变化和不对称分析，应用于过去十年在德国高风险人群中获得的大规模乳房成像数据集的亚群，涵盖个人成像体积（bbb6000名女性，平均>4次检查）。所有这些女性都接受了DNA检测，其中一部分由于发现可疑而接受了额外的临床检测。我们将与德国遗传性乳腺癌和卵巢癌协会合作访问并统计分析所有这些数据。由于DNA检测不能在更大的筛查人群中完全实现，并且已知的乳腺癌风险基因只能解释四分之一的已确定的遗传性乳腺癌风险病例，我们将测试来自动态对比增强乳房MRI和全场数字乳房x线摄影（FFDM）的大量定量成像生物标志物，并将这些成像生物标志物及其时间发展与乳腺癌发病率联系起来。致病基因突变的存在以及确定的遗传性乳腺癌风险。这种相关性将产生基于成像的亚表型分类，对未来监测计划的扩展分层有潜在的影响。成像生物标志物将包括形态学描述符、乳腺组织体积组成和密度、组织异质性和不对称性、对比度增强模式以及其纵向变化等参数。我们将进行放射组学分析以及最先进的卷积神经网络深度学习方法，以提取乳腺组织组成和密度的形态学特征，以及背景增强，以及BRCA 1或2突变患者与癌症发病率的关系，较少的突变和计算的乳腺癌风险升高。总的来说，我们的目标是开发一种先进的工具集，以改善乳腺癌的早期检测，并就乳腺癌风险的扩大提出新的假设。