Machine learning for risk-adjusted breast MRI screening

用于风险调整乳房 MRI 筛查的机器学习

• 批准号: 10316235
• 负责人: LUCAS C PARRA
• 金额: $ 63.33万
• 依托单位: CITY COLLEGE OF NEW YORK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-09 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10316235
• 关键词: 3-Dimensional; Address; Adoption; Algorithms; Appearance; Breast; Breast Cancer Detection; Breast Cancer Risk Factor; Breast Magnetic Resonance Imaging; Cancer Center; Cancerous; Clinical; Clinical Investigator; DNA Sequence Alteration; Data; Data Set; Databases; Detection; Diagnosis; Diagnostic; Diagnostic Imaging; Family; Family history of; Fostering; Future; Gadolinium; Genomics; Goals; High Risk Woman; Human; Image; Individual; Label; Lesion; Link; Location; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Mammography; Manuals; Medical Imaging; Memorial Sloan-Kettering Cancer Center; Methods; Modeling; Modernization; Mutation; Outcome; Pathology; Patients; Performance; Population; Reader; Recording of previous events; Resolution; Resources; Risk; Risk Assessment; Risk Estimate; Risk Factors; Scanning; Schedule; Site; Slice; Software Tools; Subgroup; Techniques; Technology; Testing; The Cancer Imaging Archive; Time; Training; Universities; Validation; Vision; Woman; Work; base; breast cancer diagnosis; breast imaging; cancer imaging; cancer therapy; clinical research site; cohort; contrast imaging; convolutional neural network; cost; data curation; data exchange; data sharing; deep learning; follow-up; high risk; imaging modality; individualized prevention; large datasets; lifetime risk; malignant breast neoplasm; predictive modeling; primary outcome; quality assurance; radiologist; risk prediction model; risk stratification; screening; screening program; symposium; tool; transfer learning; tumor

SUMMARY Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis to date. Women with a strong family history or related genetic mutations have an elevated risk of breast cancer and are recommended to participate in yearly MRI screenings. However, the rate of detection in this high-risk cohort is small, prompting a desire to reduce unnecessary MRI exams. The basic hypothesis of this project is that within the screening cohort the individual risk of a future cancer can be estimated based on the appearance of breast MRI and mammograms today. In preliminary work we have already identified low-risk women that could have omitted a screening session without missing a new cancer. The discovery of this lower-risk subgroup was made possible by modern deep-learning tools developed in preliminary work. Memorial Sloan Kettering Cancer Center (MSK) has accrued a database of approximately 70,000 breast MRI exams over 18 years along with the patients’ clinical outcomes. This unprecedented resource enables the training of modern machine learning “from the ground-up” to extract and classify volumetric MRI features. The specific aims of this project are as follows. Aim 1 (Data curation): Systematic analysis of the large dataset accrued at MSK requires careful curation including image content, image quality, pathology results, clinical follow-up, as well as demographic and genomic information. The outcome of this Aim is a curated dataset that can broadly benefit future technical efforts in breast diagnosis. Aim 2 (Deep learning): To make risk stratification quantitative we propose to analyze the MRI scans using modern deep networks that have been trained to identify the location and extent of a cancer. We will then transfer the MRI features of these trained networks as well as networks trained on mammograms to the task of diagnosis and risk assessment. The intended outcome of this Aim are predictive models with human-level performance at diagnosis and segmentation. Aim 3 (Risk adjusted screening): To reduce the burden of screening while maintaining sensitivity we will estimate the risk of finding a malignant tumor in the future, based on the present MRI exam and most recent mammogram as well as patient information. The machine-estimated risk will be used in a retrospective analysis to determine the primary outcome, namely, the number of exams that could have been omitted by scheduling a longer screening interval without compromising sensitivity. This will be repeated on newly accrued data at MSK, Duke and Johns Hopkins University (JHU) as secondary sites. Once validated, the risk-prediction model will be publicly released to encourage data sharing and clinical adoption. The preliminary work performed over the last two years has brought together a unique interdisciplinary team including clinical investigators on breast MRI at MSK, and machine-learning and medical imaging experts at CCNY, Duke and JHU. The platform technology that will be developed here is applicable beyond breast cancer, and the transfer learning approach applicable in particular to cancers with more limited datasets.

摘要 磁共振成像(MRI)是迄今为止诊断乳腺癌最敏感的成像手段。 有强烈家族史或相关基因突变的女性患乳腺癌的风险更高，而且 建议参加每年一次的核磁共振检查。然而，在这一高危人群中的检测率是 体型小，促使人们渴望减少不必要的核磁共振检查。这个项目的基本假设是，在 筛查队列个体患癌症的风险可以根据乳房的外观来估计。 今天的核磁共振和乳房X光检查。在初步工作中，我们已经确定了低风险女性可能有 省略了一次筛查，没有错过一例新的癌症。这一低风险亚群的发现是 在前期工作中开发的现代深度学习工具使之成为可能。纪念斯隆·凯特琳癌症 中心(MSK)在18年的时间里积累了大约70,000次乳腺MRI检查的数据库，以及 患者的临床结果。这一前所未有的资源使现代机器学习的培训成为可能 “从头开始”来提取和分类体积MRI特征。该项目的具体目标如下 下面是。目标1(数据管理)：对MSK积累的大型数据集进行系统分析需要仔细 包括图像内容、图像质量、病理结果、临床随访以及人口统计学 和基因组信息。这一目标的结果是一个经过精心策划的数据集，它可以广泛地帮助未来的技术 在乳房诊断方面的努力。目标2(深度学习)：为了使风险分层量化，我们建议 使用经过训练的现代深层网络分析MRI扫描，以确定位置和范围 癌症的症状。然后，我们将传输这些已训练网络的MRI特征以及 乳房X光检查到诊断和风险评估的任务。这一目标的预期结果是可以预测的 具有人类水平的诊断和分割性能的模型。目标3(风险调整筛查)： 在保持敏感性的同时减少筛查的负担，我们将估计发现恶性肿瘤的风险 根据目前的MRI检查和最近的乳房X光检查以及患者的情况，未来的肿瘤 信息。机器估计的风险将用于回溯性分析，以确定主要风险 结果，即通过安排较长的筛选间隔本可以省略的检查次数 而不会影响敏感性。这将在MSK、Duke和Johns的新积累数据上重复 霍普金斯大学(JHU)作为辅助站点。一旦得到验证，风险预测模型将公开 发布以鼓励数据共享和临床采用。在过去两年中完成的初步工作 几年来，汇集了一个独特的跨学科团队，包括乳腺MRI的临床研究人员，请访问 MSK，以及CCNY、Duke和JHU的机器学习和医学成像专家。平台技术 将在此开发的模型适用于乳腺癌以外的领域，并且迁移学习方法也适用 尤其是数据集更有限的癌症。