Machine learning for risk-adjusted breast MRI screening
用于风险调整乳房 MRI 筛查的机器学习
基本信息
- 批准号:10521264
- 负责人:
- 金额:$ 64.25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:3-DimensionalAddressAdoptionAlgorithmsAppearanceBreastBreast Cancer DetectionBreast Cancer Risk FactorBreast Magnetic Resonance ImagingCancer CenterCancerousClassificationClinicalClinical InvestigatorDNA Sequence AlterationDataData SetDatabasesDetectionDiagnosisDiagnosticFamilyFamily history ofFosteringFutureGadoliniumGenomicsGoalsHigh Risk WomanHumanImageIndividualLabelLearningLesionLinkLocationMRI ScansMachine LearningMagnetic Resonance ImagingMalignant - descriptorMalignant NeoplasmsMammographyManualsMedical ImagingMemorial Sloan-Kettering Cancer CenterMethodsModelingModernizationMutationOutcomePathologyPatientsPerformancePopulationReaderRecommendationRecording of previous eventsResolutionResourcesRiskRisk AdjustmentRisk AssessmentRisk EstimateRisk FactorsScanningScheduleSiteSliceSoftware ToolsSubgroupTechniquesTechnologyTestingThe Cancer Imaging ArchiveTimeTrainingUniversitiesValidationVisionWomanWorkbreast cancer diagnosisbreast imagingcancer therapyclinical research sitecohortconvolutional neural networkcostdata curationdata exchangedata sharingdeep learningfollow-uphigh riskimaging modalityindividualized preventionlarge datasetslifetime riskmalignant breast neoplasmpredictive modelingprimary outcomequality assuranceradiologistrisk predictionrisk prediction modelrisk stratificationscreeningscreening programsymposiumtechnology platformtooltransfer learningtumor
项目摘要
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.
概括
磁共振成像 (MRI) 是迄今为止诊断乳腺癌最敏感的成像方式。
具有明显家族史或相关基因突变的女性患乳腺癌的风险较高,并且
建议参加年度 MRI 筛查 然而,这一高风险人群的检出率较低。
小,促使人们希望减少不必要的 MRI 检查 该项目的基本假设是
在筛查队列中,可以根据乳腺癌的外观来估计个人未来患癌症的风险
今天,在初步工作中,我们已经通过 MRI 和乳房 X 光检查确定了可能患有此病的低风险女性。
省略了一次筛查,但没有错过一种新的癌症。
纪念斯隆·凯特琳癌症中心前期工作中开发的现代深度学习工具使之成为可能。
中心 (MSK) 积累了 18 年来大约 70,000 例乳腺 MRI 检查的数据库,以及
这种前所未有的资源使得现代机器学习的培训成为可能。
“从头开始”提取和分类体积 MRI 特征该项目的具体目标如下。
目标 1(数据管理):需要对 MSK 积累的大型数据集进行仔细的系统分析。
管理,包括图像内容、图像质量、病理结果、临床随访以及人口统计
该目标的成果是一个精心策划的数据集,可以广泛惠及未来的技术。
目标努力 2(深度学习):为了量化风险分层,我们建议
使用经过训练可识别位置和范围的现代深度网络分析 MRI 扫描
然后,我们将转移这些经过训练的网络以及经过训练的网络的 MRI 特征。
乳房X光检查对诊断和风险评估的预期任务的结果是预测性的。
目标 3(风险调整筛选):在诊断和分割方面具有人类水平性能的模型。
减轻筛查负担,同时保持敏感性,我们将估计发现恶性疾病的风险
根据目前的 MRI 检查和最近的乳房 X 光检查以及患者,未来的肿瘤
机器估计的风险将用于回顾性分析以确定主要风险。
结果,即通过安排更长的筛查间隔可以省略的检查数量
这将在 MSK、杜克大学和约翰斯大学新获得的数据上重复进行。
霍普金斯大学 (JHU) 作为辅助站点一旦经过验证,风险预测模型将被公开。
发布以鼓励数据共享和临床采用。过去两年进行的初步工作。
多年来聚集了一个独特的跨学科团队,包括乳腺 MRI 的临床研究人员
MSK 以及 CCNY、杜克大学和约翰霍普金斯大学的机器学习和医学成像专家 平台技术。
这里将开发的方法适用于乳腺癌以外的领域,并且迁移学习方法也适用
特别是对于数据集更有限的癌症。
项目成果
期刊论文数量(0)
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{{ truncateString('LUCAS C PARRA', 18)}}的其他基金
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 - 财政年份:2020
- 资助金额:
$ 64.25万 - 项目类别:
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直流电刺激对突触可塑性的影响
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9913593 - 财政年份:2016
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靶向经颅电疗系统加速中风恢复
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8199404 - 财政年份:2011
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8286826 - 财政年份:2010
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CRCNS: Effects of Weak Applied Currents on Memory Consolidation During Sleep
CRCNS:弱施加电流对睡眠期间记忆巩固的影响
- 批准号:
8286826 - 财政年份:2010
- 资助金额:
$ 64.25万 - 项目类别:
CRCNS: Effects of Weak Applied Currents on Memory Consolidation During Sleep
CRCNS:弱施加电流对睡眠期间记忆巩固的影响
- 批准号:
8055164 - 财政年份:2010
- 资助金额:
$ 64.25万 - 项目类别:
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- 资助金额:
$ 64.25万 - 项目类别:
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CRCNS:弱施加电流对睡眠期间记忆巩固的影响
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8517819 - 财政年份:2010
- 资助金额:
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