Convergent AI for Precise Breast Cancer Risk Assessment

融合人工智能精准乳腺癌风险评估

• 批准号: 10172878
• 负责人: STEPHEN TC WONG
• 金额: $ 50.31万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10172878
• 关键词: Address; American College of Radiology; Anxiety; Architecture; Artificial Intelligence; Awareness; Benign; Biopsy; Breast; Breast Cancer Risk Factor; Breast Cancer Treatment; Breast biopsy; Cancer Etiology; Cancer Model; Cessation of life; Characteristics; Clinical; Clinical Pathology; Clinical Research; Clinical/Radiologic; Communication; Core Biopsy; Data; Databases; Decision Making; Development; Diagnostic; Digital Breast Tomosynthesis; Evaluation; Female; Hospitals; Hybrids; Image; Information Systems; Interobserver Variability; Joints; Lead; Lesion; Link; Malignant - descriptor; Malignant Neoplasms; Mammary Ultrasonography; Mammography; Measures; Medical Care Costs; Medical Imaging; Methodist Church; Methods; Modeling; Molecular; Multimodal Imaging; Names; Natural Language Processing; Observational Study; Oncologist; Online Systems; Operative Surgical Procedures; Output; Pain; Pathologic; Pathology; Patients; Performance; Physicians; Picture Archiving and Communication System; Probability; Prognostic Marker; Radiology Specialty; Recommendation; Reporting; Research Personnel; Retrieval; Risk Assessment; Risk Factors; Risk Management; Standardization; Stratification; Supervision; System; Techniques; Technology; Testing; Training; Ultrasonography; United States; Variant; Woman; augmented intelligence; autoencoder; base; breast cancer diagnosis; breast imaging; calcification; cancer diagnosis; cancer risk; cancer subtypes; cancer type; clinical data warehouse; clinical risk; clinically relevant; cost; data mining; deep learning; deep learning algorithm; demographics; density; follow-up; image processing; improved; malignant breast neoplasm; multimodal data; multimodality; novel; novel strategies; patient subsets; predictive marker; predictive modeling; prospective; radiologist; radiomics; risk stratification; screening; tool; two-dimensional

ABSTRACT Breast cancer continues to be one of the leading causes of cancer death among women in the United States, despite the advances made in the identification of prognostic and predictive markers for breast cancer treatment. Mammographic reporting is the first step in the screening and diagnosis of breast cancer. Abnormal mammographic findings such as a mass, abnormal calcifications, architectural distortion, and asymmetric density can lead to a cancer diagnosis. The American College of Radiology developed the Breast Imaging Reporting and Data System (BI-RADS) lexicon to standardize mammographic reporting to facilitate biopsy decision-making. However, application of the BI-RADS lexicon has resulted in substantial inter-observer variability, including inappropriate term usage and missing data. This observer variability has lead in part to a considerable variation in the rate of biopsy across the US, with a majority of breast biopsies ultimately found to be benign lesions. Hence, there is the need for a system that can better stratify the risk of cancer and define a more optimum threshold for biopsy. To address this need, we propose to develop an intelligent-augmented risk assessment system for breast cancer management based on multimodality image and clinical information with deep learning and data mining techniques. This study aims to develop a well-defined, novel risk assessment system incorporating multi-modality datasets with a novel predictive model that outputs a probability measure of cancer that is more clinically relevant and informative than the six discrete BI-RADS scores. Using mammographic or breast ultrasound BI- RADS reporting signatures and radiomics features, a predictive model that is more precise and clinically relevant may be developed to target well-characterized and defined specific biopsy patient subgroups rather than a broad heterogeneous biopsy group. Our proposed technique entails a novel strategy using Natural Language Processing to extract pertinent clinical risk factors related to breast cancer from vast amounts of patient charts automatically and integrate them with corresponding image-omics data and radiologist- generated reports. We will extract and quantitate image features from both large amounts of mammography and breast ultrasound images and combine them with the radiology reports and pertinent clinical risk profile and other patient characteristics to generate a risk assessment score to aid radiologists and oncologists in breast cancer risk assessment and biopsy decisions. Such a web-based application tool will be the first breast cancer risk assessment system based on integrative radiomics data augmented by AI methods. The iBRISK tool will enhance engagement between the patient and clinician for making an informed decision on whether or not to biopsy. Our hypothesis is that BI-RADS reports and the imaging metrics contain significant features for the breast cancer risk assessment and biopsy decision-making. By using BI-RADS reports and the imaging metrics, we will be able to develop new metrics to better breast cancer risk assessment. The novelty of the breast cancer risk assessment system is that it will incorporate a new predictive model that deploys deep learning and AI technology to provide a more reliable stratification of the BI-RADS subtypes for breast cancer risk assessment and reduce unnecessary breast biopsies and patients’ anxiety.

抽象的 乳腺癌仍然是联合妇女癌症死亡的主要原因之一 目的地，目的地在鉴定乳腺癌预后和预测标记方面取得的进步 治疗。乳腺X线摄影报告是筛查和诊断乳腺癌的第一步。异常 乳房X线摄影发现，例如质量，异常计算，建筑失真和不对称 密度可以导致癌症诊断。美国放射学院开发了乳房成像 报告和数据系统（BI-RADS）词典以标准化乳房X线图报告以促进活检 决策。但是，BI-RADS词典的应用导致了大量观察者 可变性，包括不适当的任期用法和丢失的数据。该观察者的可变性部分导致 整个美国的活检率很大，大多数乳房活检最终发现 良性病变。因此，需要一个可以更好地分层癌症风险并定义A的系统 活检的最佳阈值。为了满足这一需求，我们建议开发出智能的风险 基于多模式图像和临床信息的乳腺癌管理评估系统 深度学习和数据挖掘技术。 这项研究旨在开发一个定义明确的新型风险评估系统，并结合了多模式。 具有新型预测模型的数据集，该模型输出了癌症的概率度量 比六个离散的BI-RADS分数相关且信息丰富。使用乳房X线摄影或乳房超声 RAD报告标志和放射线特征，这是一个更精确和临床的预测模型 可以开发相关的目的是针对特定和定义的特定活检患者亚组 比广泛的异质活检组。我们提出的技术需要使用自然的新策略 语言处理以从大量 患者图表自动图表并将其与相应的图像词数据和放射科医生整合在一起 - 生成的报告。我们将从两个大量乳房X线摄影中提取和定量图像特征 和乳房超声图像，并将它们与放射学报告和相关临床风险概况相结合 以及其他患者特征以产生风险评估评分，以帮助放射科医生和肿瘤学家 乳腺癌的风险评估和活检决定。这样的基于网络的应用程序将是第一个乳房 癌症风险评估系统基于AI方法增强的综合放射组数据。 ibrisk 工具将加强患者与临床之间的参与，以便就是否或 不进行活检。 我们的假设是BI-RADS报告和成像指标包含乳房的重要特征 癌症风险评估和活检决策。通过使用BI-RADS报告和成像指标，我们 将能够开发新的指标来改善乳腺癌风险评估。乳腺癌的新颖性 风险评估系统将结合一个新的预测模型，该模型部署深度学习和AI 为乳腺癌风险评估提供更可靠的BI-RADS亚型分层的技术 并减少不必要的乳房活检和患者动画。