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: 10028242
• 负责人: STEPHEN TC WONG
• 金额: $ 53.36万
• 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/10028242
• 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; 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 乳腺癌仍然是美国女性癌症死亡的主要原因之一 尽管在乳腺癌预后和预测标记物的鉴定方面取得了进展 乳房X光检查报告是乳腺癌筛查和诊断的第一步。 乳房X光检查结果，例如肿块、异常钙化、结构扭曲和不对称 美国放射学院开发了乳腺成像技术。 报告和数据系统 (BI-RADS) 词典，用于标准化乳房 X 线摄影报告以促进活检 然而，BI-RADS 词典的应用导致了观察者之间的大量互动。 变异性，包括不恰当的术语使用和缺失的数据，这种观察者的变异性在一定程度上导致了 美国各地的活检率差异很大，大多数乳腺活检最终发现 因此，需要一种能够更好地对癌症风险进行分层并定义癌症风险的系统。 为了满足这一需求，我们建议开发一种智能增强风险。 基于多模态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。 本研究旨在开发一个定义明确、新颖的风险评估系统，该系统包含多模态 具有新颖预测模型的数据集，可输出更具临床意义的癌症概率测量值 比使用乳房 X 光检查或乳腺超声 BI- 的六个离散 BI-RADS 评分更相关且信息丰富。 RADS 报告特征和放射组学特征，这是一种更精确、更符合临床的预测模型 可以开发相关的目标以明确特征和定义的特定活检患者亚组，而不是 与广泛的异质活检组相比，我们提出的技术需要一种使用自然的新策略。 语言处理从大量数据中提取与乳腺癌相关的临床危险因素 自动患者图表并将其与相应的图像组学数据和放射科医生集成 我们将从大量乳房X光检查中提取和量化图像特征。 和乳房超声图像，并将其与放射学报告和相关临床风险概况相结合 和其他患者特征来生成风险评估评分，以帮助放射科医生和肿瘤科医生 乳腺癌风险评估和活检决定将是第一个基于网络的应用工具。 基于人工智能方法增强的综合放射组学数据的癌症风险评估系统。 该工具将增强患者和临床医生之间的互动，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 通过使用 BI-RADS 报告和成像指标，我们可以进行癌症风险评估和活检决策。 将能够开发新的指标来更好地评估乳腺癌风险。 风险评估系统将采用部署深度学习和人工智能的新预测模型 技术为乳腺癌风险评估提供更可靠的 BI-RADS 亚型分层 减少不必要的乳房活检和患者的焦虑。