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Convergent AI for Precise Breast Cancer Risk Assessment

融合人工智能精准乳腺癌风险评估

基本信息

批准号：
10632014
负责人：
STEPHEN TC WONG
金额：
$ 49.3万
依托单位：
METHODIST HOSPITAL RESEARCH INSTITUTE
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：
2020-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10632014
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 Categories 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 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 Risk Reduction Standardization Stratification System Techniques Technology Testing Training Ultrasonography United States Variant Woman augmented intelligence autoencoder 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 deep learning model demographics density follow-up image processing imaging facilities 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 web app

项目摘要

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.

摘要乳腺癌仍然是美国妇女癌症死亡的主要原因之一。尽管在乳腺癌预后和预测标志物的鉴定方面取得了进展，治疗乳腺X线检查报告是筛查和诊断乳腺癌的第一步。异常乳房X线检查结果，如肿块、异常钙化、结构扭曲和不对称密度可以导致癌症诊断。美国放射学会开发了乳腺成像报告和数据系统（BI-RADS）词典，用于标准化乳腺X线摄影报告，以便于活检决策的然而，BI-RADS词汇的应用导致了大量的观察者间的差异。变异性，包括术语使用不当和数据缺失。这种观察者的可变性部分导致了美国各地的活检率存在相当大的差异，大多数乳腺活检最终发现，良性病变。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。活检的最佳阈值。为了满足这一需求，我们建议开发一种智能增强的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，具有新型预测模型的数据集，该模型输出的癌症概率测量值在临床上更准确，相关性和信息量比六个离散的BI-RADS评分。使用乳房X线摄影或乳房超声BI- RADS报告签名和放射组学特征，一种更精确和临床上更有效的预测模型相关性可能被开发为针对充分表征和定义的特定活检患者亚组，而不是而不是广泛的异质性活检组。我们提出的技术需要一种新的策略，语言处理从大量的数据中提取与乳腺癌相关的临床风险因素。患者图表自动化，并将其与相应的图像组学数据和放射科医生整合在一起，生成的报告。我们将从大量的乳房X线摄影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的临床风险概况相结合以及其他患者特征，以生成风险评估评分，乳腺癌风险评估和活检决策。这样一个基于网络的应用工具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学数据。iBRISK 该工具将增强患者和临床医生之间的互动，以便就是否或而不是活检我们的假设是BI-RADS报告和成像指标包含乳腺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，以更好地评估乳腺癌风险。乳腺癌的新奇风险评估系统将采用一种新的预测模型，部署深度学习和人工智能，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腺活检和患者的焦虑。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.

DOI：
10.1148/ryai.220259
发表时间：
2023-11
期刊：
Radiology. Artificial intelligence
影响因子：
0
作者：
通讯作者：

A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients.

DOI：
10.1109/jbhi.2021.3139773
发表时间：
2022-07
期刊：
IEEE journal of biomedical and health informatics
影响因子：
7.7
作者：
通讯作者：

A comparative efficacy study of diagnostic digital breast tomosynthesis and digital mammography in BI-RADS 4 breast cancer diagnosis.

DOI：
10.1016/j.ejrad.2022.110361
发表时间：
2022-08
期刊：
EUROPEAN JOURNAL OF RADIOLOGY
影响因子：
3.3
作者：
Ezeana, Chika F.;Puppala, Mamta;Wang, Lin;Chang, Jenny C.;Wong, Stephen T. C.
通讯作者：
Wong, Stephen T. C.

Deep learning powers cancer diagnosis in digital pathology.

DOI：
10.1016/j.compmedimag.2020.101820
发表时间：
2021-03
期刊：
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
影响因子：
0
作者：
He Y;Zhao H;Wong STC
通讯作者：
Wong STC

An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.

DOI：
10.1016/j.compmedimag.2021.101894
发表时间：
2021-04
期刊：
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
影响因子：
0
作者：
He T;Fong JN;Moore LW;Ezeana CF;Victor D;Divatia M;Vasquez M;Ghobrial RM;Wong STC
通讯作者：
Wong STC