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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：
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
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/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

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