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Quantitative diagnosis of breast cancer with ultrasound

乳腺癌的超声定量诊断

基本信息

批准号：
8387042
负责人：
CHANDRA M SEHGAL
金额：
$ 29.8万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-12-01 至 2014-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8387042
关键词：
Algorithms Area Benign Biological Neural Networks Biopsy Breast CH3OCF2CH(CF3)OCH2F Cancer Etiology Cessation of life Characteristics Classification Classification Scheme Clinical Computer Analysis Computers Core Biopsy Cyst Data Decision Trees Detection Diagnosis Diagnostic Evaluation Excision biopsy Goals Health Image Image Analysis Individual Intraobserver Variability Lesion Logistic Regressions Malignant - descriptor Malignant Neoplasms Mammography Measurement Medical Medicine Methods Pathologic Patients Pattern Performance Phase Predictive Value Probability Process Radial Receiver Operating Characteristics Recommendation Reproducibility Research Scanning Second Opinions Shapes Solid System Techniques Testing Time Tissues Training Ultrasonography Visual base breast cancer diagnosis breast scan cancer diagnosis clinical application clinical practice computer design design diagnostic accuracy elastography experience imaging modality improved malignant breast neoplasm novel novel diagnostics novel strategies programs radiologist research study success

项目摘要

DESCRIPTION (provided by applicant): The goal of this study is to design quantitative methods that clinicians can use to supplement their visual interpretation of sonograms for differentiating benign and malignant solid breast masses. The hypothesis is that combining quantitative methods with clinicians' assessment of images will improve the accuracy of diagnosis and reduce the number of false positive or unnecessary biopsies. Our preliminary study shows that certain sonographic features derived from lesion margin, shape, and echo characteristics can help differentiate benign and malignant solid masses. In this application, we propose to build on our initial success and develop a diagnostic system on an ultrasound scanner that provides the end user with online estimates of probability of malignancy from quantitative analysis of the breast ultrasound images. The program has four specific aims. In Specific Aim 1, ultrasound images of breast masses from 400 patients will be acquired under controlled and well- defined experimental conditions. In Specific Aim 2, new approaches will be developed to detect mass margins and to describe these features quantitatively. The qualitative features of the masses that clinicians use in routine diagnosis will also be identified. The quantitative and the qualitative feature sets will be used individually with novel classification methods based on logistic regression, neural networks and radial basis function classifiers to formulate a decision tree for cancer diagnosis. The diagnostic performance of each classification scheme and feature set will be evaluated by ROC analysis. In Specific Aim 3, the qualitative and the quantitative feature sets will be combined, integrating the intuitive medical experience of the clinicians with the precision of quantitative measurements. In the final phase of the program, Specific Aim 4, the best performing feature set and classification scheme will be implemented on an ultrasound scanner for online diagnosis of malignant and benign breast masses. This program integrates qualitative clinical and quantitative computer approaches for breast cancer diagnosis. We expect to develop a new diagnostic system that determines probability of malignancy, which clinicians could use as an online second opinion when making diagnostic decisions during the performance of a breast ultrasound examination.

描述（由申请人提供）：本研究的目的是设计定量方法，临床医生可以使用它来补充超声图像的视觉解释，以区分良性和恶性乳腺实体肿块。假设将定量方法与临床医生对图像的评估相结合，将提高诊断的准确性，减少假阳性或不必要的活检次数。我们的初步研究表明，从病变边缘、形状和回声特征得出的某些超声特征可以帮助区分良恶性实体肿块。在这个应用中，我们建议在我们最初成功的基础上，开发一个超声扫描仪诊断系统，通过对乳房超声图像的定量分析，为最终用户提供恶性肿瘤概率的在线估计。该计划有四个具体目标。在具体目标1中，将在控制和明确的实验条件下获得来自400名患者的乳腺肿块的超声图像。在具体目标2中，将开发新的方法来检测质量边缘并定量描述这些特征。临床医生在常规诊断中使用的肿块的定性特征也将被确定。定量和定性特征集将分别与基于逻辑回归、神经网络和径向基函数分类器的新型分类方法一起使用，以制定癌症诊断的决策树。每个分类方案和特征集的诊断性能将通过ROC分析进行评估。在具体目标3中，定性和定量特征集将相结合，将临床医生的直观医疗经验与定量测量的精度相结合。在项目的最后阶段，Specific Aim 4，将在超声扫描仪上实施表现最好的特征集和分类方案，用于在线诊断乳腺恶性和良性肿块。该项目整合了定性临床和定量计算机方法用于乳腺癌诊断。我们期望开发一种新的诊断系统来确定恶性肿瘤的可能性，临床医生在进行乳房超声检查时可以将其作为在线第二意见来做出诊断决定。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Machine learning to improve breast cancer diagnosis by multimodal ultrasound.

机器学习通过多模态超声改善乳腺癌诊断。

DOI：
10.1109/ultsym.2018.8579953
发表时间：
2018
期刊：
IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium
影响因子：
0
作者：
Sultan,LaithR;Schultz,SusanM;Cary,TheodoreW;Sehgal,ChandraM
通讯作者：
Sehgal,ChandraM

Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer.