IMAGING NETWORK FOR BREAST CANCER MASS SCREENING
IMAGING NETWORK FOR BREAST CANCER MASS SCREENING
批准号:
6829993
负责人:
Armen Sarvazyan
金额:
$52.72万
依托单位国家:
美国
项目类别:
财政年份:
2001
资助国家:
美国
项目状态:
已结题
起止时间:
2001-08-01 至 2007-08-31
关键词:
bioimaging /biomedical imagingbiomedical equipment developmentbreast neoplasm /cancer diagnosisbreast self examinationclinical biomedical equipmentclinical researchcomputer assisted diagnosiscomputer networkcomputer program /softwarecomputer system design /evaluationdata managementearly diagnosiselasticityelectrical measurementfemalehuman datahuman subjectimage processingmass screeningpatient oriented researchtouchwomen&aposs health
中文摘要
描述(由申请人提供):
英文摘要
DESCRIPTION (provided by applicant):
Early detection of breast cancer remains one of the most significant challenges in health care. The tactile breast imager (TBI) is a promising solution that electronically captures the sense of touch to provide a quantitative, sensitive, and permanent record of palpatory breast examination. Its inherent low cost, ease of use, and promising test results to date, give it great potential as an effective mass pre-screening tool for breast cancer detection.
In Phase I, we successfully developed and tested a prototype device that showed sensitivity well beyond the manual palpation limit in a variety of test conditions. The device was tested on physical tissue models and demonstrated to robustly detect nodules of various sizes and subcutaneous depths. Advanced data processing algorithms were developed providing vivid 3-D images of the underlying mechanical tissue structure.
In Phase II we will perform an initial clinical study to acquire raw image data and correlated lesion mechanical property data for use in refining the image processing and feature identification capabilities. We will concurrently build an advanced TBI prototype with substantially improved sensor performance. Clinical studies will be performed involving concurrent mammography / TBI imaging with qualitative comparative analysis between the modalities. We will conduct clinical development studies and build physical breast models to improve the data collection and lesion discrimination performance. The TBI will then be tested in a blinded clinical study to obtain statistically relevant measures of lesion sensitivity, specificity, and mechanical image quality. Appropriate regulatory testing will be conducted in order to prepare necessary engineering documentation for a FDA application.
期刊论文(0)
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科研奖励(0)
会议论文
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