Predicting breast cancer with ultrasound and mammography
通过超声波和乳房X光检查预测乳腺癌
基本信息
- 批准号:6417326
- 负责人:
- 金额:$ 15.4万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2002
- 资助国家:美国
- 起止时间:2002-03-01 至 2004-02-29
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
DESCRIPTION (Provided by Applicant): The purpose of this study is to increase
the specificity of breast biopsy by building computer models which combine both
mammography and breast ultrasound (US) findings to identify probably benign
breast masses. In current clinical practice, breast US is used only to
distinguish between fluid-filled cysts vs. solid masses. The proposed
artificial neural network (ANN) model would go one step further and
quantitatively identify probably benign cases which may undergo short-term
follow-up in lieu of biopsy. The hypothesis is that by combining information
from both modalities, the model will be more robust and more accurate than
those based upon either modality alone, and be able to improve upon the
performance of the radiologists.
In preliminary studies, ANN models successfully identified probably benign
breast masses using just mammographic findings or just US findings.
The specific aims of the proposed study are to: 1. Prospectively collect data
for 300 cases of biopsy-proven breast lesions for which mammography and
ultrasound (US) data are both available. 2. Optimize artificial neural network
(ANN) models to identify probably lesions based on US findings only. 3. Develop
unified models to identify probably benign lesions using both US and
mammography findings. 4. Perform statistical analysis to evaluate contribution
of US findings to the diagnostic performances of radiologists and ANN models.
The immediate benefit of this proposal is a computer-based decision aid to
improve the specificity of breast biopsy and thus reduce the cost associated
with benign biopsies. This proposal has the potential to reduce significantly
the number of unnecessary breast biopsies and their associated cost, physical
pain, and emotional distress to the patient.
描述(由申请人提供):本研究的目的是增加
通过建立联合收割机和计算机模型,
乳房X光检查和乳房超声(US)检查结果,以确定可能是良性的
乳房肿块在目前的临床实践中,乳腺超声仅用于
区分充满液体的囊肿和实性肿块。拟议
人工神经网络(ANN)模型将更进一步,
定量识别可能进行短期治疗的可能良性病例
随访代替活检。假设是通过组合信息
从这两种模式,该模型将更强大,更准确,
那些只基于任何一种模式,并能够改善
放射科医生的表现。
在初步研究中,人工神经网络模型成功地识别出可能是良性的,
乳腺肿块,仅使用乳房X线检查结果或仅使用US结果。
建议研究的具体目标是:1。专业收集数据
对于300例活检证实的乳腺病变,
超声(US)数据都是可用的。2.优化人工神经网络
(ANN)仅基于US发现识别可能病变的模型。3.发展
统一的模型,以确定可能的良性病变,
乳房X光检查结果。4.进行统计分析以评估贡献
美国的发现,放射科医生和人工神经网络模型的诊断性能。
这项建议的直接好处是一个基于计算机的决策辅助,
提高乳腺活检的特异性,从而降低相关成本
良性活检该提案有可能大幅减少
不必要的乳房活检的数量及其相关费用,
疼痛和情绪困扰。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('JOSEPH Y LO', 18)}}的其他基金
Computer-Aided Triage of Body CT Scans with Deep Learning
利用深度学习对身体 CT 扫描进行计算机辅助分类
- 批准号:
10585553 - 财政年份:2023
- 资助金额:
$ 15.4万 - 项目类别:
Tomosynthesis for Improved Breast Cancer Detection
用于改进乳腺癌检测的断层合成
- 批准号:
7096059 - 财政年份:2006
- 资助金额:
$ 15.4万 - 项目类别:
Tomosynthesis for Improved Breast Cancer Detection
用于改进乳腺癌检测的断层合成
- 批准号:
7390660 - 财政年份:2006
- 资助金额:
$ 15.4万 - 项目类别:
Tomosynthesis for Improved Breast Cancer Detection
用于改进乳腺癌检测的断层合成
- 批准号:
7591041 - 财政年份:2006
- 资助金额:
$ 15.4万 - 项目类别:
Tomosynthesis for Improved Breast Cancer Detection
用于改进乳腺癌检测的断层合成
- 批准号:
7248669 - 财政年份:2006
- 资助金额:
$ 15.4万 - 项目类别:
Predicting breast cancer with ultrasound and mammography
通过超声波和乳房X光检查预测乳腺癌
- 批准号:
6620433 - 财政年份:2002
- 资助金额:
$ 15.4万 - 项目类别:
Improved diagnosis of breast microcalcification clusters
改进乳腺微钙化簇的诊断
- 批准号:
6515215 - 财政年份:2001
- 资助金额:
$ 15.4万 - 项目类别: