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: 6417326
• 负责人: JOSEPH Y LO
• 金额: $ 15.4万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/6417326
• 关键词: artificial intelligence; breast neoplasm /cancer diagnosis; breast neoplasms; computer assisted diagnosis; computer system design /evaluation; diagnosis design /evaluation; female; human data; mammography; mathematical model; model design /development; ultrasonography; women's health

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。