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
• 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 2005-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6620433
• 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 通过建立结合两者的计算机模型来确定乳腺活检的特异性 乳房X光检查和乳房超声（美国）检查结果可识别可能是良性的 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。拟议的 人工神经网络（ANN）模型将更进一步， 定量识别可能经历短期的良性病例 随访代替活检。假设通过组合信息 从这两种模式来看，该模型将比 那些仅基于任一模式的人，并且能够改进 放射科医生的表现。 在初步研究中，人工神经网络模型成功识别出可能良性的 仅使用乳房 X 光检查结果或仅使用超声检查结果来确定乳房肿块。 本研究的具体目的是： 1. 前瞻性收集数据 针对 300 例经活检证实的乳腺病变，进行乳房 X 光检查和 超声（美国）数据均可用。 2. 优化人工神经网络 （ANN）模型仅根据美国的研究结果来识别可能的病变。 3. 开发 使用 US 和 统一模型来识别可能的良性病变 乳房X线检查结果。 4. 进行统计分析以评估贡献 美国研究结果对放射科医生和 ANN 模型诊断性能的影响。 该提案的直接好处是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、物理 给患者带来痛苦和情绪困扰。