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(申请人提供)：本研究的目的是增加 通过建立两者相结合的计算机模型来研究乳腺活检的特异性 乳房X光检查和乳房超声(US)发现以确定可能是良性的 乳房肿块。在目前的临床实践中，乳房超声仅用于 区分充满液体的囊性肿块和实性肿块。建议数 人工神经网络(ANN)模型将更进一步，并 定量确定可能接受短期治疗的可能的良性病例 以随访代替活组织检查。假设是通过将信息组合在一起 从这两种模式来看，该模型将比 仅基于任何一种模式，并能够改进 放射科医生的表现。 在初步研究中，人工神经网络模型成功地识别出可能是良性的 仅使用乳房X光检查结果或仅使用美国发现的乳房肿块。 拟议研究的具体目的是：1.前瞻性地收集数据 对于300例经活检证实的乳腺病变，进行了乳房X光检查和 超声(美国)数据均可获得。2.优化人工神经网络 (ANN)仅根据美国的发现识别可能的病变的模型。3.发展 统一的模型来识别可能的良性病变，使用US和 乳房X光检查结果。4.进行统计分析，评估贡献 美国的发现对放射科医生和人工神经网络模型的诊断性能有很大的影响。 这项建议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 做良性活组织检查。这项提议有可能大幅减少 不必要的乳房活组织检查的数量及其相关的费用、体检 疼痛，以及给病人带来的情绪困扰。