Improved diagnosis of breast microcalcification clusters

改进乳腺微钙化簇的诊断

• 批准号: 6368820
• 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: 2001
• 资助国家: 美国
• 起止时间: 2001-07-01 至 2003-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6368820
• 关键词: biopsy; breast neoplasms; calcification; computer assisted diagnosis; computer simulation; computer system design /evaluation; diagnosis design /evaluation; human data; mammography; model design /development

The purpose of this study is to develop computer models to increase the specificity of breast biopsy by improving the diagnosis of microcalcifications in mammograms. Probably benign cases may undergo short-term follow-up in lieu of biopsy. To accomplish these goals, computer models will merge together three complementary sources of diagnostic information: radiologist-extracted mammographic findings, patient history data, and computer-extracted I features from local histogram thresholding applied to digitized mammograms. The specific aims of the proposed study are to: (1) Optimize predictive models to identify probably benign microcalcification clusters based upon radiologist-extracted findings and history data. (2) Design new predictive models based upon computer-extracted features from local histogram analysis. (3) Construct and evaluate a unified malignancy predictor which combines the different information provided by the separate models. In preliminary studies, an ANN identified probably benign cases of clustered microcalcifications using BI-RADS findings and history data as inputs to the model. In addition, a local histogram thresholding technique was used to segment microcalcification clusters, and a rule- based system eliminated typically benign clusters. The immediate benefit of this proposal is a computer-based decision aid for the diagnosis of mammographically suspicious lesions with microcalcification clusters. These cases account for 40% of breast biopsies and are arguably the most difficult category to characterize for radiologists and computer models alike. Improvements in diagnostic accuracy for these cases of microcalcification clusters will thus have an important and immediate impact.

本研究的目的是开发计算机模型，通过提高乳腺X线照片中微钙化的诊断来提高乳腺活检的特异性。可能是良性的病例可以进行短期随访，以代替活检。为了实现这些目标，计算机模型将合并在一起的诊断信息的三个互补的来源：放射科医生提取的乳腺X线检查结果，患者的历史数据，和计算机提取的I功能从本地直方图阈值应用到数字化的乳腺X线照片。本研究的具体目的是：（1）优化预测模型，以根据放射科医生提取的发现和历史数据识别可能是良性的微钙化簇。(2)根据计算机从局部直方图分析中提取的特征设计新的预测模型。(3)构建并评估一个统一的恶性肿瘤预测因子，该预测因子结合了不同模型提供的不同信息。在初步研究中，人工神经网络识别可能是良性的情况下，集群微钙化使用BI-RADS的结果和历史数据作为输入到模型。此外，局部直方图阈值技术用于分割微钙化簇，基于规则的系统消除典型的良性簇。这一建议的直接好处是一个基于计算机的决策辅助诊断乳腺摄影可疑病变与微钙化簇。这些病例占乳腺活检的40%，可以说是放射科医生和计算机模型最难描述的类别。因此，提高这些微钙化簇病例的诊断准确性将产生重要而直接的影响。