Improved diagnosis of breast microcalcification clusters

改进乳腺微钙化簇的诊断

• 批准号: 6515215
• 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 2004-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6515215
• 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.

这项研究的目的是开发计算机模型来通过改善乳房X线照片中微钙化的诊断来提高乳房活检的特异性。良性病例可能会进行短期随访，以代替活检。为了实现这些目标，计算机模型将合并三个互补的诊断信息来源：放射科医生提取的乳房X线摄影发现，患者历史记录数据以及计算机提取的I特征，该特征来自应用于数字化乳房X线照片的局部直方直方图阈值。拟议研究的具体目的是：（1）优化预测模型，以基于放射科医生提取的发现和历史数据来识别可能的良性微钙化簇。 （2）根据本地直方图分析中的计算机提取特征设计新的预测模型。 （3）构建和评估一个统一的恶性预测因子，该预测指标结合了单独模型提供的不同信息。在初步研究中，ANN使用BI-RADS发现和历史数据作为模型的输入确定了群集微钙化的良性病例。此外，使用局部直方直方图阈值技术来分割微校准簇，而基于规则的系统则消除了通常是良性簇。该提案的直接好处是一种基于计算机的决策援助，用于诊断带有微钙化簇的乳房X线X射线诊断。这些案例占乳房活检的40％，可以说是放射科医生和计算机模型最困难的类别。因此，这些微钙化簇的诊断准确性的提高将产生重要而直接的影响。