Improved diagnosis of breast microcalcification clusters

这项研究的目的是开发计算机模型，通过提高乳房X线片中微钙化的诊断来提高乳房活检的特异性。良性病例可能会接受短期的随访，而不是活检。为了实现这些目标，计算机模型将把诊断信息的三个互补来源合并在一起：放射科医生提取的乳房X光检查结果、患者历史数据，以及应用于数字化乳房X光检查的计算机从局部直方图阈值提取的I特征。这项拟议研究的具体目标是：(1)优化预测模型，以基于放射科医生提取的结果和历史数据识别可能是良性的微钙化团。(2)基于计算机提取的局部直方图特征，设计新的预测模型。(3)构建和评价一个统一的恶性肿瘤预测指标，该指标综合了不同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，识别出可能是良性的聚集性微钙化病例。此外，使用局部直方图阈值技术来分割微钙化簇，并使用基于规则的系统来剔除典型的良性簇。这一建议的直接好处是为诊断具有微钙化簇的乳房X光检查可疑病变提供了一种基于计算机的决策辅助。这些病例占乳房活检的40%，对于放射科医生和计算机模型来说，可以说是最难描述的类别。因此，提高对这些微钙化团病例的诊断准确率将产生重要和直接的影响。

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.