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首页> 外文期刊>Journal of Digital Imaging >Characterizing the Clustered Microcalcifications on Mammograms to Predict the Pathological Classification and Grading: A Mathematical Modeling Approach
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Characterizing the Clustered Microcalcifications on Mammograms to Predict the Pathological Classification and Grading: A Mathematical Modeling Approach

机译：表征乳房X光照片上的聚类微钙化以预测病理学分类和分级：一种数学建模方法

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In this study, we explore a mathematical model to characterize the clustered microcalcifications on mammograms for predicting the pathological classification and grading. Our database consists of both retrospective cases (78 cases) and prospective cases (31 cases) with pathologically diagnosed clusters of microcalcifications on mammograms. The microcalcifications were divided into four grades: grade 0, benign breast disease including mastopathies (n = 12) and fibroadenomas (n = 20); grade 1, well-differentiated infiltrating ductal carcinoma (n = 12); grade 2, moderately differentiated infiltrating ductal carcinoma (n = 38); grade 3, poorly differentiated infiltrating ductal carcinoma (n = 27). A feature parameter, defined as the pattern form factor of microcalcification cluster θ by us, combines five computer-extracted image parameters of microcalcification clusters of those mammograms. In every case, only one imaging was selected for modeling analysis. A total of 109 imagings were adopted in current study. We find the existence of a positive relationship between the feature parameter θ and pathological grading G of microcalcifications in retrospective cases, which was expressed as G = 6.438 + 1.186 × Ln . The model above has been verified further by the prospective study with a comparative evaluation accuracy of approximately 77.42%. The binary predication simply for both benignancy and malignancy was also included using same but reshuffled data, and the receiver operating characteristic (ROC) analysis was performed with ROC value 0.74351∼0.79891. As one candidate for feature parameter in computer-aided diagnosis, the pattern form factor θ of clustered microcalcifications may be useful to predict the pathological grading and classification of microcalcification clusters on mammography in breast cancer.
机译：在这项研究中，我们探索数学模型来表征乳房X线照片上的簇状微钙化，以预测病理学分类和分级。我们的数据库包括回顾性病例（78例）和预期病例（31例），这些病例在乳腺X线照片上经病理诊断为微钙化簇。微钙化分为四个等级：0级，包括乳腺病的良性乳腺疾病（n = 12）和纤维腺瘤（n = 20）； 1级，高分化浸润性导管癌（n = 12）； 2级，中度浸润性导管癌（n = 38）； 3级，低分化浸润性导管癌（n = 27）。我们将特征参数定义为微钙化簇θ的图案形状因子，该特征参数结合了这些X线钼靶微钙化簇的计算机提取的五个图像参数。在每种情况下，仅选择一个成像进行建模分析。本研究共采用了109幅影像。在回顾性病例中，我们发现特征参数θ与微钙化的病理分级G之间存在正相关关系，表示为G = 6.438 + 1.186×Ln。上述模型已通过前瞻性研究进一步验证，比较评估的准确性约为77.42％。使用相同但经过改组的数据还包括仅针对良性和恶性肿瘤的二进制预测，并且以ROC值为0.74351至0.79891进行接收器工作特性（ROC）分析。作为计算机辅助诊断中特征参数的一个候选者，成簇的微钙化的图案形状因子θ可能有助于预测乳腺钼靶上微钙化聚类的病理分级和分类。

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