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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts
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Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

机译：基于面向梯度直方图的全球实质纹理特征改善了健康乳房的癌症开发风险估算

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Background: The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage.Methods: A case/control study nested within a screening program covering 1563 women with craniocau-dal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valen-ciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients.
机译：背景：数字乳房X线照片上的乳腺密集组织百分比是乳腺癌风险估算中最常用的标记之一。乳房致密组织的几何特征以及扫描乳房X线照片的滑动窗口中包含的纹理结构可以提高预测能力，当与乳房密度组织百分比组合时。方法：嵌套在筛选方案中嵌套1563的情况/对照研究患有Craniocau-DAL和Mediolate-Oblique乳房X线照片（755个对照和癌症诊断前的对侧乳房乳房X线照片的癌症诊断前808例），从Comunitat Valen-Ciana（西班牙）衰减45至70岁，用于提取几何和纹理特征。使用DMSCan进行致密组织分割，并通过两位经验丰富的放射科医师进行验证。基于随机森林的模型训练了几次改变这组变量。通过10分层折叠交叉验证方案评估1172名患者的训练数据集。接收器操作特征曲线（AUC）下的该区域是预测能力的度量。通过仅考虑将模型应用于试验组后的输出来评估结果，该结果由剩余的391名患者组成。

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