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首页> 外文会议>International Workshop on Breast Imaging >Breast Cancer Risk Prediction in Chinese Women Based on Mammographic Texture and a Comprehensive Set of Epidemiologic Factors
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Breast Cancer Risk Prediction in Chinese Women Based on Mammographic Texture and a Comprehensive Set of Epidemiologic Factors

机译：基于乳腺X线摄影和一系列流行病学因素的中国女性乳腺癌风险预测

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Considering the rapid rise in breast cancer incidence in China and lack of calibrated breast cancer prediction models for the Chinese female population, developing a breast cancer model targeting the Chinese women is necessary. This study aimed at generating a breast cancer risk prediction model for Chinese women. A total of 1079 (85 images contralateral to a cancer and 994 cases without breast cancer) women were recruited from Fudan University Shanghai Cancer Centre. For each case, we collected sixteen demographic variables such as age, BMI, number of children, family history of breast cancer, and age at menarche. Moreover, the dense tissue was automatically segmented by AutoDensity. A set of quantitative features were extracted from the dense area. Using the 80th percentile of intensity values in the dense area, the segmented area was thresholded again and the second set of computer-extracted features was calculated. The features, i.e. the demographic variables, and texture features extracted from the mammographically dense areas of the image, have been fed into an ensemble of 250 decision trees, whose results were combined using RUSBoost. The classifier achieved an AUC of 0.88 (Cl: 0.84 - 0.91) for identifying high-risk images. Therefore, adopting such model might lead to the augmentation of discriminatory power of currently-used risk prediction models. However, it should be noted that the cancer cases were retrieved from the diagnostic environment (not screening) and further validation on a dataset from a screening set-up will be required.
机译：考虑到中国乳腺癌发病率的迅速上升以及缺乏针对中国女性人群的校正乳腺癌预测模型，有必要开发针对中国女性的乳腺癌模型。这项研究旨在为中国女性建立乳腺癌风险预测模型。从复旦大学上海癌症中心招募了1079名女性（85幅与癌症相对的图像和994例无乳腺癌的女性）。对于每种情况，我们收集了16个人口统计学变量，例如年龄，BMI，儿童数量，乳腺癌家族史和初潮年龄。而且，密集组织通过AutoDensity自动分割。从密集区域提取了一组定量特征。使用密集区域中强度值的第80个百分位数，对分割区域再次设置阈值，并计算出第二组计算机提取的特征。从图像的乳房X线密集区域提取的特征（即人口统计学变量和纹理特征）已馈入250个决策树的集合中，其结果使用RUSBoost进行了合并。分类器获得的AUC为0.88（Cl：0.84-0.91），可用于识别高风险图像。因此，采用这种模型可能会导致当前使用的风险预测模型的判别能力增强。但是，应该注意的是，癌症病例是从诊断环境中检索到的（不是筛查），因此需要从筛查设置中对数据集进行进一步的验证。

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