Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.
Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.
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DOI：
10.1016/j.acra.2013.08.020
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发表时间：
2013-12
影响因子：
4.8
通讯作者：
Gur, David
中科院分区：
文献类型：
--
作者：
Tan, Maxine;Zheng, Bin;Ramalingam, Pandiyarajan;Gur, David
关键词：
本研究的目的是探讨预测乳腺X线筛查阴性妇女近期乳腺癌发生风险的可行性。它基于统计学习模型，该模型结合了与双侧乳房X线摄影组织不对称性和其他临床因素相关的计算机化图像特征。回顾性收集了994名女性的阴性数字乳腺X线照片数据库。在接下来的连续筛查检查中（12至36个月后），283名妇女被诊断为癌症阳性，349名被召回进行额外的诊断检查，后来被证明是良性的，362名仍然是阴性的（未召回）。从183个特征的初始池中，我们应用顺序向前浮动选择特征选择方法来搜索有效特征。使用10个选定的特征，我们开发并训练了一个支持向量机（SVM）分类模型，以计算每个病例的癌症风险或概率得分。以受试者工作特征曲线下面积（AUC）和比值比（OR）作为两个性能评估指标。阳性和阴性/良性病例分类的AUC=0.725±0.018。随着模型生成的风险评分的增加，OR显示出风险增加的趋势（从1.00到12.34，在阳性和阴性/良性病例组之间）。OR的回归分析也表明斜率呈显著增加趋势（p=0.006）。这项研究表明，由一个新的SVM模型计算的风险评分，涉及双边乳房X线摄影特征不对称有可能帮助预测妇女患乳腺癌的近期风险。
The objective of this study is to investigate the feasibility of predicting near-term risk of breast cancer development in women after a negative mammography screening examination. It is based on a statistical learning model that combines computerized image features related to bilateral mammographic tissue asymmetry and other clinical factors. A database of negative digital mammograms acquired from 994 women was retrospectively collected. In the next sequential screening examination (12 to 36 months later), 283 women were diagnosed positive for cancer, 349 were recalled for additional diagnostic workups and later proven to be benign, and 362 remain negative (not-recalled). From an initial pool of 183 features, we applied a Sequential Forward Floating Selection feature selection method to search for effective features. Using 10 selected features, we developed and trained a support vector machine (SVM) classification model to compute a cancer risk or probability score for each case. The area under the receiver operating characteristic curve (AUC) and odds ratios (ORs) were used as the two performance assessment indices. The AUC=0.725±0.018 was obtained for positive and negative/benign case classification. The ORs showed an increasing risk trend with increasing model-generated risk scores (from 1.00 to 12.34, between positive and negative/benign case groups). Regression analysis of ORs also indicated a significant increase trend in slope (p=0.006). This study demonstrates that the risk scores computed by a new SVM model involving bilateral mammographic feature asymmetry have potential to assist the prediction of near-term risk of women for developing breast cancer.
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影响因子：
39.2
作者：
Nelson HD;Zakher B;Cantor A;Fu R;Griffin J;O'Meara ES;Buist DS;Kerlikowske K;van Ravesteyn NT;Trentham-Dietz A;Mandelblatt JS;Miglioretti DL
通讯作者：
Miglioretti DL
影响因子：
39.2
作者：
Hubbard RA;Kerlikowske K;Flowers CI;Yankaskas BC;Zhu W;Miglioretti DL
通讯作者：
Miglioretti DL
影响因子：
4.7
作者：
Boyd, N F;Martin, L J;Yaffe, M J
通讯作者：
Yaffe, M J
影响因子：
5
作者：
Fenton, JJ;Egger, J;Elmore, JG
通讯作者：
Elmore, JG
DOI：
10.1093/jnci/87.22.1681
发表时间：
1995-11-15
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
MADIGAN, MP;ZIEGLER, RG;HOOVER, RN
通讯作者：
HOOVER, RN