School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019.
Acad Radiol. 2013 Dec;20(12):1542-50. doi: 10.1016/j.acra.2013.08.020.
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 proved 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 classification model to compute a cancer risk or probability score for each case. The area under the receiver operating characteristic curve and odds ratios (ORs) were used as the two performance assessment indices.
The area under the receiver operating characteristic curve = 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 = .006).
This study demonstrates that the risk scores computed by a new support vector machine model involving bilateral mammographic feature asymmetry have potential to assist the prediction of near-term risk of women for developing breast cancer.
本研究旨在探讨基于计算机双侧乳腺组织不对称性图像特征和其他临床因素的统计学习模型预测阴性乳腺 X 线筛查女性近期乳腺癌发病风险的可行性。
回顾性收集了 994 例阴性数字化乳腺 X 线片的数据库。在下一次连续筛查检查（12-36 个月后）中，283 例被诊断为癌症阳性，349 例因额外的诊断性检查而召回，最终证实为良性，362 例仍为阴性（未召回）。从最初的 183 个特征中，我们应用序贯前向浮动选择特征选择方法来寻找有效的特征。使用 10 个选定的特征，我们开发并训练了一个支持向量机分类模型，为每个病例计算癌症风险或概率评分。接收者操作特征曲线下的面积和比值比（OR）被用作两种性能评估指标。
阳性和阴性/良性病例分类的接收者操作特征曲线下的面积为 0.725 ± 0.018。OR 随着模型生成的风险评分的增加呈递增风险趋势（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 =.006）。
本研究表明，涉及双侧乳腺特征不对称的新支持向量机模型计算的风险评分具有辅助预测女性近期乳腺癌发病风险的潜力。
