Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.
Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.
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DOI：
10.1088/1361-6560/aaa1ca
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发表时间：
2018-01-30
影响因子：
3.5
通讯作者：
Zheng B
中科院分区：
文献类型：
--
作者：
Heidari M;Khuzani AZ;Hollingsworth AB;Danala G;Mirniaharikandehei S;Qiu Y;Liu H;Zheng B
为了自动识别一组有效的乳房x线影像特征，构建最优的乳腺癌风险分层模型，本研究旨在探讨应用嵌入局部保留投影（LPP）特征组合与再生算法的机器学习方法预测短期乳腺癌风险的优势。收集了来自500名妇女的阴性乳房x光片数据集。该数据集被分为两个年龄匹配的类别，其中250例高风险病例在随后的乳房x光检查中检测到癌症，250例低风险病例仍为阴性。首先，采用计算机辅助图像处理方案对乳房x光片上描绘的纤维腺组织进行分割，初步计算出44个与左右双侧乳房x光片组织密度分布不对称相关的特征。接下来，建立了基于多特征融合的机器学习分类器来预测下一次乳房x光检查中发现癌症的风险。采用留一case-out （LOCO）交叉验证方法对嵌入LLP算法的机器学习分类器进行训练和测试，该算法在每个LOCO过程中使用最大方差法生成具有4个特征的新操作向量。结果显示，当使用这种嵌入lpp的机器学习方法时，风险预测准确率提高了9.7%。校正后的优势比也有增加的趋势，优势比从1.0增加到11.2。本研究表明，应用LPP算法有效地降低了特征维数，并在预测短期乳腺癌风险方面产生了更高且可能更稳健的性能。
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
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影响因子：
10.6
作者：
Tan M;Zheng B;Leader JK;Gur D
通讯作者：
Gur D
DOI：
10.1146/annurev-bioeng-071516-044442
发表时间：
2017-06-21
影响因子：
9.7
作者：
Shen D;Wu G;Suk HI
通讯作者：
Suk HI
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：
8.8
作者：
Leng, Yan;Xu, Xinyan;Qi, Guanghui
通讯作者：
Qi, Guanghui
影响因子：
39.2
作者：
Carney, PA;Miglioretti, DL;Ballard-Barbash, R
通讯作者：
Ballard-Barbash, R