Predicting breast cancer 5-year survival using machine learning: A systematic review.
Predicting breast cancer 5-year survival using machine learning: A systematic review.
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DOI：
10.1371/journal.pone.0250370
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Peng X
中科院分区：
文献类型：
--
作者：
Li J;Zhou Z;Dong J;Fu Y;Li Y;Luan Z;Peng X
准确预测乳腺癌患者的存活率是癌症研究人员面临的主要问题。机器学习(ML)因能提供准确的预测结果而备受关注，但其建模方法和预测性能仍存在争议。这项系统性综述的目的是确定并批判性地评价当前关于ML在预测乳腺癌5年生存率方面的应用研究。根据Prisma指南，两名研究人员独立地检索了PubMed(包括MEDLINE)、Embase和Web of Science核心数据库，从最初到2020年11月30日。搜索词包括乳腺肿瘤、存活率、机器学习和特定的算法名称。纳入的研究涉及使用ML建立乳腺癌生存预测模型和模型性能，该模型可以用所述验证结果的值来衡量。被排除的研究中，建模过程解释不清楚，信息不完全。提取的信息包括文献信息、数据库信息、数据准备和建模过程信息、模型构建和性能评估信息以及候选预测者信息。符合纳入标准的31项研究被纳入，其中大部分在2013年后发表。最常用的ML方法是决策树(19篇，61.3%)、人工神经网络(18篇，58.1%)、支持向量机(16篇，51.6%)和集成学习(10篇，32.3%)。中位数样本量为37256名患者(范围为200至659820人)，中位预测值为16人(范围为3至625人)。29项研究的准确率从0.510到0.971不等。25篇研究的敏感度为0.037~1，24篇研究的特异度为0.008~0.993。20项研究的AUC值从0.500到0.972不等。6项研究的精确度在0.549~1之间。所有模型都经过了内部验证，只有一项经过了外部验证。总体而言，与传统的统计方法相比，最大似然模型的性能并不一定有任何提高，这一领域的研究仍然面临着与数据预处理步骤不足、样本特征选择差异过大以及验证相关问题有关的局限性。未来还需要进一步优化所提出的模型的性能，这需要更多的标准化和后续的验证。
Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information. Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated. Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.
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影响因子：
9.3
作者：
Escala-Garcia M;Morra A;Canisius S;Chang-Claude J;Kar S;Zheng W;Bojesen SE;Easton D;Pharoah PDP;Schmidt MK
通讯作者：
Schmidt MK
DOI：
10.1093/jamia/ocaa075
发表时间：
2020-07-01
影响因子：
6.4
作者：
Crowley, Ryan J.;Tan, Yuan Jin;Ioannidis, John P. A.
通讯作者：
Ioannidis, John P. A.
影响因子：
7.7
作者：
Blettner, M;Sauerbrei, W;Friedenreich, C
通讯作者：
Friedenreich, C
影响因子：
5.8
作者：
Cianfrocca, M;Goldstein, LJ
通讯作者：
Goldstein, LJ
影响因子：
2.4
作者：
Altman, Douglas G.
通讯作者：
Altman, Douglas G.