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Background 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. Methods 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. Results 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. Conclusions 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.

Objective The sphingolipid de novo synthesis pathway is considered a promising target for pharmacological intervention in atherosclerosis. However, its potential is hampered by the fact that the substance’s atherogenic mechanism is not completely understood. To unravel the complex mechanisms, we utilized the sphingomyelin synthase 2 (Sms2) gene knockout approach to test our hypothesis that selectively decreasing plasma lipoprotein SM, can play an important role in preventing atherosclerosis. Methods and Results We prepared Sms2 and Apoe double knockout (KO) mice. They showed a significant decrease in plasma lipoprotein SM levels (35%, P<0.01) and a significant increase in ceramide and dihydroceramide levels (87.5 and 27%, P<0.01, respectively), but no significant changes in other tested sphingolipids, cholesterol, and triglyceride. Non-HDL lipoproteins from the double KO mice showed a reduction of SM but not cholesterol and displayed a less tendency toward aortic sphingomyelinase-mediated lipoprotein aggregation in vitro and retention in aortas in vivo, compared to controls. More important, at age 19 weeks, Sms2 KO/Apoe KO mice showed a significant reduction in atherosclerotic lesions of the aortic arch and root (52%, P<0.01), compared to controls. We also found that the Sms2 KO/Apoe KO brachiocephalic artery (BCA) contained significantly less SM, ceramide, free cholesterol, and cholesteryl ester (35, 32, 58, and 60%, P<0.01, respectively), than that of Apoe KO BCA. Conclusions Decreasing plasma SM levels through decreasing SMS2 activity could become a promising treatment for atherosclerosis.