Purpose: Recurrence of breast cancer leads to a high lifetime risk and a low 5 year survival rate. Researchers have used machine learning to predict the risk of recurrence in patients with breast cancer, but the predictive performance of machine learning remains controversial. Hence, this study aimed to explore the accuracy of machine learning in predicting breast cancer recurrence risk and aggregate predictive variables to provide guidance for the development of subsequent risk scoring systems. Methods: We searched Pubmed, EMBASE, Cochrane, and Web of Science. The risk of bias in the included studies was evaluated using prediction model risk of bias assessment tool (PROBAST). Meta-regression was adopted to explore whether there was a significant difference in the recurrence time by machine learning. Results: Thirty-four studies involving 67,560 subjects were included, among whom 8695 experienced breast cancer recurrence. The c-index of prediction models was 0.814 (95%CI 0.802-0.826) and 0.770 (95%CI 0.737-0.803) in the training and validation sets, respectively; the sensitivity and specificity were 0.69 (95% CI 0.64-0.74), 0.89 (95% CI 0.86-0.92) in the training, and 0.64 (95% CI 0.58-0.70), 0.88 (95% CI 0.82-0.92) in the validation, respectively. Age, histological grading, and lymph node status are the most commonly used variables in model construction. Attention should be paid to unhealthy lifestyles such as drinking, smoking and BMI as modeling variables. Risk prediction models based on machine learning have long-term monitoring value for breast cancer population, and subsequent studies should consider using large-sample and multi-center data to establish risk equations for verification. Conclusion: Machine learning may be used as a predictive tool for breast cancer recurrence. Currently, there is a lack of effective and universally applicable machine learning models in clinical practice. We expect to incorporate multi-center studies in the future and attempt to develop tools for predicting breast cancer recurrence risk, so as to effectively identify populations at high risk of recurrence and develop personalized follow-up strategies and prognostic interventions to reduce the risk of recurrence.

Background: Acupoint injection has currently received increasing attention as a treatment for primary osteoporosis (POP), This study aimed to evaluate the efficacy and safety of acupoint injection as a clinical treatment for POP. Methods: Randomized controlled trials (RCTs) of acupoint injection compared with conventional non-acupoint injection for POP were identified in searches of seven databases from their inception to March 2019. All data were assessed and extracted by two authors independently. The risk of bias assessment recommended by the Cochrane Collaboration was used to assess the quality of the selected studies. RevMan 5.3 was used to conduct meta-analysis for the efficacy and safety of acupoint injection. Results: Five trials with 337 patients (aged 45–86 years) with bone mineral density (BMD) ≤2 SD were included in our meta-analysis. The results showed that, compared with conventional intramuscular injection, acupoint injection significantly increased the BMD [mean difference (MD) =0.02; 95% CI, 0.01 to 0.03, P0.05). Individuals results showed that acupoint injection improved biochemical indicators, such as NBAP, IGF-I and reduced CTX and leptin levels. While the risk of bias was high in all five trials. Conclusions: This meta-analysis and systematic review suggests that acupoint injection improves BMD and some biochemical indicators in POP patients compared with the effects of conventional intramuscular injection. However, due to the high risk of bias in all the trials reviewed, the evidence remains inconclusive and future research will be required with improved methodological quality.