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.

Aim This research aimed to explore the level of hope and symptom burden of breast cancer women undergoing chemotherapy, and predictive factors of hope were also investigated. Background Chemotherapy brings physical and psychological stress to breast cancer patients. As an effective coping strategy, hope gives them the courage to overcome difficulties and improve prognosis and survival. Therefore, efforts are needed to raise hope. Design/Methods A total of 450 women who were undergoing breast cancer chemotherapy participated in this cross‐sectional study. Sociodemographic data, disease characteristics, and measures of hope and symptom burden were collected using questionnaires. Hope was assessed using the validated Herth Hope Index, and the previously validated Memorial Symptom Assessment Scale was used to assess symptom burden. This paper adhered to the STROBE guidelines. Results Chinese breast cancer chemotherapy women hope average scores of 30.15 ± 4.82 were in the medium range of the Hearth Hope Index as specified by Herth to be 24–35. Patients with age ≤45, religious beliefs and lighter symptom burden have a higher level of hope. These variables explained a total of 22.9% of the variation in hope. Conclusions The level of hope for women undergoing breast cancer chemotherapy still needs to be further improved. Symptom burden can negatively predict hope. Relevance to clinical practice If nurses can decrease breast cancer chemotherapy women symptom burden, there is an impact on increasing levels of hope.

Background Functional exercise is crucial for breast cancer patients after surgery, and the use of virtual reality technology to assist patients with postoperative upper limb functional rehabilitation has gradually attracted the attention of researchers. However, the usability of the developed rehabilitation system is still unknown to a large extent. The purpose of this study was to develop a virtual reality upper limb rehabilitation system for patients after breast cancer surgery and to explore its usability. Methods We built a multidisciplinary team based on virtual reality and human-computer interaction technology and designed and developed an upper limb function rehabilitation system for breast cancer patients after surgery. Breast cancer patients were recruited from a grade III-a general hospital in Changchun city for the experiment. We used the System Usability Scale to evaluate the system availability, the Presence Questionnaire scale to measure the immersive virtual reality scene, and the Simulator Sickness Questionnaire subjective measurement scale for simulator sickness symptoms. Results This upper limb rehabilitation system hardware consisted of Head-mounted Display, a control handle and notebook computers. The software consisted of rehabilitation exercises and game modules. A total of 15 patients were tested on this system, all of whom were female. The mean age was 54.73±7.78 years, and no patients were excluded from the experiment because of adverse reactions such as dizziness and vomiting. The System Usability Scale score was 90.50±5.69, the Presence Questionnaire score was 113.40±9.58, the Simulator Sickness Questionnaire-nausea score was 0.93±1.16, the Simulator Sickness Questionnaire-oculomotor score was 0.80±1.27, the Simulator Sickness Questionnaire-disorientation score was 0.80±1.27, and the Simulator Sickness Questionnaire total score was 2.53±3.40. Conclusions This study fills in the blanks regarding the upper limb rehabilitation of breast cancer patients based on virtual reality technology system usability research. As the starting point of research in the future, we will improve the system’s function and design strictly randomized controlled trials, using larger samples in the promotion, to evaluate its application in breast cancer patients with upper limbs and other physiological functions and the feasibility and effects of rehabilitation.