In consideration of budget and efficiency, we suggest OCT as the best storing method that not only preserves RNA quality during the freezing-thawing process well, but also ensures more secure and stable DNA and protein.

Background Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

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Background Little is known about how health insurance policies, particularly in developing countries, may influence breast cancer prognosis. We aimed to examine the association between individual health insurance plans and breast cancer-specific mortality among patients in China.Methods We included 7,436 women diagnosed with invasive breast cancer between January 1 st , 2009, and December 31 st , 2016, at West China Hospital, Sichuan University. The health insurance plan of each patient was classified as either urban or rural schemes and was also categorized as reimbursement rate (i.e., the covered/ total charge) below or above the median. Breast cancer-specific mortality was the primary outcome. Using Cox proportional hazards models, we calculated hazard ratios (HRs) for cancer-specific mortality, contrasting rates among patients with a rural insurance scheme or low reimbursement rate to that of those with an urban insurance scheme or high reimbursement rate, respectively.Results During the median follow-up of 3.1 years, we identified 326 deaths due to breast cancer. Compared with patients covered by urban insurance schemes, patients covered by rural insurance schemes had a 29% increased cancer-specific mortality (95% CI 0% to 65%, P=0.046) after adjusting for demographics, tumor characteristics, and treatment modes. Reimbursement rate below the median was associated with a 42% increased rate of cancer-specific mortality (95% CI 11% to 82%). Every 10% increase in the reimbursement rate is associated with a 7% (95% CI, 2% to 12%) reduction in cancer-specific mortality risk, particularly in patients covered by rural insurance schemes (26%, 95% CI 9% to 39%).Conclusions Our findings suggest that under-insured patients with breast cancer in China face increased breast cancer-specific mortality, which may provide fresh insights into the role of reimbursement rate in cancer health disparities.