JMIR Publications

ABSTRACT

Background:

Accurate prediction of survival is crucial for both physicians and women with breast cancer to enable clinical decision making on appropriate treatments. The currently available survival prediction tools were developed based on demographic and clinical data obtained from specific populations and may underestimate or overestimate the survival of women with breast cancer in China.

Objective:

To develop and validate a prognostic application to predict the overall survival of women with breast cancer in China.

Methods:

Nine-year (January, 2009-December, 2017) clinical data of women with breast cancer who received surgery and adjuvant therapy from two hospitals in Xiamen were collected and matched against the death data from Xiamen Center of Disease Control and Prevention. All samples were randomly divided (7:3 ratio) into a training set for model construction and a test set for model external validation. Multivariable Cox regression analysis was used to construct a survival prediction model. The model performance was evaluated by receiver operating characteristic (ROC) curve and Brier score. Finally, by running the survival prediction model in the app background management thread, the prognostic app, called I Can Predict (iCanPredict), was developed for women with breast cancer in China.

Results:

A total of 1,592 samples were included for data analysis. The training set comprised 1,114 individuals and the test set comprised 478 individuals. Age at diagnosis, clinical stage, molecular classification, operative type, axillary lymph node dissection, chemotherapy, and endocrine therapy were incorporated into the model, where age at diagnosis (HR=1.031, 95%CI: 1.011-1.051, p=.002), clinical stage (HR= 3.044, 95%CI: 2.347-3.928, p＜.001), and endocrine therapy (HR=0.592, 95%CI: 0.384-0.914, p=.018) significantly influenced the survival of women with breast cancer. The operative type and the other four variables were not significant (P>.05). The ROC curve of the training set showed that the model exhibited good discrimination for predicting 1-year (AUC: 0.802, 95%CI: .713-.892), 5-year (AUC: 0.813, 95%CI: .760-.865), and 10-year (AUC: 0.740, 95%CI: .672-.808) overall survival. The Brier scores at 1, 5, and 10 years post-diagnosis were 0.005, 0.055, and 0.103 in the training set, respectively, and were less than 0.25, indicating good predictive ability. The test set externally validated model discrimination and calibration. In the iCanPredict app, when physicians or women input women’s clinical information and their choice of surgery and adjuvant therapy, the app will present the corresponding 10-year survival prediction.

Conclusions:

This survival prediction model provided good model discrimination and calibration. ICanPredict is the first tool of its kind in China to provide survival predictions to women with breast cancer. ICanPredict will increase women’s awareness of the similar survival rate of different surgeries and the importance of adherence to endocrine therapy, ultimately helping women to make informed decisions regarding treatment for breast cancer.