Abstract
BackgroundCurrent online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions.ObjectiveWe aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China.MethodsThis study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT.ResultsThe training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values.ConclusionsOur prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.
Highlights
Breast cancer is a heterogeneous disease with different prognoses
Patients records were derived from the Breast Cancer Information Management System (BCIMS) at the West China Hospital of Sichuan University [15]; the cases derived from the BCIMS are representative of breast cancer cases in Southwest China [16]
Principal Findings Leveraging the real-world data of 5293 women with primary invasive early breast cancer, we developed a prognostic model to estimate the individual risk of disease progression, cancer-specific mortality, and all-cause mortality using machine learning
Summary
Several prognostic prediction models have recently been developed to assist clinical decision making in breast cancer treatment [2]. These models focused on clinical and pathological factors, as well as gene expression (Oncotype, MammaPrint, BCI, and EndoPredict) [3,4,5,6,7,8]. Online and PREDICT, are based on specific populations They have been well validated and widely used in the United States and Western Europe; several validation attempts in non-European countries have revealed suboptimal predictions
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