AUTHOR=Li Chaofan , Liu Mengjie , Li Jia , Wang Weiwei , Feng Cong , Cai Yifan , Wu Fei , Zhao Xixi , Du Chong , Zhang Yinbin , Wang Yusheng , Zhang Shuqun , Qu Jingkun 

TITLE=Machine learning predicts the prognosis of breast cancer patients with initial bone metastases

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1003976

DOI=10.3389/fpubh.2022.1003976

ISSN=2296-2565

ABSTRACT=Background: Bone is the most common metastatic site of advanced breast cancer patients and the survival time is their primary concern, however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still controversial. Method: The data used for analysis in this study was obtained from the SEER database (2010-2019). We made COX regression analysis to identify prognostic factors of bone metastatic breast cancer (BMBC) patients. Through cross-validation, we constructed an XGBoost model to predicting survival in patients with BMBC. We also investigated the prognosis of patients treated with neoadjuvant chemotherapy plus surgery and chemotherapy alone using propensity score matching and KM survival analysis. Results: Our validation results showed that the model has high sensitivity, specificity and correctness, and it is the most accurate one to predict the survival of BMBC patients (1-year AUC=0.818, 3-year AUC=0.798 and 5-year survival AUC=0.791). The sensitivity of 1-year model was higher (0.79), while the specificity of 5-year model was higher (0.86). Interestingly, we found that if the time from diagnosis to therapy was ≥1 month, BMBC patients had even better survival than those who started treatment immediately (HR=0.920, 95%CI 0.869-0.974, P<0.01). The BMBC patients with income more than USD$70,000 had better OS (HR=0.814, 95%CI 0.745-0.890, P<0.001) and BCSS (HR=0.808 95%CI 0.735-0.889, P<0.001) than who with income less than USD$50,000. We also found compared with chemotherapy alone, neoadjuvant chemotherapy plus surgical treatment significantly improved OS and BCSS in all molecular subtypes of BMBC patients, while only the patients with bone metastases only, bone and liver metastases, bone and lung metastases could benefit from neoadjuvant chemotherapy plus surgical treatment. Conclusion: We constructed an AI model to provide a quantitative method to predict survival of BMBC patients, and our validation results indicate that this model should be highly reproducible in similar patient population. We also identified potential prognostic factors for BMBC patients and suggested primary surgery followed by neoadjuvant chemotherapy might increase survival in selected subgroup of patients.