Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models

BACKGROUND: De novo metastasis of breast cancer is a complex clinical issue to be identified. This study was the first to construct artificial neural networks (ANN) and logistic regression (LR) models with comparison to find out important factors associated with occurrence of de novo metastasis in invasive breast cancer. METHODS: A total of 40,899 patients diagnosed with de novo metastatic breast cancer in 2010 from Surveillance, Epidemiology and End Results (SEER) Cancer database were enrolled. ANN models and LR models were constructed based on thirteen relevant factors by 10-fold cross-validation approach respectively. Evaluation indexes as well as processing time were compared. RESULTS: Overall area under ROC curve (AUC) value of ANN models was significantly higher than that of LR models (0.917±0.01 vs. 0.844±0.011, P<0.001). In ANN models, number of positive ipsilateral axillary lymph nodes, tumor size, lymph node ratio (LNR) and regional lymph nodes status were important associated factors. While under the same experiment environment, ANN models obviously took much more processing time than LR models did (14,400 vs. 15 minutes for 10-fold cross-validation). CONCLUSIONS: ANN models outperformed traditional LR models in identifying de novo metastasis of breast cancer. On the other hand, the much longer processing time of ANN models should also be considered.