[ Special series on Breast Cancer ]
doi: 10.1186/s40880-017-0192-9
Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents
Su-Hsin Huang, Joon-Khim Loh, Jinn-Tsong Tsai, Ming-Feng Houg and Hon-Yi Shi
Department of Nursing, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung Medical University
[Abstract]
Background Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model, multiple logistic regression (MLR) model, and Cox regression model.
Methods This study compared the MLR, Cox, and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010. An estimation dataset was used to train the model, and a validation dataset was used to evaluate model performance. The sensitivity analysis was also used to assess the relative significance of input variables in the prediction model.
Results The ANN model significantly outperformed the MLR and Cox models in predicting 5-year mortality, with higher overall performance indices. The results indicated that the 5-year postoperative mortality of breast cancer patients was significantly associated with age, Charlson comorbidity index (CCI), chemotherapy, radiotherapy, hormone therapy, and breast cancer surgery volumes of hospital and surgeon (all P < 0.05). Breast cancer surgery volume of surgeon was the most influential (sensitive) variable affecting 5-year mortality, followed by breast cancer surgery volume of hospital, age, and CCI.
Conclusions Compared with the conventional MLR and Cox models, the ANN model was more accurate in predicting 5-year mortality of breast cancer patients who underwent surgery. The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.
Background Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model, multiple logistic regression (MLR) model, and Cox regression model.
Methods This study compared the MLR, Cox, and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010. An estimation dataset was used to train the model, and a validation dataset was used to evaluate model performance. The sensitivity analysis was also used to assess the relative significance of input variables in the prediction model.
Results The ANN model significantly outperformed the MLR and Cox models in predicting 5-year mortality, with higher overall performance indices. The results indicated that the 5-year postoperative mortality of breast cancer patients was significantly associated with age, Charlson comorbidity index (CCI), chemotherapy, radiotherapy, hormone therapy, and breast cancer surgery volumes of hospital and surgeon (all P < 0.05). Breast cancer surgery volume of surgeon was the most influential (sensitive) variable affecting 5-year mortality, followed by breast cancer surgery volume of hospital, age, and CCI.
Conclusions Compared with the conventional MLR and Cox models, the ANN model was more accurate in predicting 5-year mortality of breast cancer patients who underwent surgery. The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.
Chinese Journal of Cancer 2017, Volume: 36, Issue 4
[ PDF Full-text ]
[ Html full-text / Citation export] (BioMed Central)
[Google Scholar]
[ More articles of the special series on Breast Cancer ]
Cite this article
Su-Hsin Huang, Joon-Khim Loh, Jinn-Tsong Tsai, Ming-Feng Houg and Hon-Yi Shi. Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents. Chin J Cancer. 2017, 36:23. doi:10.1186/s40880-017-0192-9
Export citations
EndNote
[ Html full-text / Citation export] (BioMed Central)
[Google Scholar]
[ More articles of the special series on Breast Cancer ]
Cite this article
Su-Hsin Huang, Joon-Khim Loh, Jinn-Tsong Tsai, Ming-Feng Houg and Hon-Yi Shi. Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents. Chin J Cancer. 2017, 36:23. doi:10.1186/s40880-017-0192-9
Export citations
EndNote
SHARE THIS ARTICLE
Comments:
