Sung Eun Oh1, Sung Wook Seo2, Min-Gew Choi3, Tae Sung Sohn1, Jae Moon Bae1, Sung Kim1. 1. Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. 2. Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. sungwook.seo@samsung.com. 3. Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. mingew.choi@samsung.com.
Abstract
BACKGROUND: Artificial neural networks (ANNs) have been applied to many prediction and classification problems, and could also be used to develop a prediction model of survival outcomes for cancer patients. OBJECTIVE: The aim of this study is to develop a prediction model of survival outcomes for patients with gastric cancer using an ANN. METHODS: This study enrolled 1243 patients with stage IIA-IV gastric cancer who underwent D2 gastrectomy from January 2007 to June 2010. We used a recurrent neural network (RNN) to make the survival recurrent network (SRN), and patients were randomly sorted into a training set (80%) and a test set (20%). Fivefold cross-validation was performed with the training set, and the optimized model was evaluated with the test set. Receiver operating characteristic (ROC) curves and area under the curves (AUCs) were evaluated, and we compared the survival curves of the American Joint Committee on Cancer (AJCC) 8th stage groups with those of the groups classified by the SRN-predicted survival probability. RESULTS: The test data showed that the ROC AUC of the SRN was 0.81 at the fifth year. The SRN-predicted survival corresponded closely with the actual survival in the calibration curve, and the survival outcome could be more discriminately classified by using the SRN than by using the AJCC staging system. CONCLUSION: SRN was a more powerful tool for predicting the survival rates of gastric cancer patients than conventional TNM staging, and may also provide a more flexible and expandable method when compared with fixed prediction models such as nomograms.
BACKGROUND: Artificial neural networks (ANNs) have been applied to many prediction and classification problems, and could also be used to develop a prediction model of survival outcomes for cancerpatients. OBJECTIVE: The aim of this study is to develop a prediction model of survival outcomes for patients with gastric cancer using an ANN. METHODS: This study enrolled 1243 patients with stage IIA-IV gastric cancer who underwent D2 gastrectomy from January 2007 to June 2010. We used a recurrent neural network (RNN) to make the survival recurrent network (SRN), and patients were randomly sorted into a training set (80%) and a test set (20%). Fivefold cross-validation was performed with the training set, and the optimized model was evaluated with the test set. Receiver operating characteristic (ROC) curves and area under the curves (AUCs) were evaluated, and we compared the survival curves of the American Joint Committee on Cancer (AJCC) 8th stage groups with those of the groups classified by the SRN-predicted survival probability. RESULTS: The test data showed that the ROC AUC of the SRN was 0.81 at the fifth year. The SRN-predicted survival corresponded closely with the actual survival in the calibration curve, and the survival outcome could be more discriminately classified by using the SRN than by using the AJCC staging system. CONCLUSION: SRN was a more powerful tool for predicting the survival rates of gastric cancerpatients than conventional TNM staging, and may also provide a more flexible and expandable method when compared with fixed prediction models such as nomograms.
Authors: Jian Lv; Yuan Yuan Liu; Yi Tao Jia; Jing Li He; Guang Yao Dai; Peng Guo; Zhao Long Zhao; Yan Ni Zhang; Zhong Xin Li Journal: World J Surg Oncol Date: 2021-12-02 Impact factor: 2.754