應用類神經網路預測鼻咽癌病人之五年存活狀態

Translated title of the contribution: Clinical Application of Artificial Neural Network in Predicting Five-Year Survival of Patients with Nasopharyngeal Carcinoma

Chong-Hao Cheng, Skye Hon-Giun Cheng, Hung-Wen Chiu

Research output: Contribution to journalArticle

Abstract

Purpose: This study wants to establish a prognostic prediction system by artificial neural network for individual patients with nasopharyngeal carcinoma.Materials and methods: In this study, the dataset are 1,114 patients with nasopharyngeal carcinoma in one cancer center during the year from 1990 to 2005. The chosen variables include age, sex, primary tumor, regional lymph nodes, biopsy, radiotherapy, chemotherapy, lactic dehydrogenase, alkaline phosphatase, smoking, and family history. The final dataset are 984 patients excluding 70 patients with any data columns missing and 60 patients with distant metastasis. Seventy-five percent of 984 patients are randomly selected and classified to training group. An artificial neural network is created by computer software to predict the five-year survival status of patients with nasopharyngeal carcinoma. The performance of prediction models will be evaluated according to parameters such as accuracy, sensitivity, specificity, and the area under receiver operating characteristic curve.Result: The average age of the patients is 45.45 years old, and the five-year overall survival rate is 77.74%. The optimized artificial neural network is MLP 34-5-2 which training performance is 92.00 and test performance is 87.80. Its accuracy is 90.96%, sensitivity is 93.73%, specificity is 81.27%, and area under the ROC curve is 0.95 for all patients. Its accuracy is 87.80%, sensitivity is 92.23%, specificity is 71.70%, and area under the ROC curve is 0.88 for test group.Conclusion: This study shows that the prognostic prediction system established by artificial neural network has the potential to predict the five-year survival status of individual patients with nasopharyngeal carcinoma. With more input data, the performance of prediction models is better than previous researches. But this prognostic prediction system still need further study to prove it could be used for clinical patients.
Original languageTraditional Chinese
Pages (from-to)191-197
Number of pages7
Journal放射治療與腫瘤學
Volume20
Issue number3
Publication statusPublished - 2013

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