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Author Gong, Y. ♦ Yu, J. ♦ Yeung, V. ♦ Palmer, J. ♦ Yu, Y. ♦ Lu, B. ♦ Babinsky, L. ♦ Burkhart, R. ♦ Leiby, B. ♦ Siow, V. ♦ Lavu, H. ♦ Rosato, E. ♦ Winter, J. ♦ Lewis, N. ♦ Sama, A. ♦ Mitchell, E. ♦ Anne, P. ♦ Hurwitz, M. ♦ Yeo, C. ♦ Bar-Ad, V.
Source United States Department of Energy Office of Scientific and Technical Information
Content type Text
Language English
Subject Keyword APPLIED LIFE SCIENCES ♦ CARCINOMAS ♦ CHEMOTHERAPY ♦ DATASETS ♦ DECISION MAKING ♦ LYMPH ♦ LYMPH NODES ♦ NEURAL NETWORKS ♦ PANCREAS ♦ PATIENTS ♦ PERTURBED ANGULAR CORRELATION ♦ RADIOTHERAPY ♦ RANDOMNESS ♦ REGRESSION ANALYSIS ♦ VALIDATION
Abstract Purpose: Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two-year overall survival of patients with peri-ampullary cancer (PAC) following resection. Methods: Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T-stage, tumor differentiation, positive-lymph-node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two-year survival analysis, 309 patients were left, of which 44 patients (∼15%) were randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c-index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set. Results: By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two-year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c-indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C-index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c-index of Cox regression analysis was 0.63. Conclusion: ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post-operative care. This project was supported by PA CURE Grant.
ISSN 00942405
Educational Use Research
Learning Resource Type Article
Publisher Date 2015-06-15
Publisher Place United States
Journal Medical Physics
Volume Number 42
Issue Number 6


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