Program Information
Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma
Y Gong*, J Yu , V Yeung , J Palmer , Y Yu , B Lu , L Babinsky , R Burkhart , B Leiby , V Siow , H Lavu , E Rosato , J Winter , N Lewis , A Sama , E Mitchell , P Anne , M Hurwitz , C Yeo , V Bar-Ad , Y Xiao , Thomas Jefferson University Hospital, Philadelphia, PA
Presentations
SU-E-T-131 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall
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.
Funding Support, Disclosures, and Conflict of Interest: This project was supported by PA CURE Grant.
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