Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
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Artificial Neural Network for Prediction of Distant Metastasis in Colorectal Cancer

  • Biglarian, Akbar (Department of Biostatistics, Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences) ;
  • Bakhshi, Enayatollah (Department of Biostatistics, Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences) ;
  • Gohari, Mahmood Reza (Department of Biostatistics, Hospital Management Research Center, Tehran University of Medical Sciences) ;
  • Khodabakhshi, Reza (Radiation Oncology Department, Fayazbakhsh Hospital)
  • Published : 2012.03.31
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Abstract

Background and Objectives: Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. Methods: The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. Results: The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. Conclusion: The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.

Keywords

References

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