Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck.

PURPOSE: This study was performed to investigate the feasibility of predicting survival in squamous cell carcinoma of the head and neck (SCCHN) with an artificial neural network (ANN), and to compare ANN performance with conventional models. METHODS AND MATERIALS: Data were analyzed from a Phase III trial in which patients with locally advanced SCCHN received hyperfractionated irradiation with or without concurrent cisplatin and 5-fluorouracil. Of the 116 randomized patients, 95 who had 2-year follow-up and all required data were evaluated. ANN and logistic regression (LR) models were constructed to predict 2-year total survival using round-robin cross-validation. A modified staging model was also examined. RESULTS: The best LR model used tumor size, nodal stage, and race to predict survival. The best ANN used nodal stage, tumor size, stage, and resectability, and hemoglobin. Treatment type did not predict 2-year survival and was not included in either model. Using the respective best feature sets, the area under the receiver operating characteristic curve (Az) for the ANN was 0.78 +/- 0.05, showing more accurate overall performance than LR (Az = 0.67 +/- 0.05, p = 0.07). At 70% sensitivity, the ANN was 72% specific, while LR was 54% specific (p = 0.08). At 70% specificity, the ANN was 72% sensitive, while LR was 54% sensitive (p = 0.07). When both models used the five predictive variables best for an ANN, Az for LR decreased [Az = 0.61 +/- 0.06, p < 0.01 (ANN)]. The models performed equivalently when using the three variables best for LR. The best ANN also compared favorably with staging [Az = 0.60 +/- 0.07, p = 0.02 (ANN)]. CONCLUSIONS: An ANN modeled 2-year survival in this data set more accurately than LR or staging models and employed predictive variables that could not be used by LR. Further work is planned to confirm these results on larger patient samples, examining longer follow-up to incorporate treatment type into the model.

Duke Scholars

Author Mark Wesley Dewhirst Radiation Oncology

Author David Manfield Brizel Radiation Oncology