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JOURNAL OF NEUROSURGICAL SCIENCES
Rivista di Neurochirurgia
Indexed/Abstracted in: e-psyche, EMBASE, PubMed/MEDLINE, Neuroscience Citation Index, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 1,651
Journal of Neurosurgical Sciences 2015 Dec 11
Use of artificial neural networks to predict the probability of developing new cerebral metastases after radiosurgery alone
Azimi P., Shahzadi S., Sadeghi S. ✉
Department of Neurosurgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AIM: The present study aimed to predict the probability of developing new cerebral metastases after Gamma Knife radiosurgery (GKR) alone in patients with 1-3 brain metastases by artificial neural network (ANN) model.
METHODS: AAN and other methods were used. The AAN has been investigated in comparison of other models of analysis, including the logistic regression (LR) and the recently introduced Huttenlocher method. Patients were divided into a distant brain failure (DBF) or a distant brain control (DBC) within 6 months of follow-up. Eleven factors were determined as the input parameters for the established ANN and were trained to predict of DBF. Sensitivity analysis was performed. The ROC curve, accuracy rate, and Hosmer-Lemeshow (H-L) were considered to test different prediction three methods.
RESULTS: 192 patients were categorized into training (n=96), testing (n=48), and validation (n=48) data sets in ANN. Rate of DBC was 60.4 and based on Huttenlocher classification were 40.7 % (Group I), 49.2% (Group II), and 88.1% (Group III). Number of cerebral metastases, primary tumor type, extra-cerebral metastases and recursive partitioning analysis (RPA) were the most important variables that were indicated by the ANN analysis. Compared to two methods, ANN model was associated with superior results: accuracy rate, 95.3 %; H-L statistic, 40.9 %; and AUC, 0. 0.88 %.
CONCLUSION: The ANNs can be used to effectively help for predicting DBF in patients with 1-3 brain metastasis treated with GKR alone.