Prediction of vestibular schwannoma recurrence using artificial neural network. Issue 2 (17th February 2020)

• Record Type: Journal Article
• Authors: Abouzari, Mehdi; Goshtasbi, Khodayar; Sarna, Brooke; Khosravi, Pooya; Reutershan, Trevor; Mostaghni, Navid; Lin, Harrison W.; Djalilian, Hamid R.
• Is Part Of: Laryngoscope investigative otolaryngology. Volume 5:Issue 2(2020)
• Page Start: 278
• Page End: 285
• Publication Date: 2020-02-17
• Subjects: acoustic neuroma -- artificial intelligence -- artificial neural network -- logistic regression -- recurrence -- vestibular schwannoma; Otolaryngology -- Periodicals; Laryngoscopy -- Periodicals; Otolaryngology; Otolaryngology; Periodicals; Periodicals; 617.51
• DOI: 10.1002/lio2.362
• Languages: English
• ISSNs: 2378-8038

Abstract

Abstract: Objectives: To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence. Methods: Seven hundred eighty‐nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four‐layer 9‐5‐10‐1 feedforward backpropagation ANN for prediction of recurrence. Results: The overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both P < .001). The regression model did not show an acceptable performance (area under the curve [AUC] = 0.64; P = .27). The regression model's sensitivity and specificity were 44% and 69%, respectively and correctly classified 56% of cases. The ANN showed a superior performance compared to the regression model (AUC = 0.79; P = .001) with higher sensitivity (61%) and specificity (81%), and correctly classified 70% of cases. Conclusion: The constructed ANN model was superior to logistic regression in predicting patient‐answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.