ArticleQuantitative imaging in medicine and surgery2026
Prediction of postoperative facial nerve function with preoperative magnetic resonance imaging in patients with vestibular schwannoma.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Vestibular schwannoma (VS) is primarily treated with surgery. Safety and the preservation of facial nerve function are the main objectives of the surgery, as patients experiencing facial nerve dysfunction face a multitude of challenges. Therefore, we used preoperative magnetic resonance (MR) images to predict postoperative facial nerve function via artificial intelligence (AI) and compared the performance of the resulting learning models with those of neurosurgeons. Methods: We collected information from 89 VS patients, including data regarding postoperative facial nerve function, patient information, and MR images. The best performing of 9 constructed models was chosen as the final model. Building models to train and test, we analyzed the best-performing model to identify which factors could have the greatest impact on it with SHapley Additive exPlanations (SHAPs). We subsequently evaluated the performance of neurosurgeons at different experience levels and compared their performance with those of the models. Results: The results revealed that the decision tree (DT) model exhibited the best performance. The features selected by the DT model included the color principal component, histogram of oriented gradient (HOG), texture, the discrete cosine transform (DCT) coefficient and flatness, with weights of 0.158, 0.225, 0.275, 0.126, and 0.215, respectively. The results of SHAP analysis revealed that texture made the greatest contribution to the model, followed by HOG. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DT model were 80.93%, 92.31%, 76.32%, 57.14%, and 96.67%, respectively, all of which were better than those of the neurosurgeons (P<0.05). Besides sensitivity and NPV of experts, the data of the DT model were better than those of neurosurgeons. Conclusions: The DT prediction model could help neurosurgeons to predict postoperative facial nerve function with preoperative MR images as a decision-support tool. In addition, the performance of the model was better than that of the neurosurgeons alone.
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