Evaluation of the Location and Surgical Outcomes of Intracranial Space Occupying Lesions Using Predictive Models Based on Somatosensory Evoked Potentials and Motor Evoked Potentials

Background: Intracranial space-occupying lesions (IOLs) often require precise surgical resection. Intraoperative neurophysiological monitoring (IONM), including somatosensory evoked potentials (SEPs) and motor evoked potentials (MEPs), is widely used to preserve neurological function. However, interpretation of IONM data still relies heavily on the experience of the surgeon. The aim of this study was to develop machine-learning (ML) models based on IONM data to support the assessment of lesion location relative to functional brain areas and surgical outcomes. Methods: We initially screened 377 patients undergoing microsurgical resection of IOLs. The clinical data on these patients included demographic characteristics, quantitative IONM parameters (SEP and MEP amplitude and latency), lesion localization, and postoperative adverse events. Four ML models were developed: support vector machine (SVM), decision tree, random forest, and naïve Bayes. Model performance was evaluated using several metrics, including accuracy, sensitivity, specificity, precision, F1-score, and the area under the curve (AUC). Results: Significant differences in SEP and MEP parameters were observed between patient groups with lesions located in functional and non-functional brain areas (all p < 0.05). SEP and MEP parameters were both associated with lesion localization and postoperative adverse events, with differential correlation patterns observed between the two modalities. The ML models demonstrated moderate discriminative performance in predicting lesion involvement in functional areas, with the highest accuracy of 79.2% in the training set and 65.00% in the test set. The models showed good performance in predicting serious adverse events, with the best accuracy of >78% in both datasets. Conclusions: ML models based on IONM data may help to assess lesion location relative to functional brain areas, as well as the prediction of postoperative outcomes. These findings suggest that ML-assisted analysis of IONM data may provide an exploratory framework for understanding lesion localization and postoperative outcomes, rather than a clinically deployable decision-support tool.