Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients

Abstract

Objective This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging (MRI) for short-term postoperative facial nerve function in patients with acoustic neuroma. Methods A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included. Clinical data and raw features from four MRI sequences (T1-weighted, T2-weighted, T1-weighted contrast enhancement, and T2-weighted-Flair images) were analyzed. Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features. Nomogram, machine learning, and convolutional neural network (CNN) models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate model performance. A total of 1050 radiomic parameters were extracted, from which 13 radiomic and 3 clinical features were selected. Results The CNN model performed best among all prediction models in the test set with an area under the curve (AUC) of 0.89 (95% CI, 0.84–0.91). Conclusion CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma. As such, CNN modeling may serve as a potential decision-making tool for neurosurgery.

The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework

“…ChatGPT's potential in specialized medical areas was showcased through its role in assisting in establishing prognosis and supporting adjuvant therapy decisions in glioma patients, with its potential for oncology application [24][25][26][27][28]. Furthermore, the tool was explored in practical neurosurgery for research and patient care, raising ethical questions and discussing its limitations while emphasizing the importance of human verification [22,23]. Various case reports demonstrated ChatGPT's role in analyzing and reporting on specific medical cases, such as papers by Zamarud et al [3] and Montagne et al [4].…”

The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis

“…Full texts of the remaining 10 studies were then assessed carefully. An additional five articles were excluded for the following reasons: one focused on using magnetic resonance imaging for characterizing CTS [14], another analyzed the optic nerve [15], one targeted nerve block [16], one emphasized differentiating demyelinating peripheral nerve neuropathy [17], and one aimed to predict facial nerve function in acoustic neuroma patients [18] (Table S3). A total of 5 studies were included in the final quantitative analysis.…”