Dramatic Growth of a Vestibular Schwannoma After 16 Years of Postradiosurgery Stability in Association With Exposure to Tyrosine Kinase Inhibitors

Trochlear nerve schwannoma with concomitant osimertinib-responsive stage IV lung adenocarcinoma: illustrative case

BACKGROUND

The prognosis for cancer patients has been improved because of the development of molecularly targeted drugs. Treatment of intracranial tumors must be personalized while prioritizing the treatment of comorbid cancers.

OBSERVATIONS

A 38-year-old man presented with bloody sputum, bilateral multiple nodules, and a mass in the lower lobe of his right lung. Bronchoscopy revealed stage IV lung adenocarcinoma with an epidermal growth factor receptor (EGFR) mutation. Screening head magnetic resonance imaging revealed a 38-mm-diameter mass in the left petroclival area. Because the patient was neurologically intact, the treatment of lung adenocarcinoma was prioritized, and the third-generation EGFR-tyrosine kinase inhibitor osimertinib was used. Although nodules in the lung began to shrink, the intracranial lesion expanded and caused hydrocephalus, necessitating a ventriculoperitoneal shunt. The tumor also caused diplopia, dysarthria, and gait abnormalities. A left anterior transpetrosal approach was used to remove the tumor derived from the trochlear nerve. The pathological examination revealed schwannoma. Neurological symptoms improved following surgery. Osimertinib was continued during the perioperative period.

LESSONS

Osimertinib was effective for lung adenocarcinoma but not for trochlear nerve schwannoma, which required surgical intervention. It is necessary to tailor the treatment of benign brain tumors in patients with concurrent malignant cancers. https://thejns.org/doi/10.3171/CASE24144.

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

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.