Evaluating growth trends of residual sporadic vestibular schwannomas: a systematic review and meta-analysis

Egiz, Abdullah; Nautiyal, Hritik; Alalade, Andrew F.; Gurusinghe, Nihal; Roberts, Gareth

doi:10.1007/s11060-022-04051-2

J Neurooncol

2022

DOI: 10.1007/s11060-022-04051-2

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Evaluating growth trends of residual sporadic vestibular schwannomas: a systematic review and meta-analysis

Abdullah Egiz

Hritik Nautiyal

Andrew F. Alalade

et al.

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Cited by 4 publications

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“…Acoustic neuroma (AN) is the most common benign lesion in the posterior cranial fossa, accounting for 80% of all tumors located in the cerebellar pontine angle [1] . Safety and preservation of facial nerve function are the main objectives of surgery [2] . With advanced microscopy, neuronavigation, intraoperative electrophysiological monitoring, and other technologies, complications from resection as well as mortality rates have significantly declined [3] .…”

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confidence: 99%

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

Wang

Jia

et al. 2023

CURR MED SCI

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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.

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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

Wang

Jia

et al. 2023

CURR MED SCI

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Outcomes following surgical resection of trigeminal schwannomas: a systematic review and meta-analysis

Karras,

Texakalidis,

Thirunavu

et al. 2023

Neurosurg Rev

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Radiation for Sporadic Vestibular Schwannoma

Woodson

2023

Otolaryngologic Clinics of North America

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Evaluating growth trends of residual sporadic vestibular schwannomas: a systematic review and meta-analysis

Cited by 4 publications

References 79 publications

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

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

Outcomes following surgical resection of trigeminal schwannomas: a systematic review and meta-analysis

Radiation for Sporadic Vestibular Schwannoma

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