Richardson MS (2001) Pathology of skull base tumors. Otolaryngol Clin North Am 34(6):1025–1042

Article  CAS  PubMed  Google Scholar 

Berkowitz O, Iyer AK, Kano H, Talbott EO, Lunsford LD (2015) Epidemiology and environmental risk factors associated with vestibular schwannoma. World Neurosurg 84(6):1674–1680

Article  PubMed  Google Scholar 

Carlson ML, Tveiten ØV, Lund-Johansen M, Tombers NM, Lohse CM, Link MJ (2018) Patient motivation and long-term satisfaction with treatment choice in vestibular schwannoma. World Neurosurg 114:e1245–e1252

Article  PubMed  Google Scholar 

Harner SG, Laws ER Jr (1983) Clinical findings in patients with acoustic neurinoma. Mayo Clin Proc 58(11):721–728

CAS  PubMed  Google Scholar 

Wei PH, Qi ZG, Chen G, Hu P, Li MC, Liang JT et al (2015) Identification of cranial nerves near large vestibular schwannomas using superselective diffusion tensor tractography: experience with 23 cases. Acta Neurochir Wien 157(7):1239–1249

Article  PubMed  Google Scholar 

Irving RM, Viani L, Hardy DG, Baguley DM, Moffat DA (1995) Nervus intermedius function after vestibular schwannoma removal: clinical features and pathophysiological mechanisms. Laryngoscope 105(8 Pt 1):809–813

Article  CAS  PubMed  Google Scholar 

Kunert P, Smolarek B, Marchel A (2011) Facial nerve damage following surgery for cerebellopontine angle tumours. Prevention and comprehensive treatment. Neurol Neurochir Pol 45(5):480–488

Article  PubMed  Google Scholar 

Chorobski J (1951) The syndrome of crocodile tears. AMA Arch Neurol Psychiatry 65(3):299–318

Article  CAS  PubMed  Google Scholar 

Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna (2023) A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. New York, NY, USA: Association for Computing Machinery; 2019 [cited 2023 Aug 13]. p. 2623–2631. (KDD ‘19). Available from: https://doi.org/10.1145/3292500.3330701

Rudin C (2022) Why black box machine learning should be avoided for high-stakes decisions, in brief. Nat Rev Methods Primer 2(1):81

Article  CAS  Google Scholar 

Yang C (2023) Prediction of hearing preservation after acoustic neuroma surgery based on SMOTE-XGBoost. Math Biosci Eng MBE 20(6):10757–10772

Article  PubMed  Google Scholar 

Suresh K, Elkahwagi MA, Garcia A, Naples JG, Corrales CE, Crowson MG (2023) Development of a predictive model for persistent dizziness following vestibular schwannoma surgery. Laryngoscope 133:3534

Article  PubMed  Google Scholar 

Wang MY, Jia CG, Xu HQ, Xu CS, Li X, Wei W et al (2023) 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. Curr Med Sci 43(2):336–343

Article  PubMed  PubMed Central  Google Scholar 

Yu Y, Song G, Zhao Y, Liang J, Liu Q (2023) Prediction of vestibular schwannoma surgical outcome using deep neural network. World Neurosurg 176:e60–e67

Article  PubMed  Google Scholar 

Rampp S, Holze M, Scheller C, Strauss C, Prell J (2023) Neural networks for estimation of facial palsy after vestibular schwannoma surgery. J Clin Monit Comput 37(2):575–583

Article  PubMed  Google Scholar 

Khan NR, Elarjani T, Jamshidi AM, Chen SH, Brown CS, Abecassis J et al (2022) Microsurgical management of vestibular schwannoma (acoustic neuroma): facial nerve outcomes, radiographic analysis, complications, and long-term follow-up in a series of 420 surgeries. World Neurosurg 168:e297-308

Article  PubMed  Google Scholar 

Ren Y, MacDonald BV, Tawfik KO, Schwartz MS, Friedman RA (2021) Clinical predictors of facial nerve outcomes after surgical resection of vestibular schwannoma. Otolaryngol-Head Neck Surg Off J Am Acad Otolaryngol-Head Neck Surg 164(5):1085–1093

Article  Google Scholar 

Troude L, Boucekine M, Montava M, Lavieille JP, Régis JM, Roche PH (2019) Predictive factors of early postoperative and long-term facial nerve function after large vestibular schwannoma surgery. World Neurosurg 1(127):e599-608

Article  Google Scholar 

Falcioni M, Fois P, Taibah A, Sanna M (2011) Facial nerve function after vestibular schwannoma surgery. J Neurosurg 115(4):820–826

Article  PubMed  Google Scholar 

Um I, Lee G, Lee K (2023) Adaptive boosting for ordinal target variables using neural networks. Stat Anal Data Min ASA Data Sci J 16(3):257–271

Article  Google Scholar 

The jamovi project (2023). jamovi (Version 2.3) [Computer Software]. https://www.jamovi.org. Accessed on 13 Feb 2023.

Van Rossum G, Drake FL (2009) Python 3 reference manual. Scotts Valley, CA: CreateSpace. Accessed on 13 Feb 2023

McKinney W (2010) Data structures for statistical computing in Python. SciPy 445:56

Article  Google Scholar 

Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D et al (2020) Array programming with NumPy. Nature 585(7825):357–362

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12(85):2825–2830

Google Scholar 

Hajian-Tilaki K (2013) Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Casp J Intern Med 4(2):627–635

Google Scholar 

Çorbacıoğlu ŞK, Aksel G (2023) Receiver operating characteristic curve analysis in diagnostic accuracy studies: a guide to interpreting the area under the curve value. Turk J Emerg Med 23(4):195–198

Article  PubMed  PubMed Central  Google Scholar 

Foody GM (2023) Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLoS ONE 18(10):e0291908

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom 21(1):6

Article  Google Scholar 

Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. 785 p.

Kostenko B (2023) XGBFIR. 2023 [cited 2023 Sep 27]. https://github.com/limexp/xgbfir

Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA (2023) Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep 13(1):8984

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gong H, Wang M, Zhang H, Elahe MF, Jin M (2022) An explainable AI approach for the rapid diagnosis of COVID-19 using ensemble learning algorithms. Front Public Health 10:874455

Article  PubMed  PubMed Central  Google Scholar 

Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. In: Advances in neural information processing systems. Curran Associates, Inc.; [cited 2023 Aug 13]. https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html

Ribeiro MT, Singh S, Guestrin C (2016) “Why should I trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. New York, NY, USA: Association for Computing Machinery; [cited 2023 Aug 13]. p. 1135–44. (KDD ‘16). https://dl.acm.org/doi/https://doi.org/10.1145/2939672.2939778

Fh Y, İb C, A A, B Y, C C, M A, et al (2023) Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput Biol Med. [cited 2023 Aug 13]. https://pubmed.ncbi.nlm.nih.gov/36738712/

Fenton JE, Chin RY, Fagan PA, Sterkers O, Sterkers JM (2002) Predictive factors of long-term facial nerve function after vestibular schwannoma surgery. Otol Neurotol 23(3):388–392

Article  PubMed  Google Scholar 

Tawfik KO, Alexander TH, Saliba J, Mastrodimos B, Cueva RA (2020) Predicting long-term facial nerve outcomes after resection of vestibular schwannoma. Otol Neurotol Off Publ Am Otol Soc Am Neurotol Soc Eur Acad Otol Neurotol 41(10):e1328–e1332

Article  Google Scholar 

Killeen DE, Barnett SL, Mickey BE, Hunter JB, Isaacson B, Kutz JW (2021) The association of vestibular schwannoma volume with facial nerve outcomes after surgical resection. Laryngoscope 131(4):E1328–E1334

Article  PubMed  Google Scholar 

Macielak RJ, Wallerius KP, Lawlor SK, Lohse CM, Marinelli JP, Neff BA et al (2022) Defining clinically significant tumor size in vestibular schwannoma to inform timing of microsurgery during wait-and-scan management: moving beyond minimum detectable growth. J Neurosurg 136(5):1289–1297

Article  PubMed  Google Scholar 

Schmitt WR, Daube JR, Carlson ML, Mandrekar JN, Beatty CW, Neff BA et al (2013) Use of supramaximal stimulation to predict facial nerve outcomes following vestibular schwannoma microsurgery: results from a decade of experience. J Neurosurg 118(1):206–212

Article  PubMed  Google Scholar