|Table of Contents|

Research progress of artificial intelligence in the diagnosis and treatment of acoustic neuroma

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2024 01
Page:
146-150
Research Field:
Publishing date:

Info

Title:
Research progress of artificial intelligence in the diagnosis and treatment of acoustic neuroma
Author(s):
LIU Dong12XUE Qi3SONG Gang12WANG Zhuozheng3LIANG Jiantao12
1.Department of Neurosurgery,Xuanwu Hospital,Capital Medical University,Beijing 100053,China;2.International Neuroscience Institute(China-INI),Beijing 100053,China;3.Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China.
Keywords:
artificial intelligenceacoustic neuromavestibular schwannoma
PACS:
R739.41
DOI:
10.3969/j.issn.1672-4992.2024.01.027
Abstract:
Artificial intelligence(AI) has become the core of the fourth Industrial Revolution.AI aims to use various methods and techniques to extend human abilities,and is gradually being applied to a wide range of industries,including medicine.AI can help clinicians make decisions and bring medicine into the age of intelligence.Acoustic neuroma(AN),also known as vestibular schwannoma(VS),is a benign tumor originating from vestibular nerve,and the most common schwannoma in the brain.Common symptoms are hearing loss and tinnitus.With the popularity of magnetic resonance imaging(MRI),the discovery rate of VS is increasing.VS is now treated with follow-up observation,stereotactic surgery,or surgical treatment.The focus of the treatment has shifted from reducing mortality and total resection of the tumor to preserving facial and acoustic nerve function.This paper reviews the diagnosis,segmentation,selection of treatment methods and prognostic prediction of AI in acoustic neuroma,and looks forward to the prospect of AI in VS individualized intelligent diagnosis and treatment.

References:

[1]SUZUKI K.Overview of deep learning in medical imaging[J].Radiological Physics and Technology,2017,10(3):257-273.
[2]耿新,成睿,吉宏明.人工智能在神经外科领域的应用进展[J].中华神经外科杂志,2020,36(7):748-751. GENG X,CHENG R,JI HM.Progress in the application of artificial intelligence in neurosurgery[J].Chinese Journal of Neurosurgery,2020,36(7):748-751.
[3]CARLSON ML,INGELFINGER JR,LINK MJ.Vestibular schwannomas[J].New England Journal of Medicine,2021,384(14):1335-1348.
[4]SASAKI T,SHONO T,HASHIGUCHI K,et al.Histological considerations of the cleavage plane for preservation of facial and cochlear nerve functions in vestibular schwannoma surgery[J].J Neurosurg,2009,110(4):648-655.
[5]CAREY GE,JACOBSON CE,WARBURTON AN,et al.Machine learning for vestibular schwannoma diagnosis using audiometrie data alone[J].Otol Neurotol,2022,43(5):e530-e534.
[6]WINDISCH P,WEBER P,FURWEGER C,et al.Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices[J].Neuroradiology,2020,62(11):1515-1518.
[7]CHAKRABARTY S,SOTIRAS A,MILCHENKO M,et al.MRI-based identification and classification of major intracranial tumor types by using a 3D convolutional neural network:A retrospective multi-institutional analysis[J].Radiology Artificial Intelligence,2021,3(5):e200301.
[8]江华,于同刚,吴丽琼,等.基于AlexNet的桥小脑角脑膜瘤和听神经瘤MRI图像的识别研究[J].中国医疗器械信息,2022,28(1):44-47. JIANG H,YU TG,WU LQ,et al.MRI image recognition of cerebellopontine angle meningioma and acoustic neuroma based on alexNet[J].China Medical Device Information,2022,28(1):44-47.
[9]娄云重,刘颖,江华,等.基于MRI和深度学习的桥小脑角区脑膜瘤与听神经瘤分类算法研究[J].波谱学杂志,2020,37(3):300-310. LOU YC,LIU Y,JIANG H,et al.A deep learning algorithm for classifying meningioma and auditory neuroma in the cerebellopontine angle from magnetic resonance images[J].Chinese Journal of Magnetic Resonance,2020,37(3):300-310.
[10]刘颖,陈静聪,胡小洋,等.基于Mask RCNN的桥小脑角区脑膜瘤与听神经瘤分类定位研究[J].波谱学杂志,2021,38(1):58-68. LIU Y,CHEN JC,HU XY,et al.Classification and localization of meningioma and acoustic neuroma in cerebellopontine angle based on mask RCNN[J].Chinese Journal of Magnetic Resonance,2021,38(1):58-68.
[11]PROFANT O,BURES Z,BALOGOVA Z,et al.Decision making on vestibular schwannoma treatment:Predictions based on machine-learning analysis[J].Scientific Reports,2021,11(1):18376.
[12]GADOT R,ANAND A,LOVIN BD,et al.Predicting surgical decision-making in vestibular schwannoma using tree-based machine learning[J].Neurosurgical Focus,2022,52(4):E8.
[13]ITOYAMA T,NAKAURA T,HAMASAKI T,et al.Whole tumor radiomics analysis for risk factors associated with rapid growth of vestibular schwannoma in contrast-enhanced T1-weighted images[J].World Neurosurg,2022,166:e572-e582.
[14]LIU Z,WANG S,DONG D,et al.The applications of radiomics in precision diagnosis and treatment of oncology:Opportunities and challenges[J].Theranostics,2019,9(5):1303-1322.
[15]KANZAKI J,TOS M,SANNA M,et al.New and modified reporting systems from the consensus meeting on systems for reporting results in vestibular schwannoma[J].Otol Neurotol,2003,24(4):8-9,642-648.
[16]SHAPEY J,WANG G,DORENT R,et al.An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI[J].J Neurosurg,2019,134(1):171-179.
[17]LEE WK,WU CC,LEE CC,et al.Combining analysis of multi-parametric MR images into a convolutional neural network:Precise target delineation for vestibular schwannoma treatment planning[J].Artificial Intelligence in Medicine,2020,107:101911.
[18]LEE CC,LEE WK,WU CC,et al.Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery[J].Scientific Reports,2021,11(1):3106.
[19]GEORGE-JONES NA,WANG K,WANG J,et al.Automated detection of vestibular schwannoma growth using a two-dimensional U-Net convolutional neural network[J].Laryngoscope,2021,131(2):E619-E624.
[20]YAO P,SHAVIT SS,SHIN J,et al.Segmentation of vestibular schwannomas on postoperative gadolinium-enhanced T1-weighted and noncontrast T2-weighted magnetic resonance imaging using deep learning[J].Otol Neurotol,2022,43(10):1227-1239.
[21]CASS ND,LINDQUIST NR,ZHU Q,et al.Machine learning for automated calculation of vestibular schwannoma volumes[J].Otol Neurotol,2022,43(10):1252-1256.
[22]ZHANG Z,ZHANG X,YANG Y,et al.Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet[J].Frontiers in Neuroscience,2023,17:1207149.
[23]YANG HC,WU CC,LEE CC,et al.Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after Gamma Knife radiosurgery based on preradiosurgical MR radiomics[J].Radiotherapy and Oncology,2021,155:123-130.
[24]HUANG CY,PENG SJ,WU HM,et al.Quantification of tumor response of cystic vestibular schwannoma to Gamma Knife radiosurgery by using artificial intelligence[J].J Neurosurg,2021,2021:1-9.
[25]GEORGE-JONES NA,WANG K,WANG J,et al.Prediction of vestibular schwannoma enlargement after radiosurgery using tumor shape and MRI texture features[J].Otol Neurotol,2021,42(3):e348-e354.
[26]SONG D,ZHAI Y,TAO X,et al.Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers[J].Scientific Reports,2021,11(1):18872.
[27]CHA D,SHIN SH,KIM SH,et al.Machine learning approach for prediction of hearing preservation in vestibular schwannoma surgery[J].Scientific Reports,2020,10(1):7136.
[28]DIXON PR,WOJDYLA L,LEE J,et al.Machine learning to predict hearing preservation after middle cranial fossa approach for sporadic vestibular schwannomas[J].Otol Neurotol,2022,43(9):1072-1077.
[29]WANG J.Prediction of postoperative recovery in patients with acoustic neuroma using machine learning and SMOTE-ENN techniques[J].Mathematical Biosciences and Engineering,2022,19(10):10407-10423.
[30]YU Y,SONG G,ZHAO Y,et al.Prediction of vestibular schwannoma surgical outcome using deep neural network[J].World Neurosurg,2023,176:e60-e67.
[31]GERGANOV VM,KLINGE PM,NOURI M,et al.Prognostic clinical and radiological parameters for immediate facial nerve function following vestibular schwannoma surgery[J].Acta Neurochirurgica,2009,151(6):7,581-587.
[32]DANG S,MANZOOR NF,CHOWDHURY N,et al.Investigating predictors of increased length of stay after resection of vestibular schwannoma using machine learning[J].Otol Neurotol,2021,42(5):e584-e592.
[33]ABOUZARI M,GOSHTASBI K,SARNA B,et al.Prediction of vestibular schwannoma recurrence using artificial neural network[J].Laryngoscope Investigative Otolaryngology,2020,5(2):278-285.

Memo

Memo:
国家重点研发计划(编号:2021YFC2400803)
Last Update: 2023-11-30