{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T04:42:08Z","timestamp":1788669728092,"version":"build-2803163510"},"reference-count":48,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Vestibular schwannomas (VSs) are the most common neoplasm of the cerebellopontine angle in adults. Though these lesions are generally slow growing, their growth patterns and associated symptoms can be unpredictable, which may complicate the decision to pursue conservative management versus active intervention. Additionally, surgical decision-making can be controversial because of limited high-quality evidence and multiple quality-of-life considerations. Machine learning (ML) is a powerful tool that utilizes data sets to essentialize multidimensional clinical processes. In this study, the authors trained multiple tree-based ML algorithms to predict the decision for active treatment versus MRI surveillance of VS in a single institutional cohort. In doing so, they sought to assess which preoperative variables carried the most weight in driving the decision for intervention and could be used to guide future surgical decision-making through an evidence-based approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The authors reviewed the records of patients who had undergone evaluation by neurosurgery and otolaryngology with subsequent active treatment (resection or radiation) for unilateral VS in the period from 2009 to 2021, as well as those of patients who had been evaluated for VS and were managed conservatively throughout 2021. Clinical presentation, radiographic data, and management plans were abstracted from each patient record from the time of first evaluation until the last follow-up or surgery. Each encounter with the patient was treated as an instance involving a management decision that depended on demographics, symptoms, and tumor profile. Decision tree and random forest classifiers were trained and tested to predict the decision for treatment versus imaging surveillance on the basis of unseen data using an 80\/20 pseudorandom split. Predictor variables were tuned to maximize performance based on lowest Gini impurity indices. Model performance was optimized using fivefold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>One hundred twenty-four patients with 198 rendered decisions concerning management were included in the study. In the decision tree analysis, only a maximum tumor dimension threshold of 1.6 cm and progressive symptoms were required to predict the decision for treatment with 85% accuracy. Optimizing maximum dimension thresholds and including age at presentation boosted accuracy to 88%. Random forest analysis (n = 500 trees) predicted the decision for treatment with 80% accuracy. Factors with the highest variable importance based on multiple measures of importance, including mean minimal conditional depth and largest Gini impurity reduction, were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms at presentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>Tree-based ML was used to predict which factors drive the decision for active treatment of VS with 80%\u201388% accuracy. The most important factors were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms. These results can assist in surgical decision-making and patient counseling. They also demonstrate the power of ML algorithms in extracting useful insights from limited data sets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2022.1.focus21708","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T04:21:26Z","timestamp":1648786886000},"page":"E8","source":"Crossref","is-referenced-by-count":20,"title":["Predicting surgical decision-making in vestibular schwannoma using tree-based machine learning"],"prefix":"10.3171","volume":"52","author":[{"given":"Ron","family":"Gadot","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Baylor College of Medicine;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrish","family":"Anand","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Baylor College of Medicine;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin D.","family":"Lovin","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex D.","family":"Sweeney","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akash J.","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Baylor College of Medicine;"},{"name":"Jan and Dan Duncan Neurological Research Institute, Texas Children\u2019s Hospital, Houston, Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"14","key":"b1","series-title":"N Engl J Med","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1056\/NEJMra2020394","article-title":"Vestibular schwannomas","volume":"384","author":"Carlson ML","year":"2021","unstructured":"Carlson ML, \nLink MJ. Vestibular schwannomas. \nN Engl J Med. \n2021;\n384(\n14):\n1335\u2013\n1348.\n3382682110.1056\/NEJMra2020394"},{"issue":"5","key":"b2","series-title":"J Med Genet","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1136\/jmg.39.5.315","article-title":"Molecular genetic analysis of the NF2 gene in young patients with unilateral vestibular schwannomas","volume":"39","author":"Mohyuddin A","year":"2002","unstructured":"Mohyuddin A, \nNeary WJ, \nWallace A,  Molecular genetic analysis of the NF2 gene in young patients with unilateral vestibular schwannomas. \nJ Med Genet. \n2002;\n39(\n5):\n315\u2013\n322.\n1201114610.1136\/jmg.39.5.315"},{"issue":"4","key":"b3","series-title":"Otolaryngol Head Neck Surg","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1177\/0194599818770629","article-title":"Incidence of vestibular schwannoma over the past half-century: a population-based study of Olmsted County, Minnesota","volume":"159","author":"Marinelli JP","year":"2018","unstructured":"Marinelli JP, \nLohse CM, \nCarlson ML. Incidence of vestibular schwannoma over the past half-century: a population-based study of Olmsted County, Minnesota. \nOtolaryngol Head Neck Surg. \n2018;\n159(\n4):\n717\u2013\n723.\n2971251210.1177\/0194599818770629"},{"key":"b4","series-title":"Clin Epidemiol","doi-asserted-by":"publisher","first-page":"981","DOI":"10.2147\/CLEP.S218670","article-title":"Epidemiology of vestibular schwannomas - prospective 40-year data from an unselected national cohort","volume":"11","author":"Reznitsky M","year":"2019","unstructured":"Reznitsky M, \nPetersen MMBS, \nWest N, \nStangerup SE, \nCay\u00e9-Thomasen P. Epidemiology of vestibular schwannomas - prospective 40-year data from an unselected national cohort. \nClin Epidemiol. \n2019;\n11:\n981\u2013\n986.\n3180708010.2147\/CLEP.S218670"},{"issue":"2","key":"b5","series-title":"J Neurooncol","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s11060-016-2170-5","article-title":"Does where you live influence how your vestibular schwannoma is managed? Examining geographical differences in vestibular schwannoma treatment across the United States","volume":"129","author":"Carlson ML","year":"2016","unstructured":"Carlson ML, \nGlasgow AE, \nGrossardt BR, \nHabermann EB, \nLink MJ. Does where you live influence how your vestibular schwannoma is managed? Examining geographical differences in vestibular schwannoma treatment across the United States. \nJ Neurooncol. \n2016;\n129(\n2):\n269\u2013\n279.\n2733490310.1007\/s11060-016-2170-5"},{"issue":"12","key":"b6","series-title":"J Clin Neurosci","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1016\/j.jocn.2011.05.009","article-title":"Systematic review of quality of life in the management of vestibular schwannoma","volume":"18","author":"Gauden A","year":"2011","unstructured":"Gauden A, \nWeir P, \nHawthorne G, \nKaye A. Systematic review of quality of life in the management of vestibular schwannoma. \nJ Clin Neurosci. \n2011;\n18(\n12):\n1573\u2013\n1584.\n2201459810.1016\/j.jocn.2011.05.009"},{"issue":"1","key":"b7","series-title":"Expert Rev Neurother","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1080\/14737175.2018.1399795","article-title":"An update on the diagnosis and treatment of vestibular schwannoma","volume":"18","author":"Halliday J","year":"2018","unstructured":"Halliday J, \nRutherford SA, \nMcCabe MG, \nEvans DG. An update on the diagnosis and treatment of vestibular schwannoma. \nExpert Rev Neurother. \n2018;\n18(\n1):\n29\u2013\n39.\n2908899310.1080\/14737175.2018.1399795"},{"key":"b8","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"18599","DOI":"10.1038\/srep18599","article-title":"Secreted factors from human vestibular schwannomas can cause cochlear damage","volume":"5","author":"Dilwali S","year":"2015","unstructured":"Dilwali S, \nLandegger LD, \nSoares VY, \nDeschler DG, \nStankovic KM. Secreted factors from human vestibular schwannomas can cause cochlear damage. \nSci Rep. \n2015;\n5:\n18599.\n2669050610.1038\/srep18599"},{"key":"b9","series-title":"World Neurosurg","doi-asserted-by":"publisher","first-page":"e1245","DOI":"10.1016\/j.wneu.2018.03.182","article-title":"Patient motivation and long-term satisfaction with treatment choice in vestibular schwannoma","volume":"114","author":"Carlson ML","year":"2018","unstructured":"Carlson ML, \nTveiten \u00d8V, \nLund-Johansen M, \nTombers NM, \nLohse CM, \nLink MJ. Patient motivation and long-term satisfaction with treatment choice in vestibular schwannoma. \nWorld Neurosurg. \n2018;\n114:\ne1245\u2013\ne1252.\n2962530510.1016\/j.wneu.2018.03.182"},{"key":"b10","series-title":"J Neurosurg","article-title":"Defining clinically significant tumor size in vestibular schwannoma to inform timing of microsurgery during wait-and-scan management: moving beyond minimum detectable growth","author":"Macielak RJ","unstructured":"Macielak RJ, \nWallerius KP, \nLawlor SK,  Defining clinically significant tumor size in vestibular schwannoma to inform timing of microsurgery during wait-and-scan management: moving beyond minimum detectable growth. \nJ Neurosurg. Published online October 15, 2021. doi:10.3171\/2021.4.JNS21465\n34653971"},{"issue":"1","key":"b11","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"18376","DOI":"10.1038\/s41598-021-97819-x","article-title":"Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis","volume":"11","author":"Profant O","year":"2021","unstructured":"Profant O, \nBure\u0161 Z, \nBalogov\u00e1 Z,  Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis. \nSci Rep. \n2021;\n11(\n1):\n18376.\n3452658010.1038\/s41598-021-97819-x"},{"issue":"5","key":"b12","series-title":"Circ Cardiovasc Qual Outcomes","doi-asserted-by":"publisher","first-page":"e004879","DOI":"10.1161\/CIRCOUTCOMES.118.004879","article-title":"Tree-based analysis","volume":"12","author":"Banerjee M","year":"2019","unstructured":"Banerjee M, \nReynolds E, \nAndersson HB, \nNallamothu BK. Tree-based analysis. \nCirc Cardiovasc Qual Outcomes. \n2019;\n12(\n5):\ne004879.\n3104306410.1161\/CIRCOUTCOMES.118.004879"},{"issue":"3","key":"b13","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1093\/neuros\/nyy409","article-title":"Koos classification of vestibular schwannomas: a reliability study","volume":"85","author":"Erickson NJ","year":"2019","unstructured":"Erickson NJ, \nSchmalz PGR, \nAgee BS,  Koos classification of vestibular schwannomas: a reliability study. \nNeurosurgery. \n2019;\n85(\n3):\n409\u2013\n414.\n3016969510.1093\/neuros\/nyy409"},{"issue":"2","key":"b14","series-title":"Shanghai Jingshen Yixue","first-page":"130","article-title":"Decision tree methods: applications for classification and prediction","volume":"27","author":"Song YY","year":"2015","unstructured":"Song YY, \nLu Y. Decision tree methods: applications for classification and prediction. \nShanghai Jingshen Yixue. \n2015;\n27(\n2):\n130\u2013\n135."},{"issue":"21","key":"b15","series-title":"Bioinformatics","doi-asserted-by":"publisher","first-page":"3711","DOI":"10.1093\/bioinformatics\/bty373","article-title":"The revival of the Gini importance?","volume":"34","author":"Nembrini S","year":"2018","unstructured":"Nembrini S, \nK\u00f6nig IR, \nWright MN. The revival of the Gini importance? \nBioinformatics. \n2018;\n34(\n21):\n3711\u2013\n3718.\n2975735710.1093\/bioinformatics\/bty373"},{"issue":"6","key":"b16","series-title":"J Chem Inf Comput Sci","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest: a classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik V","year":"2003","unstructured":"Svetnik V, \nLiaw A, \nTong C, \nCulberson JC, \nSheridan RP, \nFeuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. \nJ Chem Inf Comput Sci. \n2003;\n43(\n6):\n1947\u2013\n1958.\n1463244510.1021\/ci034160g"},{"key":"b17","first-page":"5","article-title":"Random forests. Mach Learn","volume":"45","author":"Breiman L","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. \n2001;\n45:\n5\u2013\n32."},{"issue":"6","key":"b18","series-title":"Surg Infect (Larchmt)","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1089\/sur.2021.007","article-title":"Machine learning and artificial intelligence for surgical decision making","volume":"22","author":"Byerly S","year":"2021","unstructured":"Byerly S, \nMaurer LR, \nMantero A, \nNaar L, \nAn G, \nKaafarani HMA. Machine learning and artificial intelligence for surgical decision making. \nSurg Infect (Larchmt). \n2021;\n22(\n6):\n626\u2013\n634.\n10.1089\/sur.2021.007"},{"issue":"2","key":"b19","series-title":"JAMA Surg","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1001\/jamasurg.2019.4917","article-title":"Artificial intelligence and surgical decision-making","volume":"155","author":"Loftus TJ","year":"2020","unstructured":"Loftus TJ, \nTighe PJ, \nFiliberto AC,  Artificial intelligence and surgical decision-making. \nJAMA Surg. \n2020;\n155(\n2):\n148\u2013\n158.\n3182546510.1001\/jamasurg.2019.4917"},{"issue":"4","key":"b20","series-title":"PLoS One","doi-asserted-by":"publisher","first-page":"e0194168","DOI":"10.1371\/journal.pone.0194168","article-title":"A novel decision tree classification based on post-pruning with Bayes minimum risk","volume":"13","author":"Ahmed AM","year":"2018","unstructured":"Ahmed AM, \nRizaner A, \nUlusoy AH. A novel decision tree classification based on post-pruning with Bayes minimum risk. \nPLoS One. \n2018;\n13(\n4):\ne0194168.\n2961736910.1371\/journal.pone.0194168"},{"key":"b21","series-title":"Conf Proc IEEE Eng Med Biol Soc.","doi-asserted-by":"publisher","first-page":"3478","DOI":"10.1109\/IEMBS.2006.259893","article-title":"New ensemble machine learning method for classification and prediction on gene expression data","volume":"2006","author":"Wang CW","year":"2006","unstructured":"Wang CW. New ensemble machine learning method for classification and prediction on gene expression data. \nConf Proc IEEE Eng Med Biol Soc.\n2006;\n2006:\n3478\u2013\n3481.\n1794578010.1109\/IEMBS.2006.259893"},{"issue":"3","key":"b22","series-title":"Brief Bioinform","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1093\/bib\/bbs034","article-title":"Data mining in the life sciences with random forest: a walk in the park or lost in the jungle?","volume":"14","author":"Touw WG","year":"2013","unstructured":"Touw WG, \nBayjanov JR, \nOvermars L,  Data mining in the life sciences with random forest: a walk in the park or lost in the jungle? \nBrief Bioinform. \n2013;\n14(\n3):\n315\u2013\n326.\n10.1093\/bib\/bbs034"},{"key":"b23","series-title":"Front Aging Neurosci","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3389\/fnagi.2017.00329","article-title":"Random forest algorithm for the classification of neuroimaging data in Alzheimer\u2019s disease: a systematic review","volume":"9","author":"Sarica A","year":"2017","unstructured":"Sarica A, \nCerasa A, \nQuattrone A. Random forest algorithm for the classification of neuroimaging data in Alzheimer\u2019s disease: a systematic review. \nFront Aging Neurosci. \n2017;\n9:\n329.\n2905690610.3389\/fnagi.2017.00329"},{"issue":"1","key":"b24","series-title":"BMC Bioinformatics","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1186\/s12859-018-2264-5","article-title":"Random forest versus logistic regression: a large-scale benchmark experiment","volume":"19","author":"Couronn\u00e9 R","year":"2018","unstructured":"Couronn\u00e9 R, \nProbst P, \nBoulesteix AL. Random forest versus logistic regression: a large-scale benchmark experiment. \nBMC Bioinformatics. \n2018;\n19(\n1):\n270.\n3001695010.1186\/s12859-018-2264-5"},{"issue":"14","key":"b1","series-title":"N Engl J Med","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1056\/NEJMra2020394","article-title":"Vestibular schwannomas","volume":"384","author":"Carlson ML","year":"2021","unstructured":"Carlson ML, \nLink MJ. Vestibular schwannomas. \nN Engl J Med. \n2021;\n384(\n14):\n1335\u2013\n1348.\n3382682110.1056\/NEJMra2020394"},{"issue":"5","key":"b2","series-title":"J Med Genet","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1136\/jmg.39.5.315","article-title":"Molecular genetic analysis of the NF2 gene in young patients with unilateral vestibular schwannomas","volume":"39","author":"Mohyuddin A","year":"2002","unstructured":"Mohyuddin A, \nNeary WJ, \nWallace A,  Molecular genetic analysis of the NF2 gene in young patients with unilateral vestibular schwannomas. \nJ Med Genet. \n2002;\n39(\n5):\n315\u2013\n322.\n1201114610.1136\/jmg.39.5.315"},{"issue":"4","key":"b3","series-title":"Otolaryngol Head Neck Surg","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1177\/0194599818770629","article-title":"Incidence of vestibular schwannoma over the past half-century: a population-based study of Olmsted County, Minnesota","volume":"159","author":"Marinelli JP","year":"2018","unstructured":"Marinelli JP, \nLohse CM, \nCarlson ML. Incidence of vestibular schwannoma over the past half-century: a population-based study of Olmsted County, Minnesota. \nOtolaryngol Head Neck Surg. \n2018;\n159(\n4):\n717\u2013\n723.\n2971251210.1177\/0194599818770629"},{"key":"b4","series-title":"Clin Epidemiol","doi-asserted-by":"publisher","first-page":"981","DOI":"10.2147\/CLEP.S218670","article-title":"Epidemiology of vestibular schwannomas - prospective 40-year data from an unselected national cohort","volume":"11","author":"Reznitsky M","year":"2019","unstructured":"Reznitsky M, \nPetersen MMBS, \nWest N, \nStangerup SE, \nCay\u00e9-Thomasen P. Epidemiology of vestibular schwannomas - prospective 40-year data from an unselected national cohort. \nClin Epidemiol. \n2019;\n11:\n981\u2013\n986.\n3180708010.2147\/CLEP.S218670"},{"issue":"2","key":"b5","series-title":"J Neurooncol","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s11060-016-2170-5","article-title":"Does where you live influence how your vestibular schwannoma is managed? Examining geographical differences in vestibular schwannoma treatment across the United States","volume":"129","author":"Carlson ML","year":"2016","unstructured":"Carlson ML, \nGlasgow AE, \nGrossardt BR, \nHabermann EB, \nLink MJ. Does where you live influence how your vestibular schwannoma is managed? Examining geographical differences in vestibular schwannoma treatment across the United States. \nJ Neurooncol. \n2016;\n129(\n2):\n269\u2013\n279.\n2733490310.1007\/s11060-016-2170-5"},{"issue":"12","key":"b6","series-title":"J Clin Neurosci","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1016\/j.jocn.2011.05.009","article-title":"Systematic review of quality of life in the management of vestibular schwannoma","volume":"18","author":"Gauden A","year":"2011","unstructured":"Gauden A, \nWeir P, \nHawthorne G, \nKaye A. Systematic review of quality of life in the management of vestibular schwannoma. \nJ Clin Neurosci. \n2011;\n18(\n12):\n1573\u2013\n1584.\n2201459810.1016\/j.jocn.2011.05.009"},{"issue":"1","key":"b7","series-title":"Expert Rev Neurother","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1080\/14737175.2018.1399795","article-title":"An update on the diagnosis and treatment of vestibular schwannoma","volume":"18","author":"Halliday J","year":"2018","unstructured":"Halliday J, \nRutherford SA, \nMcCabe MG, \nEvans DG. An update on the diagnosis and treatment of vestibular schwannoma. \nExpert Rev Neurother. \n2018;\n18(\n1):\n29\u2013\n39.\n2908899310.1080\/14737175.2018.1399795"},{"key":"b8","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"18599","DOI":"10.1038\/srep18599","article-title":"Secreted factors from human vestibular schwannomas can cause cochlear damage","volume":"5","author":"Dilwali S","year":"2015","unstructured":"Dilwali S, \nLandegger LD, \nSoares VY, \nDeschler DG, \nStankovic KM. Secreted factors from human vestibular schwannomas can cause cochlear damage. \nSci Rep. \n2015;\n5:\n18599.\n2669050610.1038\/srep18599"},{"key":"b9","series-title":"World Neurosurg","doi-asserted-by":"publisher","first-page":"e1245","DOI":"10.1016\/j.wneu.2018.03.182","article-title":"Patient motivation and long-term satisfaction with treatment choice in vestibular schwannoma","volume":"114","author":"Carlson ML","year":"2018","unstructured":"Carlson ML, \nTveiten \u00d8V, \nLund-Johansen M, \nTombers NM, \nLohse CM, \nLink MJ. Patient motivation and long-term satisfaction with treatment choice in vestibular schwannoma. \nWorld Neurosurg. \n2018;\n114:\ne1245\u2013\ne1252.\n2962530510.1016\/j.wneu.2018.03.182"},{"key":"b10","series-title":"J Neurosurg","article-title":"Defining clinically significant tumor size in vestibular schwannoma to inform timing of microsurgery during wait-and-scan management: moving beyond minimum detectable growth","author":"Macielak RJ","unstructured":"Macielak RJ, \nWallerius KP, \nLawlor SK,  Defining clinically significant tumor size in vestibular schwannoma to inform timing of microsurgery during wait-and-scan management: moving beyond minimum detectable growth. \nJ Neurosurg. Published online October 15, 2021. doi:10.3171\/2021.4.JNS21465\n34653971"},{"issue":"1","key":"b11","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"18376","DOI":"10.1038\/s41598-021-97819-x","article-title":"Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis","volume":"11","author":"Profant O","year":"2021","unstructured":"Profant O, \nBure\u0161 Z, \nBalogov\u00e1 Z,  Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis. \nSci Rep. \n2021;\n11(\n1):\n18376.\n3452658010.1038\/s41598-021-97819-x"},{"issue":"5","key":"b12","series-title":"Circ Cardiovasc Qual Outcomes","doi-asserted-by":"publisher","first-page":"e004879","DOI":"10.1161\/CIRCOUTCOMES.118.004879","article-title":"Tree-based analysis","volume":"12","author":"Banerjee M","year":"2019","unstructured":"Banerjee M, \nReynolds E, \nAndersson HB, \nNallamothu BK. Tree-based analysis. \nCirc Cardiovasc Qual Outcomes. \n2019;\n12(\n5):\ne004879.\n3104306410.1161\/CIRCOUTCOMES.118.004879"},{"issue":"3","key":"b13","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1093\/neuros\/nyy409","article-title":"Koos classification of vestibular schwannomas: a reliability study","volume":"85","author":"Erickson NJ","year":"2019","unstructured":"Erickson NJ, \nSchmalz PGR, \nAgee BS,  Koos classification of vestibular schwannomas: a reliability study. \nNeurosurgery. \n2019;\n85(\n3):\n409\u2013\n414.\n3016969510.1093\/neuros\/nyy409"},{"issue":"2","key":"b14","series-title":"Shanghai Jingshen Yixue","first-page":"130","article-title":"Decision tree methods: applications for classification and prediction","volume":"27","author":"Song YY","year":"2015","unstructured":"Song YY, \nLu Y. Decision tree methods: applications for classification and prediction. \nShanghai Jingshen Yixue. \n2015;\n27(\n2):\n130\u2013\n135."},{"issue":"21","key":"b15","series-title":"Bioinformatics","doi-asserted-by":"publisher","first-page":"3711","DOI":"10.1093\/bioinformatics\/bty373","article-title":"The revival of the Gini importance?","volume":"34","author":"Nembrini S","year":"2018","unstructured":"Nembrini S, \nK\u00f6nig IR, \nWright MN. The revival of the Gini importance? \nBioinformatics. \n2018;\n34(\n21):\n3711\u2013\n3718.\n2975735710.1093\/bioinformatics\/bty373"},{"issue":"6","key":"b16","series-title":"J Chem Inf Comput Sci","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest: a classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik V","year":"2003","unstructured":"Svetnik V, \nLiaw A, \nTong C, \nCulberson JC, \nSheridan RP, \nFeuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. \nJ Chem Inf Comput Sci. \n2003;\n43(\n6):\n1947\u2013\n1958.\n1463244510.1021\/ci034160g"},{"key":"b17","first-page":"5","article-title":"Random forests. Mach Learn","volume":"45","author":"Breiman L","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. \n2001;\n45:\n5\u2013\n32."},{"issue":"6","key":"b18","series-title":"Surg Infect (Larchmt)","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1089\/sur.2021.007","article-title":"Machine learning and artificial intelligence for surgical decision making","volume":"22","author":"Byerly S","year":"2021","unstructured":"Byerly S, \nMaurer LR, \nMantero A, \nNaar L, \nAn G, \nKaafarani HMA. Machine learning and artificial intelligence for surgical decision making. \nSurg Infect (Larchmt). \n2021;\n22(\n6):\n626\u2013\n634.\n10.1089\/sur.2021.007"},{"issue":"2","key":"b19","series-title":"JAMA Surg","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1001\/jamasurg.2019.4917","article-title":"Artificial intelligence and surgical decision-making","volume":"155","author":"Loftus TJ","year":"2020","unstructured":"Loftus TJ, \nTighe PJ, \nFiliberto AC,  Artificial intelligence and surgical decision-making. \nJAMA Surg. \n2020;\n155(\n2):\n148\u2013\n158.\n3182546510.1001\/jamasurg.2019.4917"},{"issue":"4","key":"b20","series-title":"PLoS One","doi-asserted-by":"publisher","first-page":"e0194168","DOI":"10.1371\/journal.pone.0194168","article-title":"A novel decision tree classification based on post-pruning with Bayes minimum risk","volume":"13","author":"Ahmed AM","year":"2018","unstructured":"Ahmed AM, \nRizaner A, \nUlusoy AH. A novel decision tree classification based on post-pruning with Bayes minimum risk. \nPLoS One. \n2018;\n13(\n4):\ne0194168.\n2961736910.1371\/journal.pone.0194168"},{"key":"b21","series-title":"Conf Proc IEEE Eng Med Biol Soc.","doi-asserted-by":"publisher","first-page":"3478","DOI":"10.1109\/IEMBS.2006.259893","article-title":"New ensemble machine learning method for classification and prediction on gene expression data","volume":"2006","author":"Wang CW","year":"2006","unstructured":"Wang CW. New ensemble machine learning method for classification and prediction on gene expression data. \nConf Proc IEEE Eng Med Biol Soc.\n2006;\n2006:\n3478\u2013\n3481.\n1794578010.1109\/IEMBS.2006.259893"},{"issue":"3","key":"b22","series-title":"Brief Bioinform","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1093\/bib\/bbs034","article-title":"Data mining in the life sciences with random forest: a walk in the park or lost in the jungle?","volume":"14","author":"Touw WG","year":"2013","unstructured":"Touw WG, \nBayjanov JR, \nOvermars L,  Data mining in the life sciences with random forest: a walk in the park or lost in the jungle? \nBrief Bioinform. \n2013;\n14(\n3):\n315\u2013\n326.\n10.1093\/bib\/bbs034"},{"key":"b23","series-title":"Front Aging Neurosci","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3389\/fnagi.2017.00329","article-title":"Random forest algorithm for the classification of neuroimaging data in Alzheimer\u2019s disease: a systematic review","volume":"9","author":"Sarica A","year":"2017","unstructured":"Sarica A, \nCerasa A, \nQuattrone A. Random forest algorithm for the classification of neuroimaging data in Alzheimer\u2019s disease: a systematic review. \nFront Aging Neurosci. \n2017;\n9:\n329.\n2905690610.3389\/fnagi.2017.00329"},{"issue":"1","key":"b24","series-title":"BMC Bioinformatics","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1186\/s12859-018-2264-5","article-title":"Random forest versus logistic regression: a large-scale benchmark experiment","volume":"19","author":"Couronn\u00e9 R","year":"2018","unstructured":"Couronn\u00e9 R, \nProbst P, \nBoulesteix AL. Random forest versus logistic regression: a large-scale benchmark experiment. \nBMC Bioinformatics. \n2018;\n19(\n1):\n270.\n3001695010.1186\/s12859-018-2264-5"}],"container-title":["Neurosurgical Focus"],"original-title":[],"link":[{"URL":"https:\/\/thejns.org\/view\/journals\/neurosurg-focus\/52\/4\/article-pE8.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/neurosurg-focus\/52\/4\/article-pE8.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/neurosurg-focus\/52\/4\/article-pE8.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T08:27:38Z","timestamp":1780734458000},"score":1,"resource":{"primary":{"URL":"https:\/\/thejns.org\/view\/journals\/neurosurg-focus\/52\/4\/article-pE8.xml"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":48,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3171\/2022.1.focus21708","relation":{},"ISSN":["1092-0684"],"issn-type":[{"value":"1092-0684","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4]]}}}