{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:20:00Z","timestamp":1788787200223,"version":"build-2803163510"},"reference-count":52,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11]]},"abstract":"\n OBJECTIVE<\/jats:title>\n Pituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.<\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n A retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables\/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training\/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n The study included a total of 400 patients. The mean \u00b1 SD patient age was 53.9 \u00b1 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 \u00b1 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome\u2014major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death\u201431.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set\u2014sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing\u2019s disease.<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS<\/jats:title>\n Early postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.<\/jats:p>\n <\/jats:sec>","DOI":"10.3171\/2018.8.focus18268","type":"journal-article","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T09:13:48Z","timestamp":1542359628000},"page":"E8","source":"Crossref","is-referenced-by-count":76,"title":["A machine learning approach to predict early outcomes after pituitary adenoma surgery"],"prefix":"10.3171","volume":"45","author":[{"given":"Todd C.","family":"Hollon","sequence":"first","affiliation":[{"name":"Departments of Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adish","family":"Parikh","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Michigan, Ann Arbor, Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balaji","family":"Pandian","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Michigan, Ann Arbor, Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamaal","family":"Tarpeh","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Michigan, Ann Arbor, Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel A.","family":"Orringer","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariel L.","family":"Barkan","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery,"},{"name":"Internal Medicine, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin L.","family":"McKean","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery,"},{"name":"Otolaryngology, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen E.","family":"Sullivan","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","series-title":"J Am Med Inform Assoc","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1093\/jamia\/ocv110","article-title":"Real-time prediction of mortality, readmission, and length of stay using electronic health record data","volume":"23","author":"Cai X","year":"2016","unstructured":"Cai X, Perez-Concha O, Coiera E, Martin-Sanchez F, Day R, Roffe D, : Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J Am Med Inform Assoc 23:553\u2013561, 201610.1093\/jamia\/ocv11026374704"},{"key":"b2","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1227\/NEU.0000000000001011","article-title":"Outcome of transsphenoidal surgery for Cushing disease: a single-center experience over 32 years","volume":"78","author":"Chandler WF","year":"2016","unstructured":"Chandler WF, Barkan AL, Hollon T, Sakharova A, Sack J, Brahma B, : Outcome of transsphenoidal surgery for Cushing disease: a single-center experience over 32 years. Neurosurgery 78:216\u2013223, 20162634800710.1227\/NEU.0000000000001011"},{"key":"b3","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"817","DOI":"10.3171\/JNS\/2008\/109\/11\/0817","article-title":"Preoperative prognostic classification system for hemispheric low-grade gliomas in adults","volume":"109","author":"Chang EF","year":"2008","unstructured":"Chang EF, Smith JS, Chang SM, Lamborn KR, Prados MD, Butowski N, : Preoperative prognostic classification system for hemispheric low-grade gliomas in adults. J Neurosurg 109:817\u2013824, 200810.3171\/JNS\/2008\/109\/11\/081718976070"},{"key":"b4","series-title":"Nat Rev Endocrinol","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1038\/nrendo.2014.64","article-title":"Aggressive pituitary adenomas\u2014diagnosis and emerging treatments","volume":"10","author":"Di Ieva A","year":"2014","unstructured":"Di Ieva A, Rotondo F, Syro LV, Cusimano MD, Kovacs K: Aggressive pituitary adenomas\u2014diagnosis and emerging treatments. Nat Rev Endocrinol 10:423\u2013435, 201410.1038\/nrendo.2014.64"},{"key":"b5","series-title":"Machine Learning in Healthcare Informatics","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40017-9","year":"2014","unstructured":"Dua S, Acharya UR, Dua P (eds): Machine Learning in Healthcare Informatics. Berlin: Springer, 201410.1007\/978-3-642-40017-9"},{"key":"b6","series-title":"Int J Radiat Oncol Biol Phys","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/S0360-3016(96)00619-0","article-title":"Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials","volume":"37","author":"Gaspar L","year":"1997","unstructured":"Gaspar L, Scott C, Rotman M, Asbell S, Phillips T, Wasserman T, : Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys 37:745\u2013751, 1997912894610.1016\/S0360-3016(96)00619-0"},{"key":"b7","series-title":"Int J Radiat Oncol Biol Phys","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1016\/S0360-3016(00)00547-2","article-title":"Validation of the RTOG recursive partitioning analysis (RPA) classification for brain metastases","volume":"47","author":"Gaspar LE","year":"2000","unstructured":"Gaspar LE, Scott C, Murray K, Curran W: Validation of the RTOG recursive partitioning analysis (RPA) classification for brain metastases. Int J Radiat Oncol Biol Phys 47:1001\u20131006, 20001086307110.1016\/S0360-3016(00)00547-2"},{"key":"b8","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3171\/2014.10.JNS14372","article-title":"Endoscopic endonasal transsphenoidal surgery in elderly patients with pituitary adenomas","volume":"123","author":"Gondim JA","year":"2015","unstructured":"Gondim JA, Almeida JP, de Albuquerque LA, Gomes E, Schops M, Mota JI: Endoscopic endonasal transsphenoidal surgery in elderly patients with pituitary adenomas. J Neurosurg 123:31\u201338, 201510.3171\/2014.10.JNS1437225839926"},{"key":"b9","series-title":"J Biomed Inform","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.jbi.2015.05.003","article-title":"Supporting information retrieval from electronic health records: a report of University of Michigan\u2019s nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE)","volume":"55","author":"Hanauer DA","year":"2015","unstructured":"Hanauer DA, Mei Q, Law J, Khanna R, Zheng K: Supporting information retrieval from electronic health records: a report of University of Michigan\u2019s nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE). J Biomed Inform 55:290\u2013300, 20152597915310.1016\/j.jbi.2015.05.003"},{"key":"b10","series-title":"Clin Neurosurg","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1093\/neurosurgery\/16.CN_suppl_1.185","article-title":"Transphenoidal microsurgery of the normal and pathological pituitary","volume":"16","author":"Hardy J","year":"1969","unstructured":"Hardy J: Transphenoidal microsurgery of the normal and pathological pituitary. Clin Neurosurg 16:185\u2013217, 196910.1093\/neurosurgery\/16.CN_suppl_1.1855811707"},{"key":"b11","series-title":"Neurosurgery","first-page":"610","article-title":"Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings","volume":"33","author":"Knosp E","year":"1993","unstructured":"Knosp E, Steiner E, Kitz K, Matula C: Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings. Neurosurgery 33:610\u2013618, 19938232800"},{"key":"b12","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1227\/01.NEU.0000367555.16733.E1","article-title":"A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery","volume":"66","author":"Lawton MT","year":"2010","unstructured":"Lawton MT, Kim H, McCulloch CE, Mikhak B, Young WL: A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery. Neurosurgery 66:702\u2013713, 20102019066610.1227\/01.NEU.0000367555.16733.E1"},{"key":"b13","series-title":"Int J Radiat Oncol Biol Phys","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1016\/j.ijrobp.2010.06.012","article-title":"Validation and simplification of the Radiation Therapy Oncology Group recursive partitioning analysis classification for glioblastoma","volume":"81","author":"Li J","year":"2011","unstructured":"Li J, Wang M, Won M, Shaw EG, Coughlin C, Curran WJ Jr, : Validation and simplification of the Radiation Therapy Oncology Group recursive partitioning analysis classification for glioblastoma. Int J Radiat Oncol Biol Phys 81:623\u2013630, 201110.1016\/j.ijrobp.2010.06.01220888136"},{"key":"b14","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"12692","DOI":"10.1038\/s41598-017-12951-x","article-title":"Machine learning improves risk stratification after acute coronary syndrome","volume":"7","author":"Myers PD","year":"2017","unstructured":"Myers PD, Scirica BM, Stultz CM: Machine learning improves risk stratification after acute coronary syndrome. Sci Rep 7:12692, 20172897894810.1038\/s41598-017-12951-x"},{"key":"b15","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1227\/NEU.0b013e31828ea04b","article-title":"Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks","volume":"72","author":"Oermann EK","year":"2013","unstructured":"Oermann EK, Kress MA, Collins BT, Collins SP, Morris D, Ahalt SC, : Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks. Neurosurgery 72:944\u201352, 201310.1227\/NEU.0b013e31828ea04b23467250"},{"key":"b16","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"21161","DOI":"10.1038\/srep21161","article-title":"Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations","volume":"6","author":"Oermann EK","year":"2016","unstructured":"Oermann EK, Rubinsteyn A, Ding D, Mascitelli J, Starke RM, Bederson JB, : Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations. Sci Rep 6:21161, 201610.1038\/srep2116126856372"},{"issue":"Suppl 5","key":"b17","series-title":"Neuro Oncol","first-page":"v1","article-title":"CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2009\u20132013","volume":"18","author":"Ostrom QT","year":"2016","unstructured":"Ostrom QT, Gittleman H, Xu J, Kromer C, Wolinsky Y, Kruchko C, : CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2009\u20132013. Neuro Oncol 18 (Suppl 5):v1\u2013v75, 2016"},{"key":"b18","series-title":"Neuro Oncol","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1093\/neuonc\/not069","article-title":"A practical scoring system to determine whether to proceed with surgical resection in recurrent glioblastoma","volume":"15","author":"Park CK","year":"2013","unstructured":"Park CK, Kim JH, Nam DH, Kim CY, Chung SB, Kim YH, : A practical scoring system to determine whether to proceed with surgical resection in recurrent glioblastoma. Neuro Oncol 15:1096\u20131101, 20132380067710.1093\/neuonc\/not069"},{"issue":"3","key":"b19","series-title":"Neurosurg Focus","doi-asserted-by":"publisher","first-page":"E7","DOI":"10.3171\/foc.2007.23.3.9","article-title":"National trends, complications, and outcomes following transsphenoidal surgery for Cushing\u2019s disease from 1993 to 2002","volume":"23","author":"Patil CG","year":"2007","unstructured":"Patil CG, Lad SP, Harsh GR, Laws ER Jr, Boakye M: National trends, complications, and outcomes following transsphenoidal surgery for Cushing\u2019s disease from 1993 to 2002. Neurosurg Focus 23(3):E7, 200710.3171\/foc.2007.23.3.917961019"},{"key":"b20","series-title":"J Clin Oncol","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1200\/JCO.2002.08.121","article-title":"Prognostic factors for survival in adult patients with cerebral low-grade glioma","volume":"20","author":"Pignatti F","year":"2002","unstructured":"Pignatti F, van den Bent M, Curran D, Debruyne C, Sylvester R, Therasse P, : Prognostic factors for survival in adult patients with cerebral low-grade glioma. J Clin Oncol 20:2076\u20132084, 20021195626810.1200\/JCO.2002.08.121"},{"key":"b21","series-title":"NPJ Digit Med","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning for electronic health records","volume":"1","author":"Rajkomar A","year":"2018","unstructured":"Rajkomar A, Oren E, Chen K, Dai AM, Hajaj M, Liu PJ, : Scalable and accurate deep learning for electronic health records. NPJ Digit Med 1:18, 201810.1038\/s41746-018-0029-1"},{"key":"b22","series-title":"J Neurol Neurosurg Psychiatry","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1136\/jnnp.20.1.22","article-title":"The recurrence of intracranial meningiomas after surgical treatment","volume":"20","author":"Simpson D","year":"1957","unstructured":"Simpson D: The recurrence of intracranial meningiomas after surgical treatment. J Neurol Neurosurg Psychiatry 20:22\u201339, 195710.1136\/jnnp.20.1.2213406590"},{"key":"b23","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"476","DOI":"10.3171\/jns.1986.65.4.0476","article-title":"A proposed grading system for arteriovenous malformations","volume":"65","author":"Spetzler RF","year":"1986","unstructured":"Spetzler RF, Martin NA: A proposed grading system for arteriovenous malformations. J Neurosurg 65:476\u2013483, 198610.3171\/jns.1986.65.4.04763760956"},{"key":"b24","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3171\/2010.8.JNS10663","article-title":"A 3-tier classification of cerebral arteriovenous malformations. Clinical article","volume":"114","author":"Spetzler RF","year":"2011","unstructured":"Spetzler RF, Ponce FA: A 3-tier classification of cerebral arteriovenous malformations. Clinical article. J Neurosurg 114:842\u2013849, 201110.3171\/2010.8.JNS1066320932095"},{"key":"b25","series-title":"World Neurosurg","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.wneu.2017.05.162","article-title":"Intraoperative scoring system to predict postoperative remission in endoscopic endonasal transsphenoidal surgery for growth hormone-secreting pituitary adenomas","volume":"105","author":"Yano S","year":"2017","unstructured":"Yano S, Shinojima N, Kawashima J, Kondo T, Hide T: Intraoperative scoring system to predict postoperative remission in endoscopic endonasal transsphenoidal surgery for growth hormone-secreting pituitary adenomas. World Neurosurg 105:375\u2013385, 201710.1016\/j.wneu.2017.05.16228602880"},{"key":"b26","series-title":"Nat Commun","doi-asserted-by":"publisher","first-page":"12474","DOI":"10.1038\/ncomms12474","article-title":"Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features","volume":"7","author":"Yu KH","year":"2016","unstructured":"Yu KH, Zhang C, Berry GJ, Altman RB, R\u00e9 C, Rubin DL, : Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 7:12474, 20162752740810.1038\/ncomms12474"},{"key":"b1","series-title":"J Am Med Inform Assoc","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1093\/jamia\/ocv110","article-title":"Real-time prediction of mortality, readmission, and length of stay using electronic health record data","volume":"23","author":"Cai X","year":"2016","unstructured":"Cai X, Perez-Concha O, Coiera E, Martin-Sanchez F, Day R, Roffe D, : Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J Am Med Inform Assoc 23:553\u2013561, 201610.1093\/jamia\/ocv11026374704"},{"key":"b2","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1227\/NEU.0000000000001011","article-title":"Outcome of transsphenoidal surgery for Cushing disease: a single-center experience over 32 years","volume":"78","author":"Chandler WF","year":"2016","unstructured":"Chandler WF, Barkan AL, Hollon T, Sakharova A, Sack J, Brahma B, : Outcome of transsphenoidal surgery for Cushing disease: a single-center experience over 32 years. Neurosurgery 78:216\u2013223, 20162634800710.1227\/NEU.0000000000001011"},{"key":"b3","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"817","DOI":"10.3171\/JNS\/2008\/109\/11\/0817","article-title":"Preoperative prognostic classification system for hemispheric low-grade gliomas in adults","volume":"109","author":"Chang EF","year":"2008","unstructured":"Chang EF, Smith JS, Chang SM, Lamborn KR, Prados MD, Butowski N, : Preoperative prognostic classification system for hemispheric low-grade gliomas in adults. J Neurosurg 109:817\u2013824, 200810.3171\/JNS\/2008\/109\/11\/081718976070"},{"key":"b4","series-title":"Nat Rev Endocrinol","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1038\/nrendo.2014.64","article-title":"Aggressive pituitary adenomas\u2014diagnosis and emerging treatments","volume":"10","author":"Di Ieva A","year":"2014","unstructured":"Di Ieva A, Rotondo F, Syro LV, Cusimano MD, Kovacs K: Aggressive pituitary adenomas\u2014diagnosis and emerging treatments. Nat Rev Endocrinol 10:423\u2013435, 201410.1038\/nrendo.2014.64"},{"key":"b5","series-title":"Machine Learning in Healthcare Informatics","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40017-9","year":"2014","unstructured":"Dua S, Acharya UR, Dua P (eds): Machine Learning in Healthcare Informatics. Berlin: Springer, 201410.1007\/978-3-642-40017-9"},{"key":"b6","series-title":"Int J Radiat Oncol Biol Phys","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/S0360-3016(96)00619-0","article-title":"Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials","volume":"37","author":"Gaspar L","year":"1997","unstructured":"Gaspar L, Scott C, Rotman M, Asbell S, Phillips T, Wasserman T, : Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys 37:745\u2013751, 1997912894610.1016\/S0360-3016(96)00619-0"},{"key":"b7","series-title":"Int J Radiat Oncol Biol Phys","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1016\/S0360-3016(00)00547-2","article-title":"Validation of the RTOG recursive partitioning analysis (RPA) classification for brain metastases","volume":"47","author":"Gaspar LE","year":"2000","unstructured":"Gaspar LE, Scott C, Murray K, Curran W: Validation of the RTOG recursive partitioning analysis (RPA) classification for brain metastases. Int J Radiat Oncol Biol Phys 47:1001\u20131006, 20001086307110.1016\/S0360-3016(00)00547-2"},{"key":"b8","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3171\/2014.10.JNS14372","article-title":"Endoscopic endonasal transsphenoidal surgery in elderly patients with pituitary adenomas","volume":"123","author":"Gondim JA","year":"2015","unstructured":"Gondim JA, Almeida JP, de Albuquerque LA, Gomes E, Schops M, Mota JI: Endoscopic endonasal transsphenoidal surgery in elderly patients with pituitary adenomas. J Neurosurg 123:31\u201338, 201510.3171\/2014.10.JNS1437225839926"},{"key":"b9","series-title":"J Biomed Inform","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.jbi.2015.05.003","article-title":"Supporting information retrieval from electronic health records: a report of University of Michigan\u2019s nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE)","volume":"55","author":"Hanauer DA","year":"2015","unstructured":"Hanauer DA, Mei Q, Law J, Khanna R, Zheng K: Supporting information retrieval from electronic health records: a report of University of Michigan\u2019s nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE). J Biomed Inform 55:290\u2013300, 20152597915310.1016\/j.jbi.2015.05.003"},{"key":"b10","series-title":"Clin Neurosurg","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1093\/neurosurgery\/16.CN_suppl_1.185","article-title":"Transphenoidal microsurgery of the normal and pathological pituitary","volume":"16","author":"Hardy J","year":"1969","unstructured":"Hardy J: Transphenoidal microsurgery of the normal and pathological pituitary. Clin Neurosurg 16:185\u2013217, 196910.1093\/neurosurgery\/16.CN_suppl_1.1855811707"},{"key":"b11","series-title":"Neurosurgery","first-page":"610","article-title":"Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings","volume":"33","author":"Knosp E","year":"1993","unstructured":"Knosp E, Steiner E, Kitz K, Matula C: Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings. Neurosurgery 33:610\u2013618, 19938232800"},{"key":"b12","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1227\/01.NEU.0000367555.16733.E1","article-title":"A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery","volume":"66","author":"Lawton MT","year":"2010","unstructured":"Lawton MT, Kim H, McCulloch CE, Mikhak B, Young WL: A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery. Neurosurgery 66:702\u2013713, 20102019066610.1227\/01.NEU.0000367555.16733.E1"},{"key":"b13","series-title":"Int J Radiat Oncol Biol Phys","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1016\/j.ijrobp.2010.06.012","article-title":"Validation and simplification of the Radiation Therapy Oncology Group recursive partitioning analysis classification for glioblastoma","volume":"81","author":"Li J","year":"2011","unstructured":"Li J, Wang M, Won M, Shaw EG, Coughlin C, Curran WJ Jr, : Validation and simplification of the Radiation Therapy Oncology Group recursive partitioning analysis classification for glioblastoma. Int J Radiat Oncol Biol Phys 81:623\u2013630, 201110.1016\/j.ijrobp.2010.06.01220888136"},{"key":"b14","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"12692","DOI":"10.1038\/s41598-017-12951-x","article-title":"Machine learning improves risk stratification after acute coronary syndrome","volume":"7","author":"Myers PD","year":"2017","unstructured":"Myers PD, Scirica BM, Stultz CM: Machine learning improves risk stratification after acute coronary syndrome. Sci Rep 7:12692, 20172897894810.1038\/s41598-017-12951-x"},{"key":"b15","series-title":"Neurosurgery","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1227\/NEU.0b013e31828ea04b","article-title":"Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks","volume":"72","author":"Oermann EK","year":"2013","unstructured":"Oermann EK, Kress MA, Collins BT, Collins SP, Morris D, Ahalt SC, : Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks. Neurosurgery 72:944\u201352, 201310.1227\/NEU.0b013e31828ea04b23467250"},{"key":"b16","series-title":"Sci Rep","doi-asserted-by":"publisher","first-page":"21161","DOI":"10.1038\/srep21161","article-title":"Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations","volume":"6","author":"Oermann EK","year":"2016","unstructured":"Oermann EK, Rubinsteyn A, Ding D, Mascitelli J, Starke RM, Bederson JB, : Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations. Sci Rep 6:21161, 201610.1038\/srep2116126856372"},{"issue":"Suppl 5","key":"b17","series-title":"Neuro Oncol","first-page":"v1","article-title":"CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2009\u20132013","volume":"18","author":"Ostrom QT","year":"2016","unstructured":"Ostrom QT, Gittleman H, Xu J, Kromer C, Wolinsky Y, Kruchko C, : CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2009\u20132013. Neuro Oncol 18 (Suppl 5):v1\u2013v75, 2016"},{"key":"b18","series-title":"Neuro Oncol","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1093\/neuonc\/not069","article-title":"A practical scoring system to determine whether to proceed with surgical resection in recurrent glioblastoma","volume":"15","author":"Park CK","year":"2013","unstructured":"Park CK, Kim JH, Nam DH, Kim CY, Chung SB, Kim YH, : A practical scoring system to determine whether to proceed with surgical resection in recurrent glioblastoma. Neuro Oncol 15:1096\u20131101, 20132380067710.1093\/neuonc\/not069"},{"issue":"3","key":"b19","series-title":"Neurosurg Focus","doi-asserted-by":"publisher","first-page":"E7","DOI":"10.3171\/foc.2007.23.3.9","article-title":"National trends, complications, and outcomes following transsphenoidal surgery for Cushing\u2019s disease from 1993 to 2002","volume":"23","author":"Patil CG","year":"2007","unstructured":"Patil CG, Lad SP, Harsh GR, Laws ER Jr, Boakye M: National trends, complications, and outcomes following transsphenoidal surgery for Cushing\u2019s disease from 1993 to 2002. Neurosurg Focus 23(3):E7, 200710.3171\/foc.2007.23.3.917961019"},{"key":"b20","series-title":"J Clin Oncol","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1200\/JCO.2002.08.121","article-title":"Prognostic factors for survival in adult patients with cerebral low-grade glioma","volume":"20","author":"Pignatti F","year":"2002","unstructured":"Pignatti F, van den Bent M, Curran D, Debruyne C, Sylvester R, Therasse P, : Prognostic factors for survival in adult patients with cerebral low-grade glioma. J Clin Oncol 20:2076\u20132084, 20021195626810.1200\/JCO.2002.08.121"},{"key":"b21","series-title":"NPJ Digit Med","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning for electronic health records","volume":"1","author":"Rajkomar A","year":"2018","unstructured":"Rajkomar A, Oren E, Chen K, Dai AM, Hajaj M, Liu PJ, : Scalable and accurate deep learning for electronic health records. NPJ Digit Med 1:18, 201810.1038\/s41746-018-0029-1"},{"key":"b22","series-title":"J Neurol Neurosurg Psychiatry","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1136\/jnnp.20.1.22","article-title":"The recurrence of intracranial meningiomas after surgical treatment","volume":"20","author":"Simpson D","year":"1957","unstructured":"Simpson D: The recurrence of intracranial meningiomas after surgical treatment. J Neurol Neurosurg Psychiatry 20:22\u201339, 195710.1136\/jnnp.20.1.2213406590"},{"key":"b23","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"476","DOI":"10.3171\/jns.1986.65.4.0476","article-title":"A proposed grading system for arteriovenous malformations","volume":"65","author":"Spetzler RF","year":"1986","unstructured":"Spetzler RF, Martin NA: A proposed grading system for arteriovenous malformations. J Neurosurg 65:476\u2013483, 198610.3171\/jns.1986.65.4.04763760956"},{"key":"b24","series-title":"J Neurosurg","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3171\/2010.8.JNS10663","article-title":"A 3-tier classification of cerebral arteriovenous malformations. Clinical article","volume":"114","author":"Spetzler RF","year":"2011","unstructured":"Spetzler RF, Ponce FA: A 3-tier classification of cerebral arteriovenous malformations. Clinical article. J Neurosurg 114:842\u2013849, 201110.3171\/2010.8.JNS1066320932095"},{"key":"b25","series-title":"World Neurosurg","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.wneu.2017.05.162","article-title":"Intraoperative scoring system to predict postoperative remission in endoscopic endonasal transsphenoidal surgery for growth hormone-secreting pituitary adenomas","volume":"105","author":"Yano S","year":"2017","unstructured":"Yano S, Shinojima N, Kawashima J, Kondo T, Hide T: Intraoperative scoring system to predict postoperative remission in endoscopic endonasal transsphenoidal surgery for growth hormone-secreting pituitary adenomas. World Neurosurg 105:375\u2013385, 201710.1016\/j.wneu.2017.05.16228602880"},{"key":"b26","series-title":"Nat Commun","doi-asserted-by":"publisher","first-page":"12474","DOI":"10.1038\/ncomms12474","article-title":"Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features","volume":"7","author":"Yu KH","year":"2016","unstructured":"Yu KH, Zhang C, Berry GJ, Altman RB, R\u00e9 C, Rubin DL, : Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 7:12474, 20162752740810.1038\/ncomms12474"}],"container-title":["Neurosurgical Focus"],"original-title":[],"link":[{"URL":"https:\/\/thejns.org\/view\/journals\/neurosurg-focus\/45\/5\/article-pE8.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/neurosurg-focus\/45\/5\/article-pE8.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/neurosurg-focus\/45\/5\/article-pE8.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T08:53:02Z","timestamp":1780131182000},"score":1,"resource":{"primary":{"URL":"https:\/\/thejns.org\/view\/journals\/neurosurg-focus\/45\/5\/article-pE8.xml"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":52,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3171\/2018.8.focus18268","relation":{},"ISSN":["1092-0684"],"issn-type":[{"value":"1092-0684","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11]]}}}