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Neurosurgical Focus

Curated by expert guest editors, and enhanced by color images and video clips, Neurosurgical Focus covers a different neurosurgery-related topic in depth online each month. Each issue constitutes a state-of-the-art “textbook” chapter in neurosurgery.
• 2017 Impact Factor: 2.647

Volume 45: Issue 5 (Nov 2018): Predictive Analytics in Medicine

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Neurosurgical Focus

Introduction. Predictive analytics in medicine

By: Anthony L. Asher, Clinton J. Devin, Robert E. Harbaugh and Mohamad Bydon
Variability in the utility of predictive models in predicting patient-reported outcomes following spine surgery for degenerative conditions: a systematic review

By: Nicholas Dietz, Mayur Sharma, Ahmad Alhourani, Beatrice Ugiliweneza, Dengzhi Wang, Miriam A. Nuño, Doniel Drazin and Maxwell Boakye
Development of a validated computer-based preoperative predictive model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients

By: Justin K. Scheer, Taemin Oh, Justin S. Smith, Christopher I. Shaffrey, Alan H. Daniels, Daniel M. Sciubba, D. Kojo Hamilton, Themistocles S. Protopsaltis, Peter G. Passias, Robert A. Hart, Douglas C. Burton, Shay Bess, Renaud Lafage, Virginie Lafage, Frank Schwab, Eric O. Klineberg, Christopher P. Ames and the International Spine Study Group
Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study

By: Victor E. Staartjes, Carlo Serra, Giovanni Muscas, Nicolai Maldaner, Kevin Akeret, Christiaan H. B. van Niftrik, Jorn Fierstra, David Holzmann and Luca Regli
Erratum. Open-loop deep brain stimulation for the treatment of epilepsy: a systematic review of clinical outcomes over the past decade (2008–present)

By: Gillian Shasby
Letter to the Editor. Plasticity of the supplementary motor area

By: Lorenzo Nigro
Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury

By: Andrew T. Hale, David P. Stonko, Amber Brown, Jaims Lim, David J. Voce, Stephen R. Gannon, Truc M. Le and Chevis N. Shannon
Editorial. Artificial neural networks for neurosurgical diagnosis, prognosis, and management

By: Robert E. Harbaugh
Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging

By: Andrew T. Hale, David P. Stonko, Li Wang, Megan K. Strother and Lola B. Chambless
Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis

By: Anthony V. Nguyen, Elizabeth E. Blears, Evan Ross, Rishi R. Lall and Juan Ortega-Barnett
Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders

By: Aditya V. Karhade, Paul Ogink, Quirina Thio, Marike Broekman, Thomas Cha, William B. Gormley, Stuart Hershman, Wilco C. Peul, Christopher M. Bono and Joseph H. Schwab
Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning

By: Nikhil Paliwal, Prakhar Jaiswal, Vincent M. Tutino, Hussain Shallwani, Jason M. Davies, Adnan H. Siddiqui, Rahul Rai and Hui Meng
A machine learning approach to predict early outcomes after pituitary adenoma surgery

By: Todd C. Hollon, Adish Parikh, Balaji Pandian, Jamaal Tarpeh, Daniel A. Orringer, Ariel L. Barkan, Erin L. McKean and Stephen E. Sullivan
A predictive model and nomogram for predicting return to work at 3 months after cervical spine surgery: an analysis from the Quality Outcomes Database

By: Clinton J. Devin, Mohamad Bydon, Mohammed Ali Alvi, Panagiotis Kerezoudis, Inamullah Khan, Ahilan Sivaganesan, Matthew J. McGirt, Kristin R. Archer, Kevin T. Foley, Praveen V. Mummaneni, Erica F. Bisson, John J. Knightly, Christopher I. Shaffrey and Anthony L. Asher

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