Medicine and Health Sciences Commons^™

Graph Theoretical Measures Of Fast Ripple Networks Improve The Accuracy Of Post-Operative Seizure Outcome Prediction, Shennan A Weiss, Itzhak Fried, Chengyuan Wu, Ashwini Sharan, Daniel Y. Rubinstein, Jerome Engel, Michael R Sperling, Richard J Staba

Department of Neurosurgery Faculty Papers

Fast ripples (FR) are a biomarker of epileptogenic brain, but when larger portions of FR generating regions are resected seizure freedom is not always achieved. To evaluate and improve the diagnostic accuracy of FR resection for predicting seizure freedom we compared the FR resection ratio (RR) with FR network graph theoretical measures. In 23 patients FR were semi-automatically detected and quantified in stereo EEG recordings during sleep. MRI normalization and co-registration localized contacts and relation to resection margins. The number of FR, and graph theoretical measures, which were spatial (i.e., FR rate-distance radius) or temporal correlational (i.e., FR mutual information), …

Discrepancies In Stroke Distribution And Dataset Origin In Machine Learning For Stroke., Lohit Velagapudi, Nikolaos Mouchtouris, Michael P Baldassari, David Nauheim, Omaditya Khanna, Fadi Al Saiegh, Nabeel Herial, M Reid Gooch, Stavropoula Tjoumakaris, Robert H Rosenwasser, Pascal Jabbour

Department of Neurosurgery Faculty Papers

BACKGROUND: Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities.

AIMS: 582 studies were identified on initial searching of the PubMed database. Of these studies, 106 full texts were assessed after title and abstract screening which resulted in 489 papers excluded. Of these 106 studies, …

Molecular-Based Recursive Partitioning Analysis Model For Glioblastoma In The Temozolomide Era: A Correlative Analysis Based On Nrg Oncology Rtog 0525., Erica Hlavin Bell, Stephanie L Pugh, Joseph P. Mcelroy, Mark R. Gilbert, Minesh Mehta, Alexander C Klimowicz, Anthony Magliocco, Markus Bredel, Pierre Robe, Anca L. Grosu, Roger Stupp, Walter Curran, Aline P. Becker, Andrea L. Salavaggione, Jill S. Barnholtz-Sloan, Kenneth Aldape, Deborah T. Blumenthal, Paul D. Brown, Jon Glass, Luis Souhami, R. Jeffrey Lee, David Brachman, John Flickinger, Minhee Won, Arnab Chakravarti

Department of Neurosurgery Faculty Papers

Importance: There is a need for a more refined, molecularly based classification model for glioblastoma (GBM) in the temozolomide era.

Objective: To refine the existing clinically based recursive partitioning analysis (RPA) model by incorporating molecular variables.

Design, Setting, and Participants: NRG Oncology RTOG 0525 specimens (n = 452) were analyzed for protein biomarkers representing key pathways in GBM by a quantitative molecular microscopy-based approach with semiquantitative immunohistochemical validation. Prognostic significance of each protein was examined by single-marker and multimarker Cox regression analyses. To reclassify the prognostic risk groups, significant protein biomarkers on single-marker analysis were incorporated into an RPA model …