@article {4573, title = {Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.}, journal = {J Neurooncol}, volume = {169}, year = {2024}, month = {2024 Aug}, pages = {175-185}, abstract = {

PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care.

METHODS: Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS >= 70).

RESULTS: The models achieved a nested cross-validation accuracy of 94.1\% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor.

CONCLUSION: The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor{\textquoteright}s location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.

}, keywords = {Adult, Aged, Brain, Brain Neoplasms, Female, Glioma, Humans, machine learning, Magnetic Resonance Imaging, Male, Middle Aged, Neoplasm Grading, Prognosis, Rest, Retrospective Studies}, issn = {1573-7373}, doi = {10.1007/s11060-024-04715-1}, author = {Luckett, Patrick H and Olufawo, Michael O and Park, Ki Yun and Lamichhane, Bidhan and Dierker, Donna and Verastegui, Gabriel Trevino and Lee, John J and Yang, Peter and Kim, Albert and Butt, Omar H and Chheda, Milan G and Snyder, Abraham Z and Shimony, Joshua S and Leuthardt, Eric C} } @article {4343, title = {Potential differences between monolingual and bilingual patients in approach and outcome after awake brain surgery.}, journal = {J Neurooncol}, volume = {148}, year = {2020}, month = {07/2020}, pages = {587-598}, abstract = {

INTRODUCTION: 20.8\% of the United States population and 67\% of the European population speak two or more languages. Intraoperative different languages, mapping, and localization are crucial. This investigation aims to address three questions between BL and ML patients: (1) Are there differences in complications (i.e. seizures) and DECS techniques during intra-operative brain mapping? (2) Is EOR different? and (3) Are there differences in the recovery pattern post-surgery?

METHODS: Data from 56 patients that underwent left-sided awake craniotomy for tumors infiltrating possible dominant hemisphere language areas from September 2016 to June 2019 were identified and analyzed in this study; 14 BL and 42 ML control patients. Patient demographics, education level, and the age of language acquisition were documented and evaluated. fMRI was performed on all participants.

RESULTS: 0 (0\%) BL and 3 (7\%) ML experienced intraoperative seizures (P = 0.73). BL patients received a higher direct DECS current in comparison to the ML patients (average = 4.7, 3.8, respectively, P = 0.03). The extent of resection was higher in ML patients in comparison to the BL patients (80.9 vs. 64.8, respectively, P = 0.04). The post-operative KPS scores were higher in BL patients in comparison to ML patients (84.3, 77.4, respectively, P = 0.03). BL showed lower drop in post-operative KPS in comparison to ML patients (- 4.3, - 8.7, respectively, P = 0.03).

CONCLUSION: We show that BL patients have a lower incidence of intra-operative seizures, lower EOR, higher post-operative KPS and tolerate higher DECS current, in comparison to ML patients.

}, keywords = {Brain Mapping, Brain Neoplasms, Craniotomy, Female, Follow-Up Studies, Glioma, Humans, Incidence, Language, Male, Middle Aged, Monitoring, Intraoperative, Prognosis, Retrospective Studies, Seizures, United States, Wakefulness}, issn = {1573-7373}, doi = {10.1007/s11060-020-03554-0}, author = {ReFaey, Karim and Tripathi, Shashwat and Bhargav, Adip G and Grewal, Sanjeet S and Middlebrooks, Erik H and Sabsevitz, David S and Jentoft, Mark and Peter Brunner and Wu, Adela and Tatum, William O and Ritaccio, Anthony and Chaichana, Kaisorn L and Quinones-Hinojosa, Alfredo} } @article {2197, title = {Passive real-time identification of speech and motor cortex during an awake craniotomy.}, journal = {Epilepsy Behav}, volume = {18}, year = {2010}, month = {05/2010}, pages = {123-8}, abstract = {

Precise localization of eloquent cortex is a clinical necessity prior to surgical resections adjacent to speech or\ motor\ cortex. In the intraoperative setting, this traditionally requires inducing temporary lesions by direct electrocortical stimulation (DECS). In an attempt to increase efficiency and potentially reduce the amount of necessary stimulation, we used a passive mapping procedure in the setting of an awake craniotomy for tumor in two patients resection. We recorded electrocorticographic (ECoG) signals from exposed cortex while patients performed simple cue-directed\ motor\ and speech tasks. SIGFRIED, a procedure for real-time event detection, was used to identify areas of\ cortical\ activation by detecting task-related modulations in the ECoG high gamma band. SIGFRIED{\textquoteright}s real-time output quickly localized\ motor\ and speech areas of cortex similar to those identified by DECS. In conclusion, real-time passive identification of\ cortical\ function using SIGFRIED may serve as a useful adjunct to\ cortical\ stimulation mapping in the intraoperative setting.

}, keywords = {Brain Mapping, Brain Neoplasms, Cerebral Cortex, Craniotomy, Electric Stimulation, Electroencephalography, Humans, Neurologic Examination}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2010.02.017}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20478745}, author = {Roland, Jarod and Peter Brunner and Johnston, James and Gerwin Schalk and Leuthardt, E C} }