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Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis

Haosu Zhang, Kartikay Tehlan, Sebastian Ille, Maximilian Schwendner, Zhenyu Gong, Axel Schroeder, Bernhard Meyer, and Sandro M. Krieg

OBJECTIVE

Language-related networks have been recognized in functional maintenance, which has also been considered the mechanism of plasticity and reorganization in patients with cerebral malignant tumors. However, the role of interhemispheric connections (ICs) in language restoration remains unclear at the network level. Navigated transcranial magnetic stimulation (nTMS) and diffusion tensor imaging fiber tracking data were used to identify language-eloquent regions and their corresponding subcortical structures, respectively.

METHODS

Preoperative image–based IC networks and nTMS mapping data from 30 patients without preoperative and postoperative aphasia as the nonaphasia group, 30 patients with preoperative and postoperative aphasia as the glioma-induced aphasia (GIA) group, and 30 patients without preoperative aphasia but who developed aphasia after the operation as the surgery-related aphasia group were investigated using fully connected layer-based deep learning (FC-DL) analysis to weight ICs.

RESULTS

GIA patients had more weighted ICs than the patients in the other groups. Weighted ICs between the left precuneus and right paracentral lobule, and between the left and right cuneus, were significantly different among these three groups. The FC-DL approach for modeling functional and structural connectivity was also tested for its potential to predict postoperative language levels, and both the achieved sensitivity and specificity were greater than 70%. Weighted IC was reorganized more in GIA patients to compensate for language loss.

CONCLUSIONS

The authors’ method offers a new perspective to investigate brain structural organization and predict functional prognosis.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Introduction. Machine learning in neurosurgery: transitioning to a new era of contemporary medicine

Mohamad Bydon, John H. Shin, Shelly D. Timmons, and Eric A. Potts

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph

Andrew Abumoussa, Vivek Gopalakrishnan, Benjamin Succop, Michael Galgano, Sivakumar Jaikumar, Yueh Z. Lee, and Deb A. Bhowmick

OBJECTIVE

The goal of this work was to methodically evaluate, optimize, and validate a self-supervised machine learning algorithm capable of real-time automatic registration and fluoroscopic localization of the spine using a single radiograph or fluoroscopic frame.

METHODS

The authors propose a two-dimensional to three-dimensional (2D-3D) registration algorithm that maximizes an image similarity metric between radiographic images to identify the position of a C-arm relative to a 3D volume. This work utilizes digitally reconstructed radiographs (DRRs), which are synthetic radiographic images generated by simulating the x-ray projections as they would pass through a CT volume. To evaluate the algorithm, the authors used cone-beam CT data for 127 patients obtained from an open-source de-identified registry of cervical, thoracic, and lumbar scans. They systematically evaluated and tuned the algorithm, then quantified the convergence rate of the model by simulating C-arm registrations with 80 randomly simulated DRRs for each CT volume. The endpoints of this study were time to convergence, accuracy of convergence for each of the C-arm’s degrees of freedom, and overall registration accuracy based on a voxel-by-voxel measurement.

RESULTS

A total of 10,160 unique radiographic images were simulated from 127 CT scans. The algorithm successfully converged to the correct solution 82% of the time with an average of 1.96 seconds of computation. The radiographic images for which the algorithm converged to the solution demonstrated 99.9% registration accuracy despite utilizing only single-precision computation for speed. The algorithm was found to be optimized for convergence when the search space was limited to a ± 45° offset in the right anterior oblique/left anterior oblique, cranial/caudal, and receiver rotation angles with the radiographic isocenter contained within 8000 cm3 of the volumetric center of the CT volume.

CONCLUSIONS

The investigated machine learning algorithm has the potential to aid surgeons in level localization, surgical planning, and intraoperative navigation through a completely automated 2D-3D registration process. Future work will focus on algorithmic optimizations to improve the convergence rate and speed profile.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Machine learning predictive models in neurosurgery: an appraisal based on the TRIPOD guidelines. Systematic review

Anmol Warman, Anita L. Kalluri, and Tej D. Azad

OBJECTIVE

In recent years, machine learning models for clinical prediction have become increasingly prevalent in the neurosurgical literature. However, little is known about the quality of these models, and their translation to clinical care has been limited. The aim of this systematic review was to empirically determine the adherence of machine learning models in neurosurgery with standard reporting guidelines specific to clinical prediction models.

METHODS

Studies describing the development or validation of machine learning predictive models published between January 1, 2020, and January 10, 2023, across five neurosurgery journals (Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, Neurosurgery, and World Neurosurgery) were included. Studies where the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were not applicable, radiomic studies, and natural language processing studies were excluded.

RESULTS

Forty-seven studies featuring a machine learning–based predictive model in neurosurgery were included. The majority (53%) of studies were single-center studies, and only 15% of studies externally validated the model in an independent cohort of patients. The median compliance across all 47 studies was 82.1% (IQR 75.9%–85.7%). Giving details of treatment (n = 17 [36%]), including the number of patients with missing data (n = 11 [23%]), and explaining the use of the prediction model (n = 23 [49%]) were identified as the TRIPOD criteria with the lowest rates of compliance.

CONCLUSIONS

Improved adherence to TRIPOD guidelines will increase transparency in neurosurgical machine learning predictive models and streamline their translation into clinical care.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Metabolomic differentiation of tumor core versus edge in glioma

Mary E. Baxter, Hunter A. Miller, Joseph Chen, Brian J. Williams, and Hermann B. Frieboes

OBJECTIVE

Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between the glioma core (inner) and edge (infiltrating) regions. This proof-of-concept study differentiates metabolic signatures associated with these regions, with the potential for prognosis and targeted therapy that could improve surgical outcomes.

METHODS

Paired glioma core and infiltrating edge samples were obtained from 27 patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples and metabolomic data were obtained via 2D liquid chromatography–mass spectrometry/mass spectrometry. To gauge the potential of metabolomics to identify clinically relevant predictors of survival from tumor core versus edge tissues, a boosted generalized linear machine learning model was used to predict metabolomic profiles associated with O -methylguanine DNA methyltransferase (MGMT) promoter methylation.

RESULTS

A panel of 66 (of 168) metabolites was found to significantly differ between glioma core and edge regions (p ≤ 0.05). Top metabolites with significantly different relative abundances included DL-alanine, creatine, cystathionine, nicotinamide, and D-pantothenic acid. Significant metabolic pathways identified by quantitative enrichment analysis included glycerophospholipid metabolism; butanoate metabolism; cysteine and methionine metabolism; glycine, serine, alanine, and threonine metabolism; purine metabolism; nicotinate and nicotinamide metabolism; and pantothenate and coenzyme A biosynthesis. The machine learning model using 4 key metabolites each within core and edge tissue specimens predicted MGMT promoter methylation status, with AUROCEdge = 0.960 and AUROCCore = 0.941. Top metabolites associated with MGMT status in the core samples included hydroxyhexanoycarnitine, spermine, succinic anhydride, and pantothenic acid, and in the edge samples metabolites included 5-cytidine monophosphate, pantothenic acid, itaconic acid, and uridine.

CONCLUSIONS

Key metabolic differences are identified between core and edge tissue in glioma and, furthermore, demonstrate the potential for machine learning to provide insight into potential prognostic and therapeutic targets.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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MRI-based detection of cervical ossification of the posterior longitudinal ligament using a novel automated machine learning diagnostic tool

Shachar Shemesh, Gil Kimchi, Gal Yaniv, and Ran Harel

OBJECTIVE

Currently, CT is considered the gold standard for the diagnosis of ossification of the posterior longitudinal ligament (OPLL). The objective of this study was to develop artificial intelligence (AI) software and a validated model for the identification and representation of cervical OPLL (C-OPLL) on MRI, obviating the need for spine CT.

METHODS

A retrospective evaluation was performed of consecutive imaging studies of all adult patients who underwent both cervical CT and MRI for any clinical indication within a span of 36 months (between January 2017 and July 2020) in a single tertiary-care referral hospital. C-OPLL was identified by a panel of neurosurgeons and a neuroradiologist. MATLAB software was then used to create an AI tool for the diagnosis of C-OPLL by using a convolutional neural network method to identify features on MR images. A reader study was performed to compare the performance of the AI model to that of the diagnostic panel using standard test performance metrics. Interobserver variability was assessed using Cohen’s kappa score.

RESULTS

Nine hundred consecutive patients were found to be eligible for radiological evaluation, yielding 65 identified C-OPLL carriers. The AI model, utilizing MR images, was able to accurately segment the vertebral bodies, PLL, and discoligamentous complex, and detect C-OPLL carriers. The AI model identified 5 additional C-OPLL patients who were not initially detected. The performance of the MRI-based AI model resulted in a sensitivity of 85%, specificity of 98%, negative predictive value of 98%, and positive predictive value of 85%. The overall accuracy of the model was 98%, with a kappa score of 0.917.

CONCLUSIONS

The novel AI software developed in this study was highly specific for identifying C-OPLL on MRI, without the use of CT. This model may obviate the need for CT scans while maintaining adequate diagnostic accuracy. With further development, this MRI-based AI model has the potential to aid in the diagnosis of various spinal disorders and its automated layers may lay the foundation for MRI-specific diagnostic criteria for C-OPLL.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Quantification of motion during microvascular anastomosis simulation using machine learning hand detection

Nicolas I. Gonzalez-Romo, Sahin Hanalioglu, Giancarlo Mignucci-Jiménez, Grant Koskay, Irakliy Abramov, Yuan Xu, Wonhyoung Park, Michael T. Lawton, and Mark C. Preul

OBJECTIVE

Microanastomosis is one of the most technically demanding and important microsurgical skills for a neurosurgeon. A hand motion detector based on machine learning tracking technology was developed and implemented for performance assessment during microvascular anastomosis simulation.

METHODS

A microanastomosis motion detector was developed using a machine learning model capable of tracking 21 hand landmarks without physical sensors attached to a surgeon’s hands. Anastomosis procedures were simulated using synthetic vessels, and hand motion was recorded with a microscope and external camera. Time series analysis was performed to quantify the economy, amplitude, and flow of motion using data science algorithms. Six operators with various levels of technical expertise (2 experts, 2 intermediates, and 2 novices) were compared.

RESULTS

The detector recorded a mean (SD) of 27.6 (1.8) measurements per landmark per second with a 10% mean loss of tracking for both hands. During 600 seconds of simulation, the 4 nonexperts performed 26 bites in total, with a combined excess of motion of 14.3 (15.5) seconds per bite, whereas the 2 experts performed 33 bites (18 and 15 bites) with a mean (SD) combined excess of motion of 2.8 (2.3) seconds per bite for the dominant hand. In 180 seconds, the experts performed 13 bites, with mean (SD) latencies of 22.2 (4.4) and 23.4 (10.1) seconds, whereas the 2 intermediate operators performed a total of 9 bites with mean (SD) latencies of 31.5 (7.1) and 34.4 (22.1) seconds per bite.

CONCLUSIONS

A hand motion detector based on machine learning technology allows the identification of gross and fine movements performed during microanastomosis. Economy, amplitude, and flow of motion were measured using time series data analysis. Technical expertise could be inferred from such quantitative performance analysis.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Radiomic signatures of meningiomas using the Ki-67 proliferation index as a prognostic marker of clinical outcomes

Omaditya Khanna, Anahita Fathi Kazerooni, Sherjeel Arif, Aria Mahtabfar, Arbaz A. Momin, Carrie E. Andrews, Karim Hafazalla, Michael P. Baldassari, Lohit Velagapudi, Jose A. Garcia, Chiharu Sako, Christopher J. Farrell, James J. Evans, Kevin D. Judy, David W. Andrews, Adam E. Flanders, Wenyin Shi, and Christos Davatzikos

OBJECTIVE

The clinical behavior of meningiomas is not entirely captured by its designated WHO grade, therefore other factors must be elucidated that portend increased tumor aggressiveness and associated risk of recurrence. In this study, the authors identify multiparametric MRI radiomic signatures of meningiomas using Ki-67 as a prognostic marker of clinical outcomes independent of WHO grade.

METHODS

A retrospective analysis was conducted of all resected meningiomas between 2012 and 2018. Preoperative MR images were used for high-throughput radiomic feature extraction and subsequently used to develop a machine learning algorithm to stratify meningiomas based on Ki-67 indices < 5% and ≥ 5%, independent of WHO grade. Progression-free survival (PFS) was assessed based on machine learning prediction of Ki-67 strata and compared with outcomes based on histopathological Ki-67.

RESULTS

Three hundred forty-three meningiomas were included: 291 with WHO grade I, 43 with grade II, and 9 with grade III. The overall rate of recurrence was 19.8% (15.1% in grade I, 44.2% in grade II, and 77.8% in grade III) over a median follow-up of 28.5 months. Grade II and III tumors had higher Ki-67 indices than grade I tumors, albeit tumor and peritumoral edema volumes had considerable variation independent of meningioma WHO grade. Forty-six high-performing radiomic features (1 morphological, 7 intensity-based, and 38 textural) were identified and used to build a support vector machine model to stratify tumors based on a Ki-67 cutoff of 5%, with resultant areas under the curve of 0.83 (95% CI 0.78–0.89) and 0.84 (95% CI 0.75–0.94) achieved for the discovery (n = 257) and validation (n = 86) data sets, respectively. Comparison of histopathological Ki-67 versus machine learning–predicted Ki-67 showed excellent performance (overall accuracy > 80%), with classification of grade I meningiomas exhibiting the greatest accuracy. Prediction of Ki-67 by machine learning classifier revealed shorter PFS for meningiomas with Ki-67 indices ≥ 5% compared with tumors with Ki-67 < 5% (p < 0.0001, log-rank test), which corroborates divergent patient outcomes observed using histopathological Ki-67.

CONCLUSIONS

The Ki-67 proliferation index may serve as a surrogate marker of increased meningioma aggressiveness independent of WHO grade. Machine learning using radiomic feature analysis may be used for the preoperative prediction of meningioma Ki-67, which provides enhanced analytical insights to help improve diagnostic classification and guide patient-specific treatment strategies.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Surgical classification using natural language processing of informed consent forms in spine surgery

Michael D. Shost, Seth M. Meade, Michael P. Steinmetz, Thomas E. Mroz, and Ghaith Habboub

OBJECTIVE

In clinical spine surgery research, manually reviewing surgical forms to categorize patients by their surgical characteristics is a crucial yet time-consuming task. Natural language processing (NLP) is a machine learning tool used to adaptively parse and categorize important features from text. These systems function by training on a large, labeled data set in which feature importance is learned prior to encountering a previously unseen data set. The authors aimed to design an NLP classifier for surgical information that can review consent forms and automatically classify patients by the surgical procedure performed.

METHODS

Thirteen thousand two hundred sixty-eight patients who underwent 15,227 surgeries from January 1, 2012, to December 31, 2022, at a single institution were initially considered for inclusion. From these surgeries, 12,239 consent forms were classified based on the Current Procedural Terminology (CPT) code, categorizing them into 7 of the most frequently performed spine surgeries at this institution. This labeled data set was split 80%/20% into train and test subsets, respectively. The NLP classifier was then trained and the results demonstrated its performance on the test data set using CPT codes to determine accuracy.

RESULTS

This NLP surgical classifier had an overall weighted accuracy rate of 91% for sorting consents into correct surgical categories. Anterior cervical discectomy and fusion had the highest positive predictive value (PPV; 96.8%), whereas lumbar microdiscectomy had the lowest PPV in the testing data (85.0%). Sensitivity was highest for lumbar laminectomy and fusion (96.7%) and lowest for the least common operation, cervical posterior foraminotomy (58.3%). Negative predictive value and specificity were > 95% for all surgical categories.

CONCLUSIONS

Utilizing NLP for text classification drastically improves the efficiency of classifying surgical procedures for research purposes. The ability to quickly classify surgical data can be significantly beneficial to institutions without a large database or substantial data review capabilities, as well as for trainees to track surgical experience, or practicing surgeons to evaluate and analyze their surgical volume. Additionally, the capability to quickly and accurately recognize the type of surgery will facilitate the extraction of new insights from the correlations between surgical interventions and patient outcomes. As the database of surgical information grows from this institution and others in spine surgery, the accuracy, usability, and applications of this model will continue to increase.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery

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Using machine learning to predict 30-day readmission and reoperation following resection of supratentorial high-grade gliomas: an ACS NSQIP study involving 9418 patients

Abdul Karim Ghaith, Marc Ghanem, Cameron Zamanian, Antonio A. Bon-Nieves, Archis Bhandarkar, Karim Nathani, Mohamad Bydon, and Alfredo Quiñones-Hinojosa

OBJECTIVE

High-grade gliomas (HGGs) are among the rarest yet most aggressive tumor types in neurosurgical practice. In the current literature, few studies have assessed the drivers of early outcomes following resection of these tumors and investigated their association with quality of care. The authors aimed to identify the clinical predictors for 30-day readmission and reoperation following HGG surgery using the American College of Surgeons (ACS) National Surgical Quality Improvement Project (NSQIP) database and sought to create web-based applications predicting each outcome.

METHODS

Using the ACS NSQIP database, the authors conducted a retrospective, multicenter cohort analysis of patients who underwent resection of supratentorial HGGs between January 1, 2016, and December 31, 2020. Demographics and comorbidities were extracted. The primary outcomes were 30-day unplanned readmission and reoperation. A stratified 80:20 split of the available data was carried out. Supervised machine learning algorithms were trained to predict 30-day outcomes.

RESULTS

A total of 9418 patients were included in our cohort. The observed rate of unplanned readmission within 30 days of surgery was 13.0% (n = 1221). In terms of predictors, weight, chronic steroid use, preoperative blood urea nitrogen level, and white blood cell count were associated with a higher risk of readmission. The observed rate of unplanned reoperation within 30 days of surgery was 5.2% (n = 489). In terms of predictors, increased weight, longer operative time, and more days between hospital admission and operation were associated with an increased risk of early reoperation. The random forest algorithm showed the highest predictive performance for early readmission (area under the curve [AUC] = 0.967), while the XGBoost algorithm showed the highest predictive performance for early reoperation (AUC = 0.985). Web-based tools for both outcomes were deployed (https://glioma-readmission.herokuapp.com/ and https://glioma-reoperation.herokuapp.com/).

CONCLUSIONS

In this study, the authors provide the first nationwide analysis for short-term outcomes in patients undergoing resection of supratentorial HGGs. Multiple patient, hospital, and admission factors were associated with readmission and reoperation, confirmed by machine learning predicting patients’ prognosis, leading to better planning preoperatively and subsequently improved personalized patient care.

In Neurosurgical Focus Volume 54 (2023): Issue 6 (Jun 2023): Machine Learning in Neurosurgery