Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

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's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.

Utilization of Augmented Reality Head-Mounted Display for the Surgical Management of Thoracolumbar Spinal Trauma.

Background and Objectives: Augmented reality head-mounted display (AR-HMD) is a novel technology that provides surgeons with a real-time CT-guided 3-dimensional recapitulation of a patient's spinal anatomy. In this case series, we explore the use of AR-HMD alongside more traditional robotic assistance in surgical spine trauma cases to determine their effect on operative costs and perioperative outcomes. Materials and Methods: We retrospectively reviewed trauma patients who underwent pedicle screw placement surgery guided by AR-HMD or robotic-assisted platforms at an academic tertiary care center between 1 January 2021 and 31 December 2022. Outcome distributions were compared using the Mann-Whitney U test. Results: The AR cohort (n = 9) had a mean age of 66 years, BMI of 29.4 kg/m2, Charlson Comorbidity Index (CCI) of 4.1, and Surgical Invasiveness Index (SII) of 8.8. In total, 77 pedicle screws were placed in this cohort. Intra-operatively, there was a mean blood loss of 378 mL, 0.78 units transfused, 398 min spent in the operating room, and a 20-day LOS. The robotic cohort (n = 13) had a mean age of 56 years, BMI of 27.1 kg/m2, CCI of 3.8, and SII of 14.2. In total, 128 pedicle screws were placed in this cohort. Intra-operatively, there was a mean blood loss of 432 mL, 0.46 units transfused units used, 331 min spent in the operating room, and a 10.4-day LOS. No significant difference was found between the two cohorts in any outcome metrics. Conclusions: Although the need to address urgent spinal conditions poses a significant challenge to the implementation of innovative technologies in spine surgery, this study represents an initial effort to show that AR-HMD can yield comparable outcomes to traditional robotic surgical techniques. Moreover, it highlights the potential for AR-HMD to be readily integrated into Level 1 trauma centers without requiring extensive modifications or adjustments.

Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.

PURPOSE: Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care. METHODS: GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models. RESULTS: The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks. CONCLUSION: We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.

Glioblastoma induces whole-brain spectral change in resting state fMRI: Associations with clinical comorbidities and overall survival.

Glioblastoma, a highly aggressive form of brain tumor, is a brain-wide disease. We evaluated the impact of tumor burden on whole brain resting-state functional magnetic resonance imaging (rs-fMRI) activity. Specifically, we analyzed rs-fMRI signals in the temporal frequency domain in terms of the power-law exponent and fractional amplitude of low-frequency fluctuations (fALFF). We contrasted 189 patients with newly-diagnosed glioblastoma versus 189 age-matched healthy reference participants from an external dataset. The patient and reference datasets were matched for age and head motion. The principal finding was markedly flatter spectra and reduced grey matter fALFF in the patients as compared to the reference dataset. We posit that the whole-brain spectral change is attributable to global dysregulation of excitatory and inhibitory balance and metabolic demand in the tumor-bearing brain. Additionally, we observed that clinical comorbidities, in particular, seizures, and MGMT promoter methylation, were associated with flatter spectra. Notably, the degree of change in spectra was predictive of overall survival. Our findings suggest that frequency domain analysis of rs-fMRI activity provides prognostic information in glioblastoma patients and offers a means of noninvasively studying the effects of glioblastoma on the whole brain.

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival.

Background: Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients. Methods: We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients' associated changes in CT as a potential biomarker of overall survival. Results: Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients' cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%. Conclusions: These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients.

Rapid Implementation of a 3-Dimensional-Printed Patient-Specific Titanium Sacrum Implant for Severe Neuropathic Spinal Arthropathy and Guide to Compassionate US Regulatory Approval.

BACKGROUND AND OBJECTIVE: Rapid design and production of patient-specific 3-dimensional-printed implants (3DPIs) present a novel opportunity to restore the biomechanically demanding integrity of the lumbopelvic junction. We present a unique case of a 61-year-old patient with severe neuropathic spinal arthropathy (Charcot spine) who initially underwent a T4-to-sacrum spinal fusion. Massive bone destruction led to dissociation of his upper body from his pelvis and legs. Reconstruction of the spinopelvic continuity was planned with the aid of a personalized lumbosacral 3DPI. METHOD: Using high-resolution computed tomography scans, the custom 3DPI was made using additive titanium manufacturing. The unique 3DPI consisted of (1) a sacral platform with iliac screws, (2) modular corpectomy device with rigid connection to the sacral platform, and (3) anterior plate connection with screws for proximal fixation. The procedures to obtain compassionate use Food and Drug Administration approval were followed. The patient underwent debridement of a chronically open wound before undertaking the 3-stage reconstructive procedure. The custom 3DPI and additional instrumentation were inserted as part of a salvage rebuilding procedure. RESULTS: The chronology of the rapid implementation of the personalized sacral 3DPI from decision, design, manufacturing, Food and Drug Administration approval, and surgical execution lasted 28 days. The prosthesis was positioned in the defect according to the expected anatomic planes and secured using a screw-rod system and a vascularized fibular bone strut graft. The prosthesis provided an ideal repair of the lumbosacral junction and pelvic ring by merging spinal pelvic fixation, posterior pelvic ring fixation, and anterior spinal column fixation. CONCLUSION: To the best of our knowledge, this is the first case of a multilevel lumbar, sacral, and sacropelvic neuropathic (Charcot) spine reconstruction using a 3DPI sacral prosthesis. As the prevalence of severe spine deformities continues to increase, adoption of 3DPIs is becoming more relevant to offer personalized treatment for complex deformities.

Cognition and Brain System Segregation in Pediatric Brain Tumor Patients Treated with Proton Therapy.

Purpose: Pediatric brain tumor patients often experience significant cognitive sequelae. Resting-state functional MRI (rsfMRI) provides a measure of brain network organization, and we hypothesize that pediatric brain tumor patients treated with proton therapy will demonstrate abnormal brain network architecture related to cognitive outcome and radiation dosimetry. Participants and Methods: Pediatric brain tumor patients treated with proton therapy were enrolled on a prospective study of cognitive assessment using the NIH Toolbox Cognitive Domain. rsfMRI was obtained in participants able to complete unsedated MRI. Brain system segregation (BSS), a measure of brain network architecture, was calculated for the whole brain, the high-level cognition association systems, and the sensory-motor systems. Results: Twenty-six participants were enrolled in the study for cognitive assessment, and 18 completed rsfMRI. There were baseline cognitive deficits in attention and inhibition and processing speed prior to radiation with worsening performance over time in multiple domains. Average BSS across the whole brain was significantly decreased in participants compared with healthy controls (1.089 and 1.101, respectively; P = 0.001). Average segregation of association systems was significantly lower in participants than in controls (P < 0.001) while there was no difference in the sensory motor networks (P = 0.70). Right hippocampus dose was associated with worse attention and inhibition (P < 0.05) and decreased segregation in the dorsal attention network (P < 0.05). Conclusion: Higher mean dose to the right hippocampus correlated with worse dorsal attention network segregation and worse attention and inhibition cognitive performance. Patients demonstrated alterations in brain network organization of association systems measured with rsfMRI; however, somatosensory system segregation was no different from healthy children. Further work with preradiation rsfMRI is needed to assess the effects of surgery and presence of a tumor on brain network architecture.

Making Meaningful Use of Price Transparency Data: Describing Price Variation of Spine Surgery and Imaging in a Single System.

Price transparency is an increasingly popular solution for high healthcare expenditures in the United States, but little is known about its potential to facilitate patient price shopping. Our objective was to analyze interhospital and interpayer price variability in spine surgery and spine imaging using newly public payer-specific negotiated charges (PNCs). We selected a subset of billing codes for spine surgery and spine imaging at 12 hospitals within a Saint Louis metropolitan area healthcare system. We then compared PNCs for these procedures and tested for significant differences in interhospital and interinsurer IQR using the Mann-Whitney U Test. We found significantly greater IQRs of PNCs as a factor of the insurance plan than as a factor of the hospital for cervical spinal fusions (interinsurer IQR $8256; interhospital IQR $533; P < .0001), noncervical spinal fusions (interinsurer IQR $28 423; interhospital IQR $5512; P < .001), computed tomographies of the lower spine (interinsurer IQR $595; interhospital IQR $113; P < .0001), and MRIs lower spinal canal (interinsurer IQR $1010; interhospital IQR $158; P < .0001). There was no significant difference between the interinsurer IQR and the interhospital IQR for lower spine x-rays (interinsurer IQR $107; interhospital IQR $67; P = .0543). Despite some between-hospital heterogeneity, we show significantly higher price variability between insurers than between hospitals. Our single system analysis limits our ability to generalize, but our results suggest that savings depend more on hospital and provider negotiations than patient price shopping, given the difficulty of switching insurers.

Combination laser interstitial thermal therapy plus stereotactic radiotherapy increases time to progression for biopsy-proven recurrent brain metastases.

Background: Improved survival for patients with brain metastases has been accompanied by a rise in tumor recurrence after stereotactic radiotherapy (SRT). Laser interstitial thermal therapy (LITT) has emerged as an effective treatment for SRT failures as an alternative to open resection or repeat SRT. We aimed to evaluate the efficacy of LITT followed by SRT (LITT+SRT) in recurrent brain metastases. Methods: A multicenter, retrospective study was performed of patients who underwent treatment for biopsy-proven brain metastasis recurrence after SRT at an academic medical center. Patients were stratified by "planned LITT+SRT" versus "LITT alone" versus "repeat SRT alone." Index lesion progression was determined by modified Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. Results: Fifty-five patients met inclusion criteria, with a median follow-up of 7.3 months (range: 1.0-30.5), age of 60 years (range: 37-86), Karnofsky Performance Status (KPS) of 80 (range: 60-100), and pre-LITT/biopsy contrast-enhancing volume of 5.7 cc (range: 0.7-19.4). Thirty-eight percent of patients underwent LITT+SRT, 45% LITT alone, and 16% SRT alone. Median time to index lesion progression (29.8, 7.5, and 3.7 months [P = .022]) was significantly improved with LITT+SRT. When controlling for age in a multivariate analysis, patients treated with LITT+SRT remained significantly less likely to have index lesion progression (P = .004). Conclusions: These data suggest that LITT+SRT is superior to LITT or repeat SRT alone for treatment of biopsy-proven brain metastasis recurrence after SRT failure. Prospective trials are warranted to validate the efficacy of using combination LITT+SRT for treatment of recurrent brain metastases.