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Abstract INTRODUCTION Glioblastoma (GBM) is the most common primary malignant brain tumor and has a poor clinical outcome. Extracellular vesicles (EVs) are promising biomarkers to assist the differential diagnosis between pseudoprogression and true tumor progression. We aimed to identify non-tumor plasma EV markers that predicted GBM occurrence using trained multiple tree-based machine learning algorithms. METHODS We included plasma samples of 40 GBM and 40 non-GBM (20 brain metastasis, 20 normal donors). Spectral flow cytometry was performed to determine plasma EV subpopulation frequency based on CD9,CD81,CD63,CD11b,CD45,CD31, and CD41a expression. Multiparametric t-SNE and FlowSOM analysis clustered plasma EVs based on these markers. Decision trees and random forest classifiers were trained and tested to predict the disease status (GBM vs non-GBM) based on plasma EV subpopulations. RESULTS Compared to non-GBM, GBM had increased frequency of CD9+ EVs (64.52% vs 40.69%, p< 0.0001), CD9+CD63-CD81- EVs (54.45% vs 37.26%, p< 0.0001) and myeloid-derived EVs (CD9+CD11b+ EVs, 20.77% vs 12.48%, p = 0.0241). In the decision tree analysis, low frequency of CD9+ EVs was found in 44% of all cases and represented a chance of 75% to be classified as non-GBM cases. An algorithm based on random forest analysis (n = 500 trees) using these markers had a sensitivity of 85.71% and specificity of 77.8% with an overall accuracy of 81.25% for identifying GBM. The frequencies of CD9+, CD9+CD63-CD81-, and CD9+CD63+CD81+ were the most critical factors in predicting GBM diagnosis. FlowSOM analysis identified 20 plasma EV subpopulations but had a lower accuracy (62.5%) when combined with our machine learning algorithm. CONCLUSIONS Non-tumor plasma EV expression profile combined with tree-based machine learning demonstrated high accuracy in predicting patient disease status. CD9+ EV frequency was one of the most promising biomarkers to assist in this diagnosis. We will continue to test our established algorithm and further validate our findings in a larger cohort.

Abstract INTRODUCTION Extracellular Vesicles (EVs) are lipid bilayer particles present in almost all biofluids and carry tumor-specific molecular cargo. Plasma EVs are promising sources of biomarker discovery for glioblastoma (GBM). The clinical relevance of non-neoplastic plasma EVs in GBM remains unclear. In this study, we determine if associations exist between plasma EV subpopulation frequencies in GBM and clinical factors. METHODS Platelet-free plasma samples of 63 GBM, 43 Grade 2 glioma, and 45 healthy donors were included. All glioma plasma samples were collected before surgery. Plasma EV subpopulation frequency based on surface expression of EV-associated tetraspanins (CD9/CD81/CD63) and markers of cell origin (CD45/CD41a/CD11b/CD31) was determined by spectral flow cytometry. Clinical information including age, surgery, MGMT methylation status, corticosteroid usage, tumor volume, and overall survival data was recorded. RESULTS Compared to normal donors, GBM patients had increased CD9+, CD63+, CD81+, and CD11b+ EVs but decreased CD41a+ EVs. GBM patients also had the highest frequency of myeloid-derived CD9+CD11b+ EVs (20.17%) followed by grade 2 gliomas (13.78%) and normal donors (2.76%, p< 0.001). More GBM patients (29/63, 46.0%) received preoperative corticosteroids than grade 2 glioma patients (9/43, 20.9%; p = 0.008). Corticosteroid use was not associated with differences in EV subpopulation frequencies in GBM or grade 2 glioma patients. Pearson correlation analysis showed weak but statistically significant correlations between GBM total tumor volume and CD9+ EVs (p = 0.014), CD41a+ EVs (p = 0.037), and CD9+/CD63-/CD81- EVs (p = 0.007). In GBM patients, younger age (< 60 yrs, p = 0.01), gross total resection (p = 0.02), MGMT methylation (p = 0.002), high CD9+ EV expression (p = 0.011), low CD9+CD63-CD81- EV expression (p = 0.009) and low CD9+CD11b+ EV expression (p = 0.006) correlated with longer overall survival. CONCLUSION Differentially expressed non-neoplastic plasma EV subpopulations have significant clinical relevance in GBM including association with tumor volume and predicting survival. Validation of those markers in larger cohorts could reveal new prognostic biomarkers for GBM.

OBJECTIVE Chordomas are rare tumors that often recur regardless of surgery with negative margins and postoperative radiotherapy. The predictive accuracy of widely used immunohistochemical (IHC) markers in addressing the recurrence of skull base chordomas (SBCs) is yet to be determined. This study aimed to investigate IHC markers in the prediction of recurrence after SBC resection with adjuvant radiation therapy. METHODS The authors reviewed the records of patients who had treatment for SBC between January 2017 and June 2021 across the Mayo Clinic in Minnesota, Florida, and Arizona. Exclusion criteria included patients who had no histopathology or recurrence as an outcome. Histopathological markers included cytokeratin A1/A3 only, epithelial membrane antigen (EMA), S100 protein, pan-cytokeratin, IN1, GATA3, CAM5.2, OSCAR, and chondroid. Information from patient records was abstracted, including treatment, clinical and radiological follow-up duration, demographics, and histopathological factors. Decision tree and random forest classifiers were trained and tested to predict the recurrence based on unseen data using an 80/20 split. RESULTS A total of 38 patients with a diagnosis of SBC who underwent resection (gross-total resection: 42.1%; and subtotal resection: 57.9%) and radiation therapy were extracted from the medical records. The mean patient age was 48.2 (SD 19.6) years; most patients were male (n = 23; 60.5%) and White (n = 36; 94.7%). Pan-cytokeratin was associated with an increased risk of postoperative recurrence (OR 14.67, 95% CI 2.44–88.13; p = 0.00517) after resection and adjuvant radiotherapy. The decision tree analysis found pan-cytokeratin–positive tumors to have a 78% chance of being classified as a recurrence, with an accuracy of 75%. The distribution of minimal depth in the prediction of postoperative recurrence indicates that the most important variables were pan-cytokeratin, followed by cytokeratin A1/A3 and EMA. CONCLUSIONS The authors’ machine learning algorithm identified pan-cytokeratin as the largest contributor to recurrence among other IHC markers after SBC resection. Machine learning may facilitate the prediction of outcomes in rare tumors, such as chordomas.

OBJECTIVE Malignant melanotic nerve sheath tumors are rare tumors characterized by neoplastic melanin-producing Schwann cells. In this study, the authors report their institution’s experience in treating spinal and peripheral malignant melanotic nerve sheath tumors and compare their results with the literature. METHODS Data were collected from 8 patients who underwent surgical treatment for malignant melanotic nerve sheath tumors between 1996 and 2023 at Mayo Clinic and 63 patients from the literature. Time-to-event analyses were performed for the combined group of 71 cases to evaluate the risk of recurrence, metastasis, and death based on tumor location and type of treatment received. Unpaired 2-sample t-tests and Fisher’s exact tests were used to determine statistical significance between groups. RESULTS Between 1996 and 2023, 8 patients with malignant melanotic nerve sheath tumors underwent surgery at the authors’ institution, while 63 patients were identified in the literature. The authors’ patients and those in the literature had the same mean age at diagnosis (43 years). At the authors’ institution, 5 patients (63%) experienced metastasis, 6 patients (75%) experienced long-term recurrence, and 5 patients (62.5%) died. In the literature, most patients (60.3%) were males, with a peak incidence between the 4th and 5th decades of life. Nineteen patients (31.1%) were diagnosed with Carney complex. Nerve root tumors accounted for most presentations (n = 39, 61.9%). Moreover, 24 patients (38.1%) had intradural lesions, with 54.2% (n = 13) being intramedullary and 45.8% (n = 11) extramedullary. Most patients underwent gross-total resection (GTR) (n = 41, 66.1%), followed by subtotal resection (STR) (n = 12, 19.4%), STR with radiation therapy (9.7%), and GTR with radiation therapy (4.8%). Sixteen patients (27.6%) experienced metastasis, 23 (39.7%) experienced recurrence, and 13 (22%) died. Kaplan-Meier analyses showed no significant differences among treatment approaches in terms of recurrence-free, metastasis-free, and overall survival (p > 0.05). Similar results were obtained when looking at the differences with respect to intradural versus nerve root location of the tumor (p > 0.05). CONCLUSIONS Malignant melanotic nerve sheath tumors are rare tumors with a high potential for malignancy. They carry a dismal prognosis, with a pooled local recurrence rate of 42%, distant metastasis rate of 27%, and mortality rate of 26%. The findings from this study suggest a trend favoring the use of GTR alone or STR with radiation therapy over STR alone. Mortality was similar regardless, which highlights the need for the development of effective treatment options to improve survival in patients with melanotic schwannomas.

BACKGROUND AND OBJECTIVES: Nongeneral anesthesia (non-GA) spine surgery is growing in popularity and has facilitated earlier postoperative recovery, reduced cost, and fewer complications compared with spine surgery under general anesthesia (GA). Changes in reimbursement policies have been demonstrated to correlate with clinical practice; however, they have yet to be studied for GA vs non-GA spine procedures. We aimed to investigate trends in physician reimbursement for GA vs non-GA spine surgery in the United States. METHODS: We queried the ACS-NSQIP for GA and non-GA (regional, epidural, spinal, and anesthesia care/intravenous sedation) spine surgeries during 2011–2020. Work relative value units per operative hour (wRVUs/h) were retrieved for decompression or stabilization of the cervical, thoracic, and lumbar spine. Propensity score matching (1:1) was performed using all baseline variables. RESULTS: We included 474 706 patients who underwent spine decompression or stabilization procedures. GA was used in 472 248 operations, whereas 2458 operations were non-GA. The proportion of non-GA spine operations significantly increased during the study period. Operative times ( P < .001) and length of stays ( P < .001) were shorter in non-GA when compared with GA procedures. Non-GA lumbar procedures had significantly higher wRVUs/h when compared with the same procedures performed under GA (decompression; P < .001 and stabilization; P = .039). However, the same could not be said about cervicothoracic procedures. Lumbar decompression surgeries using non-GA witnessed significant yearly increase in wRVUs/h ( P < .01) contrary to GA ( P = .72). Physician reimbursement remained stable for procedures of the cervical or thoracic spine regardless of the anesthesia. CONCLUSION: Non-GA lumbar decompressions and stabilizations are associated with higher and increasing reimbursement trends (wRVUs/h) compared with those under GA. Reimbursement for cervical and thoracic surgeries was equal regardless of the type of anesthesia and being relatively stable during the study period. The adoption of a non-GA technique relative to the GA increased significantly during the study period.

OBJECTIVE Chordomas are slow-growing tumors derived from notochord remnants. Despite margin-negative excision and postoperative radiation therapy, spinal chordomas (SCs) often progress. The potential of immunohistochemical (IHC) markers, such as epithelial membrane antigen (EMA), combined with machine learning algorithms to predict long-term (≥ 12 months) postoperative tumor progression, has been understudied. The authors aimed to identify IHC markers using trained tree-based algorithms to predict long-term (≥ 12 months) postoperative tumor progression. METHODS The authors reviewed the records of patients who underwent resection of SCs between January 2017 and June 2021 across the Mayo Clinic enterprise. Demographics, type of treatment, histopathology, and other relevant clinical factors were abstracted from each patient’s record. Low tumor progression was defined as more than a 94.3-mm3 decrease in the tumor size at the latest radiographic follow-up. Decision trees and random forest classifiers were trained and tested to predict the long-term volumetric progression after an 80/20 data split. RESULTS Sixty-two patients diagnosed with and surgically treated for SC were identified, of whom 31 were found to have a more advanced tumor progression based on the tumor volume change cutoff of 94.3 mm3. The mean age was 54.3 ± 13.8 years, and most patients were male (62.9%) and White (98.4%). The most common treatment modality was subtotal resection with radiation therapy (35.5%), with proton beam therapy being the most common (71%). Most SCs were sacrococcygeal (41.9%), followed by cervical (32.3%). EMA-positive SCs had a postoperative progression risk of 67%. Pancytokeratin-positive SCs had a progression rate of 67%; however, patients with S100 protein–positive SCs had a 54% risk of progression. The accuracy of this model in predicting the progression of unseen test data was 66%. Pancytokeratin (mean minimal depth = 1.57), EMA (mean minimal depth = 1.58), cytokeratin A1/A3 (mean minimal depth = 1.59), and S100 protein (mean minimal depth = 1.6) predicted the long-term volumetric progression. Multiway variable importance plots show the relative importance of the top 10 variables based on three measures of varying significance and their predictive role. CONCLUSIONS These IHC variables with tree-based machine learning tools successfully demonstrate a high capacity to identify a patient’s tumor progression pattern with an accuracy of 66%. Pancytokeratin, EMA, cytokeratin A1/A3, and S100 protein were the IHC drivers of a low tumor progression. This shows the power of machine learning algorithms in analyzing and predicting outcomes of rare conditions in a small sample size.

IMPORTANCEPatients undergoing spine surgery often experience severe pain. The optimal dosage of pregabalin and gabapentin for pain control and safety in these patients has not been well established.OBJECTIVETo evaluate the associations of pain, opioid consumption, and adverse events with different dosages of pregabalin and gabapentin in patients undergoing spine surgery.DATA SOURCESPubMed/MEDLINE, Embase, Web of Science, Cochrane library, and Scopus databases were searched for articles until August 7, 2021.STUDY SELECTIONRandomized clinical trials conducted among patients who received pregabalin or gabapentin while undergoing spine surgery were included.DATA EXTRACTION AND SYNTHESISTwo investigators independently performed data extraction following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) reporting guideline. The network meta-analysis was conducted from August 2022 to February 2023 using a random-effects model.MAIN OUTCOMES AND MEASURESThe primary outcome was pain intensity measured using the Visual Analog Scale (VAS), and secondary outcomes included opioid consumption and adverse events.RESULTSTwenty-seven randomized clinical trials with 1861 patients (median age, 45.99 years [range, 20.00-70.00 years]; 759 women [40.8%]) were included in the systematic review and network meta-analysis. Compared with placebo, the VAS pain score was lowest with gabapentin 900 mg per day, followed by gabapentin 1200 mg per day, gabapentin 600 mg per day, gabapentin 300 mg per day, pregabalin 300 mg per day, pregabalin 150 mg per day, and pregabalin 75 mg per day. Additionally, gabapentin 900 mg per day was found to be associated with the lowest opioid consumption among all dosages of gabapentin and pregabalin, with a mean difference of −22.07% (95% CI, −33.22% to −10.92%) for the surface under the cumulative ranking curve compared with placebo. There was no statistically significant difference in adverse events (nausea, vomiting, and dizziness) among all treatments. No substantial inconsistency between direct and indirect evidence was detected for all outcomes.CONCLUSIONS AND RELEVANCEThese findings suggest that gabapentin 900 mg per day before spine surgery is associated with the lowest VAS pain score among all dosages. In addition, no differences in adverse events were noted among all treatments.

OBJECTIVE Chordomas are rare tumors from notochordal remnants and account for 1%–4% of all primary bone malignancies, often arising from the clivus and sacrum. Despite margin-negative resection and postoperative radiotherapy, chordomas often recur. Further, immunohistochemical (IHC) markers have not been assessed as predictive of chordoma recurrence. The authors aimed to identify the IHC markers that are predictive of postoperative long-term (≥ 1 year) chordoma recurrence by using trained multiple tree-based machine learning (ML) algorithms. METHODS The authors reviewed the records of patients who had undergone treatment for clival and spinal chordomas between January 2017 and June 2021 across the Mayo Clinic enterprise (Minnesota, Florida, and Arizona). Demographics, type of treatment, histopathology, and other relevant clinical factors were abstracted from each patient record. Decision tree and random forest classifiers were trained and tested to predict long-term recurrence based on unseen data using an 80/20 split. RESULTS One hundred fifty-one patients diagnosed and treated for chordomas were identified: 58 chordomas of the clivus, 48 chordomas of the mobile spine, and 45 chordomas sacrococcygeal in origin. Patients diagnosed with cervical chordomas were the oldest among all groups (58 ± 14 years, p = 0.009). Most patients were male (n = 91, 60.3%) and White (n = 139, 92.1%). Most patients underwent resection with or without radiation therapy (n = 129, 85.4%). Subtotal resection followed by radiation therapy (n = 51, 33.8%) was the most common treatment modality, followed by gross-total resection then radiation therapy (n = 43, 28.5%). Multivariate analysis showed that S100 and pan-cytokeratin are more likely to predict the increase in the risk of postoperative recurrence (OR 3.67, 95% CI 1.09–12.42, p= 0.03; and OR 3.74, 95% CI 0.05–2.21, p = 0.02, respectively). In the decision tree analysis, a clinical follow-up > 1897 days was found in 37% of encounters and a 90% chance of being classified for recurrence (accuracy = 77%). Random forest analysis (n = 500 trees) showed that patient age, type of surgical treatment, location of tumor, S100, pan-cytokeratin, and EMA are the factors predicting long-term recurrence. CONCLUSIONS The IHC and clinicopathological variables combined with tree-based ML tools successfully demonstrated a high capacity to identify recurrence patterns with an accuracy of 77%. S100, pan-cytokeratin, and EMA were the IHC drivers of recurrence. This shows the power of ML algorithms in analyzing and predicting outcomes of rare conditions of a small sample size.

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.