Spinner, Robert J.

Abstract Background Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. Methods We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. Results One hundred and seven schwannomas and 59 neurofibromas were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUCs for the Logistic Regression (AUC = 0.923) and K Nearest Neighbor (AUC = 0.923) classifiers were significantly greater than the human evaluators (AUC = 0.766; p = 0.041). Conclusions The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.

OBJECTIVE Spinal procedures are increasingly conducted as outpatient procedures, with a growing proportion conducted in ambulatory surgery centers (ASCs). To date, studies reporting outcomes and cost analyses for outpatient spinal procedures in the US have not distinguished the various outpatient settings from each other. In this study, the authors used a state-level administrative database to compare rates of overnight stays and nonroutine discharges as well as index admission charges and cumulative 7-, 30-, and 90-day charges for patients undergoing outpatient lumbar decompression in freestanding ASCs and hospital outpatient (HO) settings. METHODS For this project, the authors used the Florida State Ambulatory Surgery Database (SASD), offered by the Healthcare Cost and Utilization Project (HCUP), for the years 2013 and 2014. Patients undergoing outpatient lumbar decompression for degenerative diseases were identified using CPT (Current Procedural Terminology) and ICD-9 codes. Outcomes of interest included rates of overnight stays, rate of nonroutine discharges, index admission charges, and subsequent admission cumulative charges at 7, 30, and 90 days. Multivariable analysis was performed to assess the impact of outpatient type on index admission charges. Marginal effect analysis was employed to study the difference in predicted dollar margins between ASCs and HOs for each insurance type. RESULTS A total of 25,486 patients were identified; of these, 7067 patients (27.7%) underwent lumbar decompression in a freestanding ASC and 18,419 (72.3%) in an HO. No patient in the ASC group required an overnight stay compared to 9.2% (n = 1691) in the HO group (p < 0.001). No clinically significant difference in the rate of nonroutine discharge was observed between the two groups. The mean index admission charge for the ASC group was found to be significantly higher than that for the HO group ($35,017.28 ± $14,335.60 vs $33,881.50 ± $15,023.70; p < 0.001). Patients in ASCs were also found to have higher mean 7-day (p < 0.001), 30-day (p < 0.001), and 90-day (p = 0.001) readmission charges. ASC procedures were associated with increased charges compared to HO procedures for patients on Medicare or Medicaid (mean index admission charge increase $4049.27, 95% CI $2577.87–$5520.67, p < 0.001) and for patients on private insurance ($4775.72, 95% CI $4171.06–$5380.38, p < 0.001). For patients on self-pay or no charge, a lumbar decompression procedure at an ASC was associated with a decrease in index admission charge of −$10,995.38 (95% CI −$12124.76 to −$9866.01, p < 0.001) compared to a lumbar decompression procedure at an HO. CONCLUSIONS These “real-world” results from an all-payer statewide database indicate that for outpatient spine surgery, ASCs may be associated with higher index admission and subsequent 7-, 30-, and 90-day charges. Given that ASCs are touted to have lower overall costs for patients and better profit margins for physicians, these analyses warrant further investigation into whether this cost benefit is applicable to outpatient spine procedures.

An expert opinion study was designed to query five countries and six brachial plexus surgeons regarding the demographics, mechanisms of injury, evaluation, timing of surgery, reconstructive strategies and controversies in adult traumatic pan brachial plexus injuries. Variations in assessing outcomes, management of neuropathic pain and future considerations were elucidated. Clear differences in regional demographics, mechanisms of injury, patient evaluation and treatment strategies were identified. The role of phrenic nerve and contralateral C7 transfer, acute use of free functioning muscle transfers, root reimplantation and amputation/myoelectric prosthetic fitting were regional/surgeon dependent. Comparison of outcomes across regions requires an understanding of the regional nuances of patient demographics, injury mechanisms, preferred reconstructive strategies and how outcomes are measured. Future studies are required to allow accurate regional comparisons.

OBJECTIVE The objective of this study was to determine if patients with nerve sheath tumors affecting the C5 spinal nerve are at greater risk for postoperative weakness than those with similar tumors affecting other spinal nerves contributing to the brachial plexus. METHODS A retrospective chart review (1998–2020)identified patients with pathologically confirmed schwannomas or neurofibromas from the C5 to T1 nerves. Patients with plexiform nerve sheath tumors, tumors involving more than 1 nerve, and malignant peripheral nerve sheath tumors were excluded. Collected variables included basic demographics, tumor dimensions, its location relative to the dura, involved nerve level, surgical approach, extent of resection, presenting symptoms, postoperative neurological deficits, and recurrence rate. RESULTS Forty-six patients (23 men, 23 women) were identified for inclusion in the study with an average age of 47 ± 17 years, BMI of 28 ± 5 kg/m2, and follow-up of 32 ± 45 months. Thirty-nine patients (85%) had schwannomas and 7 (15%) had neurofibromas. Tumors involved the C5 (n = 12), C6 (n = 11), C7 (n = 14), C8 (n = 6), and T1 (n = 3) nerves. Multivariable logistic regression analysis with an area under the curve of 0.85 demonstrated C5 tumor level as an independent predictor of new postoperative weakness (odds ratio 7.4, p = 0.028). Of those patients with new postoperative weakness, 75% improved and 50% experienced complete resolution of their motor deficits. CONCLUSIONS Patients with C5 nerve sheath tumor resections are at higher odds of new postoperative weakness. This may be due to the predominant single innervation of shoulder muscle targets in contrast to other upper extremity muscles that receive input from 2 or more spinal nerves. These findings are important for clinical decision-making and preoperative patient counseling.

OBJECTIVE Sustained postoperative opioid use after elective surgery is a matter of growing concern. Herein, the authors investigated incidence and predictors of long-term opioid use among patients undergoing elective lumbar spine surgery, especially as a function of opioid prescribing practices at postoperative discharge (dose in morphine milligram equivalents [MMEs] and type of opioid). METHODS The OptumLabs Data Warehouse (OLDW) was queried for postdischarge opioid prescriptions for patients undergoing elective lumbar decompression and discectomy (LDD) or posterior lumbar fusion (PLF) for degenerative spine disease. Only patients who received an opioid prescription at postoperative discharge and those who had a minimum of 180 days of insurance coverage prior to surgery and 180 days after surgery were included. Opioid-naive patients were defined as those who had no opioid fills in 180 days prior to surgery. The following patterns of long-term postoperative use were investigated: additional fills (at least one opioid fill 90–180 days after surgery), persistent fills (any span of opioid use starting in the 180 days after surgery and lasting at least 90 days), and Consortium to Study Opioid Risks and Trends (CONSORT) criteria for persistent use (episodes of opioid prescribing lasting longer than 90 days and 120 or more total days’ supply or 10 or more prescriptions in 180 days after the index fill). Multivariable logistic regression was performed to identify predictors of long-term use. RESULTS A total of 25,587 patients were included, of whom 52.7% underwent PLF (n = 13,486) and 32.5% (n = 8312) were opioid-naive prior to surgery. The rates of additional fills, persistent fills, and CONSORT use were 47%, 30%, and 23%, respectively, after PLF and 35.4%, 19%, and 14.2%, respectively, after LDD. The rates among opioid-naive patients were 18.9%, 5.6%, and 2.5% respectively, after PLF and 13.3%, 2.0%, and 0.8%, respectively, after LDD. Using multivariable logistic regression, the following were identified to be significantly associated with higher risk of long-term opioid use following PLF: discharge opioid prescription ≥ 500 MMEs, prescription of a long-acting opioid, female sex, multilevel surgery, and comorbidities such as depression and drug abuse (all p < 0.05). Elderly (age ≥ 65 years) and opioid-naive patients were found to be at lower risk (all p < 0.05). Similar results were obtained on analysis for LDD with the following significant additional risk factors identified: discharge opioid prescription ≥ 400 MMEs, prescription of tramadol alone at discharge, and inpatient surgery (all p < 0.05). CONCLUSIONS In an analysis of pharmacy claims from a national insurance database, the authors identified incidence and predictors of long-term opioid use after elective lumbar spine surgery.

AbstractBy recognizing anatomic and radiologic patterns of rare and often misdiagnosed peripheral nerve tumors/lesions, we have defined mechanisms for the propagation of neural diseases. The novel concept of the nervous system serving as a complex system of “highways” driving the neural and perineural spread of these lesions is described in three examples: Intraneural dissection of joint fluid in intraneural ganglion cysts, perineural spread of cancer cells, and dissemination of unknown concentrations of neurotrophic/inhibitory factors for growth in hamartomas/choristomas of nerve. Further mapping of these pathways to identify the natural history of diseases, the spectrum of disease evolution, the role of genetic mutations, and how these neural pathways interface with the lymphatic, vascular, and cerebrospinal systems may lead to advances in targeted treatments.

» The incidence of brachial plexus birth injury (BPBI) in the United States is declining and now occurs in <1 per 1,000 births. » The gold standard for predicting the need for early intervention remains serial examination. » Early treatment of BPBI with reconstructive surgery requires the ability to perform both interposition nerve grafting and nerve transfers. » Given the heterogeneity of lesions, the evidence is largely limited to retrospective comparative studies and case series.

BACKGROUND The advancement of high-resolution imaging and increased clinical experience have led to an increased understanding of the formation and treatment of intraneural ganglion cysts. Nearly all intraneural ganglion cysts in the common peroneal nerve have been reported to arise from a joint connection to the superior tibiofibular joint. The authors have identified four cases of intraneural ganglion cysts arising from the knee joint itself; however, none of these reported cases were well described, documented, or illustrated with high-resolution imaging. OBSERVATIONS Here the authors present the case of an intraneural ganglion cyst arising from the knee joint and causing intermittent weakness and pain. The articular branch to the knee joint was clearly demonstrated on high-resolution magnetic resonance imaging and confirmed at surgical exploration. The patient was treated with articular branch ligation and has had complete resolution of his symptoms without recurrence of the cyst on follow-up imaging. LESSONS This case adds to the mounting evidence that intraneural cyst pathology is dependent on a connection to a synovial joint as stated in the unifying theory of intraneural cyst development.

Abstract BACKGROUND Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans ( P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was ( P = .001). CONCLUSION Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.