Differentiating between spinal schwannomas and meningiomas using MRI: A focus on cystic change

Characterization of spinal hemangioblastomas in patients with and without von Hippel-Lindau, and YAP expression

INTRODUCTION

Hemangioblastoma (HB) is a benign tumor of the central nervous system, associated with von Hippel-Lindau disease (VHL), or sporadic. The aim of this study was to compare and examine the clinical-pathological profile of patients with spinal hemangioblastoma and YAP expression.

METHODS

A retrospective, descriptive, comparative study. All patients who underwent surgery for spinal HB between 2016 and 2023 were included. Clinical and radiological data were collected and analyzed. An immunohistochemistry panel including NeuN, neurofilaments (NF), and YAP-1, was performed.

RESULTS

Nine patients were studied, six women and three men. Four patients had previously diagnosed VHL. The tumor location included: four cervical (44.44%), two thoracic (22.22%), two pontine with cervical extension (22.22%) and one patient with two lesions, one cervical and one thoracic (11.11%). Non-significant clinical differences were identified between VHL and sporadic patients. Imaging evidenced seven extramedullary and three intramedullary tumors. Histologically, intra-tumoral and perivascular axonal tracts were observed in all cases. One third of the tumors (two with VHL and one sporadic) presented extramedullary hematopoiesis. Seven cases (77.8%) expressed nuclear YAP (three with VHL and four sporadic HBs). The surgical outcome was good and only one patient with VHL undergoing subtotal resection had recurrence.

CONCLUSIONS

Spinal HBs can be associated with VHL or be sporadic. To the best of our knowledge, this is the first study to describe YAP expression in HB. It is important to investigate the involvement of the Hippo pathway in HBs as a possible therapeutic target.

Diagnostic and Therapeutic Insights into Spinal Glomangioma of a Unique Intradural, Extramedullary Presentation-Systematic Review

Contemporary literature lacks examples of intradural, extramedullary spinal glomangiomas. Moreover, glomus tumors in general are exceedingly rare among benign spinal tumors and are mostly located within epidural space or within intervertebral foramen, and only a few cases have been documented to date. This report provides a detailed analysis of the clinical presentation, imaging characteristics, surgical intervention, and pathological findings of a 45-year-old patient experiencing progressive locomotor deterioration. The tumor was surgically excised, and subsequent histological examination identified it as a representative of glomus tumors-a glomangioma. Notably, this represents a unique case as it was the first example of such a tumor being discovered intradurally. Radical surgical excision remains the modality of choice in most benign spinal tumors of this localization. Although the malignant transformation of glomus tumors within the spine has not been documented thus far, cases have arisen in other areas. Consequently, we will investigate potential oncological treatments for cases with malignant potential and highlight advancements in surgical techniques for benign intradural spinal tumors.

Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis

Background

Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas.

Methods

We retrospectively collected 88 schwannomas (52 males and 36 females) and 51 meningiomas (10 males and 41 females) who underwent magnetic resonance imaging (MRI) examinations prior to the surgery. Simple clinical data and MRI imaging features, including age, sex, tumor location and size, T1-weighted images (T1WI) and T2-weighted images (T2WI) signal characteristics, degree and pattern of enhancement, dural tail sign, ginkgo leaf sign, and intervertebral foramen widening (IFW), were reviewed. Finally, a CART model and RF model were established based on the aforementioned features to evaluate their effectiveness in differentiating between the two types of tumors. Meanwhile, we also compared the performance of the ML models to the radiologists. The receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the models and clinicians' discrimination performance.

Results

Our investigation reveals significant variations in ten out of 11 variables in the training group and five out of 11 variables in the test group when comparing schwannomas and meningiomas (P<0.05). Ultimately, the CART model incorporated five variables: enhancement pattern, the presence of IFW, tumor location, maximum diameter, and T2WI signal intensity (SI). The RF model combined all 11 variables. The CART model, RF model, radiologist 1, and radiologist 2 achieved an area under the curve (AUC) of 0.890, 0.956, 0.681, and 0.723 in the training group, and 0.838, 0.922, 0.580, and 0.659 in the test group, respectively.

Conclusions

The RF prediction model exhibits more exceptional performance than an experienced radiologist in discriminating intraspinal schwannomas from meningiomas. The RF model seems to be better in discriminating the two tumors than the CART model.

Spinal Meningiomas: A Comprehensive Review and Update on Advancements in Molecular Characterization, Diagnostics, Surgical Approach and Technology, and Alternative Therapies

Spinal meningiomas are the most common intradural, extramedullary tumor in adults, yet the least common entity when accounting for all meningiomas spanning the neuraxis. While traditionally considered a benign recapitulation of their intracranial counterpart, a paucity of knowledge exists regarding the differences between meningiomas arising from these two anatomic compartments in terms of histopathologic subtypes, molecular tumor biology, surgical principles, long-term functional outcomes, and recurrence rates. To date, advancements at the bench have largely been made for intracranial meningiomas, including the discovery of novel gene targets, DNA methylation profiles, integrated diagnoses, and alternative systemic therapies, with few exceptions reserved for spinal pathology. Likewise, evolving clinical research offers significant updates to our understanding of guiding surgical principles, intraoperative technology, and perioperative patient management for intracranial meningiomas. Nonetheless, spinal meningiomas are predominantly relegated to studies considering non-specific intradural extramedullary spinal tumors of all histopathologic types. The aim of this review is to comprehensively report updates in both basic science and clinical research regarding intraspinal meningiomas and to provide illustrative case examples thereof, thereby lending a better understanding of this heterogenous class of central nervous system tumors.

Long-Term Functional Outcomes Following Surgical Treatment of Spinal Schwannomas: A Population-Based Cohort Study

Spinal schwannomas are the second most common form of primary intradural spinal tumor. Despite being benign, they may cause spinal cord compression and subsequently acute or chronic neurological dysfunction. The primary treatment is surgical resection. The aim of this study was to identify pre- and postoperative predictors of favorable outcomes after surgical treatment for spinal schwannoma. All adult patients surgically treated for spinal schwannoma between 2006 and 2020 were eligible for inclusion. Medical records and imaging data were retrospectively reviewed. The primary outcome measures were neurological improvement according to the modified McCormick Scale (mMC) and changes in motor deficit, sensory deficit, gait disturbance, bladder dysfunction, and pain at long-term follow-up. In total, 180 patients with a median follow-up time of 4.4 years were included. Pain was the most common presenting symptom (87%). The median time between symptom presentation and surgery was 12 months, while the median time between diagnosis (first MRI) and surgery was 3 months. Gross total resection (GTR) was achieved in 150 (83%) patients and the nerve root could be preserved in 133 (74%) patients. A postoperative complication occurred in 10 patients (5.6%). There were significant postoperative improvements in terms of motor, sensory, gait, and bladder functions, as well as pain (p < 0.001). Of these symptoms, bladder dysfunction was the one most often improved, with complete symptom resolution in all cases. However, no other predictors of improvement could be identified. There were three cases of recurrence after GTR and nine cases of regrowth after STR. Reoperation was performed in six (3.3%) cases. GTR was associated with a significant improvement in neurological status at long-term follow-up and increased the chance of progression-free survival.

Development of a novel prediction model for differential diagnosis between spinal myxopapillary ependymoma and schwannoma

Spinal myxopapillary ependymoma (MPE) and schwannoma represent clinically distinct intradural extramedullary tumors, albeit with shared and overlapping magnetic resonance imaging (MRI) characteristics. We aimed to identify significant MRI features that can differentiate between MPE and schwannoma and develop a novel prediction model using these features. In this study, 77 patients with MPE (n = 24) or schwannoma (n = 53) who underwent preoperative MRI and surgical removal between January 2012 and December 2022 were included. MRI features, including intratumoral T2 dark signals, subarachnoid hemorrhage (SAH), leptomeningeal seeding, and enhancement patterns, were analyzed. Logistic regression analysis was conducted to distinguish between MPE and schwannomas based on MRI parameters, and a prediction model was developed using significant MRI parameters. The model was validated internally using a stratified tenfold cross-validation. The area under the curve (AUC) was calculated based on the receiver operating characteristic curve analysis. MPEs had a significantly larger mean size (p = 0.0035), higher frequency of intratumoral T2 dark signals (p = 0.0021), associated SAH (p = 0.0377), and leptomeningeal seeding (p = 0.0377). Focal and diffuse heterogeneous enhancement patterns were significantly more common in MPEs (p = 0.0049 and 0.0038, respectively). Multivariable analyses showed that intratumoral T2 dark signal (p = 0.0439) and focal (p = 0.0029) and diffuse enhancement patterns (p = 0.0398) were independent factors. The prediction model showed an AUC of 0.9204 (95% CI 0.8532-0.9876) and the average AUC for internal validation was 0.9210 (95% CI 0.9160-0.9270). MRI provides useful data for differentiating spinal MPEs from schwannomas. The prediction model developed based on the MRI features demonstrated excellent discriminatory performance.

Primary extracranial meningioma of the pelvis discovered on screening pelvic examination

Many conditions that affect a woman's health can be evaluated through the pelvic examination. Early detection and treatment of a range of gynaecologic and non-gynaecological conditions, including unusual pelvic masses, may decrease a woman's morbidity and mortality. Here, we have a female patient in her early 20s who was found to have a mass on her first screening pelvic examination. Subsequent imaging followed by surgical resection were performed with the final diagnosis of a pelvic meningioma. Routine pelvic examinations in asymptomatic women may be more useful than merely screening for cervical cancer and sexually transmitted infections. Once detected, the differential diagnosis of a pelvic mass may include aetiologies outside of the gynaecological organ system.

Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging

Spinal cord tumors are infrequently identified spinal diseases that are often difficult to diagnose even with magnetic resonance imaging (MRI) findings. To minimize the probability of overlooking these tumors and improve diagnostic accuracy, an automatic diagnostic system is needed. We aimed to develop an automated system for detecting and diagnosing spinal schwannomas and meningiomas based on deep learning using You Only Look Once (YOLO) version 4 and MRI. In this retrospective diagnostic accuracy study, the data of 50 patients with spinal schwannomas, 45 patients with meningiomas, and 100 control cases were reviewed, respectively. Sagittal T1-weighted (T1W) and T2-weighted (T2W) images were used for object detection, classification, training, and validation. The object detection and diagnosis system was developed using YOLO version 4. The accuracies of the proposed object detections based on T1W, T2W, and T1W + T2W images were 84.8%, 90.3%, and 93.8%, respectively. The accuracies of the object detection for two spine surgeons were 88.9% and 90.1%, respectively. The accuracies of the proposed diagnoses based on T1W, T2W, and T1W + T2W images were 76.4%, 83.3%, and 84.1%, respectively. The accuracies of the diagnosis for two spine surgeons were 77.4% and 76.1%, respectively. We demonstrated an accurate, automated detection and diagnosis of spinal schwannomas and meningiomas using the developed deep learning-based method based on MRI. This system could be valuable in supporting radiological diagnosis of spinal schwannomas and meningioma, with a potential of reducing the radiologist's overall workload.

Current Knowledge on Spinal Meningiomas Epidemiology, Tumor Characteristics and Non-Surgical Treatment Options: A Systematic Review and Pooled Analysis (Part 1)

BACKGROUND

Spinal meningiomas are the most common primary intradural spinal tumors. Although they are a separate entity, a large portion of the knowledge on spinal meningiomas is based on findings in intracranial meningiomas. Therefore, a comprehensive review of all the literature on spinal meningiomas was performed.

METHODS

Electronic databases were searched for all studies on spinal meningiomas dating from 2000 and onward. Findings of matching studies were pooled to strengthen the current body of evidence.

RESULTS

A total of 104 studies were included. The majority of patients were female (72.83%), elderly (peak decade: seventh), and had a world health organization (WHO) grade 1 tumor (95.7%). Interestingly, the minority of pediatric patients had a male overrepresentation (62.0% vs. 27.17%) and higher-grade tumors (33.3% vs. 4.3%). Sensory and motor dysfunction and pain were the most common presenting symptoms. Despite a handful of studies reporting promising findings associated with the use of non-surgical treatment options, the literature still suffers from contradictory results and limitations of study designs.

CONCLUSIONS

Elderly females with WHO grade 1 tumors constituted the stereotypical type of patient. Compared to surgical alternatives, the evidence for the use of non-surgical treatments is still relatively weak.

Primary Benign Tumors of the Spinal Canal

Benign tumors that grow in the spinal canal are heterogeneous neoplasms with low incidence; from these, meningiomas and nerve sheath tumors (neurofibromas and schwannomas) account for 60%-70% of all primary spinal tumors. Benign spinal canal tumors provoke nonspecific clinical manifestations, mostly related to the affected level of the spinal cord. These tumors present a challenge for the patient and healthcare professionals, for they are often difficult to diagnose and the high frequency of posttreatment complications. In this review, we describe the epidemiology, risk factors, clinical features, diagnosis, histopathology, molecular biology, and treatment of extramedullary benign meningiomas, osteoid osteomas, osteoblastomas, aneurysmal bone cysts, osteochondromas, neurofibromas, giant cell tumors of the bone, eosinophilic granulomas, hemangiomas, lipomas, and schwannomas located in the spine, as well as possible future targets that could lead to an improvement in their management.

The value of quantitative magnetic resonance imaging signal intensity in distinguishing between spinal meningiomas and schwannomas

Background: Prior studies have suggested a number of the subjective visual characteristics that help distinguish between spinal meningiomas and schwannomas on magnetic resonance imaging and computed tomography; however, objective quantification of the signal intensity can be useful information. This study assessed whether quantitative magnetic resonance imaging (MRI) signal intensity (SI) measurements could distinguish intradural-extramedullary schwannomas from meningiomas. Methods: From July 2019 to September 2021, 54 patients with intradural-extramedullary tumors (37 meningiomas and 17 schwannomas) underwent surgery, and tumors were verified pathologically. Defined regions of interest were used to quantify SI values on T1- (T1W) and T2-weighted images (T2W). Receiver operating characteristic curve analysis was used to obtain cutoff values and calculate the area under the curve (AUC), sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV). Results: Both Maximum (T2_max) and mean (T2_mean) T2W SI values demonstrated outstanding (AUC: 0.91) abilities to differentiate meningiomas from schwannomas with Se, Sp, PPV, and NPV values of 94.6%, 70.6%, 87.5%, and 85.7%, respectively, for T2_max and 81.1%, 88.2%, 93.8%, and 68.2% for T2_mean. The maximum SI value on contrast-enhanced T1W (T1CE_max) and the T2W tumor: fat SI ratio (rTF) demonstrated acceptable abilities (AUC: 0.73 and 0.79, respectively) to differentiate meningiomas from schwannomas with Se, Sp, PPV, and NPV values of 94.6%, 70.6%, 87.5%, and 85.7%, respectively, for T1CE_max and 81.1%, 88.2%, 93.8%, and 68.2% for rTF. Conclusions: Quantitative SI values (T2_max, T2_mean, T2_min, T1CE_max, rTF) can be used to differentiate intradural-extramedullary schwannomas from meningiomas.