Differentiating spinal intradural-extramedullary schwannoma from meningioma using MRI T2 weighted images

Clinical, molecular, and genetic features of spinal meningiomas

Spinal meningiomas comprise 25%-46% of all primary spinal tumors. While the majority are benign and slow-growing, when left untreated, they can result in significant neurological decline. Emerging clinical, imaging, and molecular data have begun to reveal spinal meningiomas as distinct tumor subtypes compared to their intracranial counterparts. Moreover, recent studies indicate molecular and genetic subtype heterogeneity of spinal meningiomas both within and across the classically defined WHO grades. In the current review, we focus on recent advances highlighting the epidemiological, pathological, molecular/genetic, and clinical characteristics of spinal meningiomas. Furthermore, we explore patient and tumor-specific factors that predict prognosis and postoperative outcomes. We highlight areas that require further investigation, specifically efforts aimed at linking unique molecular, genetic, and imaging characteristics to distinct clinical presentations to better predict and manage patient outcomes.

Analysis of the current status and hot topics in spinal schwannoma imaging research based on bibliometrics

Objective

This study aims to explore the current hot topics and future research trends in spinal schwannoma imaging research, providing a reference for related studies and promoting the development of spinal schwannoma imaging.

Methods

We conducted a literature search in the Web of Science database using the search terms (((TS = (Spinal schwannoma)) AND TS = (Imaging)) OR TS = (Spinal schwannoma)) AND TS = (image) to retrieve relevant articles. The collected data, including authors, keywords, journals, countries, institutions, and references, were subjected to visual analysis using the visualization software CiteSpace 6.4.2R and VOSviewer 1.6.19.

Results

A total of 310 relevant articles were identified. After further screening based on time limits, inclusion, and exclusion criteria, 179 articles were included in the study, consisting of 132 original articles and 42 reviews. These articles were authored by 1,034 authors from 35 countries and 324 institutions and were published in 82 different journals. The included articles cited a total of 6,583 references from 1,314 journals.

Conclusion

Although the field of spinal schwannoma imaging research is not a popular research area in the medical community, there has been an increasing international interest in this field in recent years. While China ranks high in terms of the number of published articles, there is still a gap in terms of the quality and research level compared to developed countries in Europe and America. MRI, as the gold standard for diagnosing spinal schwannomas, is expected to be a research hotspot in terms of feature analysis, enhancement characteristics, and quantitative analysis. It is also hoped that China can increase its investment in research and contribute to the field by publishing high-quality articles in the future.

Differentiation of the Intradural Extramedullary Spinal Tumors, Schwannomas, and Meningiomas Utilizing the Contrast Ratio as a Quantitative Magnetic Resonance Imaging Method

BACKGROUND

Schwannomas and meningiomas are the most common intradural extramedullary spinal tumors; however, differentiating between them using magnetic resonance imaging (MRI) is a frequent challenge. In this study, we aimed to investigate the use of the contrast ratio (CR) as a quantitative MRI method in the differentiation of schwannomas and meningiomas.

METHODS

We analyzed the data of patients with intradural extramedullary spinal tumors who underwent surgery and were diagnosed with either schwannomas or meningiomas by histopathological analysis. Regions of interest were set for the entire spinal tumor on T2-weighted sagittal MRI. To obtain the CR values of spinal tumors (CRtumor), we used the signal intensity (SI) values of the tumor (SItumor) and spinal cord (SIcord) according to the following formula: [CRtumor = (SItumor-SIcord)/(SItumor+SIcord)].

RESULTS

The study included 50 patients (23 males and 27 females) with a mean age of 61.5 years old (11-85 years old). Histopathological analysis revealed that 33 and 17 patients were diagnosed with schwannomas and meningiomas, respectively. The mean CR values of the schwannomas and meningiomas were 0.3040 ± 0.1386 and 0.0173 ± 0.1929, respectively. The CR value of the schwannomas was statistically significantly higher than that of meningiomas (P < 0.01). The cutoff CR value obtained from the receiver operating characteristic curve was 0.143, with a specificity and sensitivity of 90.9% and 88.2%, respectively. Furthermore, the value for the area under the receiver operating characteristic curve was 0.925 (95% confidence interval: 0.852-0.998).

CONCLUSIONS

The evaluation of CRs by using MRI to distinguish between schwannomas and meningiomas is a beneficial quantitative tool.

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.

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.

Factors predicting recurrence in benign spinal nerve sheath tumors: A retrospective study of 457 patients from a single institution

BACKGROUND

Benign Nerve sheath tumors (NST) comprise almost one-third of primary spinal tumours. The majority are sporadic. They have low rates of recurrence but an occasional recurrence may need re-surgery. The present study was designed to identify the variables that can predict the risk of their recurrence.

METHODS

A retrospective chart review was done including all the histologically proven benign spinal NSTs operated between 2001 and 2019 in our institute. Demographic, operative and postoperative follow-up data were recorded. Recurrence was defined as local reappearance after definite surgical excision or symptomatic increase in size of a residual tumour on follow-up imaging studies. Statistical analysis was done to determine the significant variables associated with local recurrence.

RESULTS

457 patients with a median age of 38 years operated for 459 NSTs qualified for the study. The most frequent location of occurrence of tumours was found to be Low Cervical level (C3-C7 levels). Majority of Schwannoma were located intradurally while Neurofibroma were dumb-bell shaped and extradural. Most of the tumours had solid consistency. Post operatively, 7.7% patients developed complications. 7.8% tumours developed local recurrence after median period of 12 months. The patients developing recurrence were younger compared to nonrecurring tumors. On univariate analysis, male gender, Low cervical and Cervicothoracic junction location were associated with higher recurrence. On multivariate analysis, location at Cervicothoracic junction reached significance.

CONCLUSION

Overall recurrence risk among all NST was 7.8% with a median progression free survival of 36 months. The location of tumour at cervicothoracic location was the significant risk factors for recurrence of tumour in our study.

Atypical intradural extramedullary spinal schwannoma causing cauda equina syndrome: A case report and literature review

INTRODUCTION

Spinal schwannomas are slow-growing benign tumors that are generally asymptomatic. However, we describe an atypical case in which an intradural extramedullary schwannoma presented as an acute cauda equina syndrome.

PRESENTATION OF CASE

This was a 58-year-old woman with a 2-month history of severe low back pain and worsening neurological deficits and a 2-day period of acute onset of lower extremity numbness and urinary incontinence. Physical and neurological examination revealed significant lower extremity weakness, tenderness on palpation of the spine, positive straight leg test bilaterally, decreased sensation below the L4 dermatome, reduced sphincter tone, saddle anesthesia, decreased deep tendon reflexes, and loss of sphincter control, consistent with compression of the cauda equina. Magnetic resonance imaging revealed a large mass of heterogeneous composition at the level of L3 lumbar, intruding into the cauda equina. Wide decompression was successfully performed, and histopathological examination confirmed the diagnosis. With rehabilitation, there was some recovery of lower extremity motor function.

DISCUSSION

Spinal schwannomas are rare, accounting for only about 2 % of spinal tumors. Cauda equina syndrome is also rare, with an incidence of 0.08-0.27 % among patients presenting with low back pain. Therefore, it is important for clinicians to have an awareness of the possible association between spinal schwannoma and cauda equina syndrome and to complete a comprehensive assessment of patients with back pain, including magnetic resonance imaging.

CONCLUSION

Early recognition and treatment of a spinal schwannoma causing neurological symptoms can improve patient outcomes.

Diagnostic value of MRI in coexistence of schwannoma and meningioma mimicking a single dumbbell-shaped tumor in high cervical level. Case series and literature review

CONTEXT

Concurrent schwannoma and meningioma arising in the high cervical level mimicking a single dumbbell-shaped tumor is significantly rare, most of them were found during the surgeries or postoperative histological findings unexpectedly. The specific feature of schwannoma and meningioma coexistence in high cervical level on MR images has not been clearly described yet.

FINDINGS

We presented four cases of concurrent extradural schwannoma and intradural meningioma mimicking a single dumbbell-shaped tumor arising in the high cervical level. There was no interconnection between intradural and extradural masses in any case. In MRI reviews, the signal intensity between intradural lesions and spinal cord was similar on T2 weighted MR images. However, on contrast-enhanced MR images, the intradural lesions were more enhanced than spinal cord and presented as crescent-shaped intradural minor lesions adjacent to the more significantly enhanced extradural major tumor. These MRI findings could not be easily identified without meticulous observation preoperatively. Postoperative pathological findings confirmed the discrete tumors arising in the same cervical level.

CONCLUSION

The comparison of signal intensity changes among the spinal cord, intradural tumor and extradural tumor between T2 weighted and contrast-enhanced MR images may be helpful to predict coexistent schwannoma and meningioma in the high cervical level preoperatively. Intradural exploration is highly recommended when less enhanced crescent-shaped intradural minor lesion was observed adjacent to the significantly enhanced dumbbell-shaped major tumor in preoperative MRI findings.

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.

Resection of Bilateral Symmetrical Multiple Level Cervical Ganglioneuroma in a 43-Year-Old Man, a Probable Case of Neurofibromatosis Type-1: Report of a Case and Review of Literature

Ganglioneuroma is a benign tumor, originating from sympathetic nervous system. Intradural and dumbbell shape spinal ganglioneuroma has been reported in the literature. In this study, we intend to present our case, a 43-year-old man with multiple cutaneous dimples—probably a Neurofibromatosis type-1 (NF-1) case—and subacute myelopathy, who presented with bilateral symmetric dumbbell shape C2/C3 and C4/C5 intradural extramedullary tumors. After resection, the pathologic feature was revealed as ganglioneuroma. We also reviewed the literature for similar cases, which revealed our case to be the 9th bilateral and symmetrical spinal GN, all of which in cervical region; the 5th involving multiple level (the 3rd multiple bilateral symmetrical involvement), the 3rd extending intradurally, and the first case of involving all cervical nerve root ganglions in different sizes. Bilateral symmetrical spinal GNs have also appeared to have different body location, geographic, and gender distribution.

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 (T2max) and mean (T2mean) 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 T2max and 81.1%, 88.2%, 93.8%, and 68.2% for T2mean. The maximum SI value on contrast-enhanced T1W (T1CEmax) 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 T1CEmax and 81.1%, 88.2%, 93.8%, and 68.2% for rTF. Conclusions: Quantitative SI values (T2max, T2mean, T2min, T1CEmax, rTF) can be used to differentiate intradural-extramedullary schwannomas from meningiomas.

Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection From Magnetic Resonance Imaging

STUDY DESIGN

A retrospective analysis of magnetic resonance imaging (MRI) was conducted.

OBJECTIVE

This study aims to develop an automated system for the detection of spinal schwannoma, by employing deep learning based on object detection from MRI. The performance of the proposed system was verified to compare the performances of spine surgeons.

SUMMARY OF BACKGROUND DATA

Several MRI scans were conducted for the diagnoses of patients suspected to suffer from spinal diseases. Typically, spinal diseases do not involve tumors on the spinal cord, although a few tumors may exist at the unexpectable level or without symptom by chance. It is difficult to recognize these tumors; in some cases, these tumors may be overlooked. Hence, a deep learning approach based on object detection can minimize the probability of overlooking these tumors.

METHODS

Data from 50 patients with spinal schwannoma who had undergone MRI were retrospectively reviewed. Sagittal T1- and T2-weighted magnetic resonance imaging (T1WI and T2WI) were used in the object detection training and for validation. You Only Look Once version3 was used to develop the object detection system, and its accuracy was calculated. The performance of the proposed system was compared to that of two doctors.

RESULTS

The accuracies of the proposed object detection based on T1W1, T2W1, and both T1W1 and T2W1 were 80.3%, 91.0%, and 93.5%, respectively. The accuracies of the doctors were 90.2% and 89.3%.

CONCLUSION

Automated object detection of spinal schwannoma was achieved. The proposed system yielded a high accuracy that was comparable to that of the doctors.Level of Evidence: 4.

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

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

Objectives

To retrospectively determine the diagnostic ability of MRI in differentiating between intradural extramedullary spinal schwannomas and meningiomas.

Methods

A total of 199 patients with spinal intradural extramedullary tumors who underwent preoperative contrast-enhanced MRI between January 2012 and December 2018 were included in this study. Two radiologists independently analyzed the presence of cystic change, dural tail sign, and neural foraminal extension. Clinical and MRI features between the two groups were compared by univariable and multivariable analyses using logistic regression. Interobserver agreements were calculated using kappa statistics.

Results

Patients with schwannoma showed significantly higher frequency of cystic change (96% vs 24%, P < 0.001), neural foraminal extension (29% vs 3%, P = 0.001), and lumbar location (41% vs 5%, P = 0.008). Patients with meningioma showed significantly higher frequency of dural tail sign (64% vs 1%, P < 0.001), thoracic location (75% vs 31%, P = 0.007), older age (59.7 years vs 47.6 years, P < 0.001), higher female predominance (83% vs 50%, P < 0.001), and smaller size (19.8 cm vs 28.8 cm, P < 0.001). Multivariable analysis showed that cystic change (P < 0.001; odds ratio [OR], 0.02), dural tail sign (P < 0.001; OR, 36.23), age (P = 0.032; OR, 1.06), and lumbar location (P = 0.006; OR, 0.02) were independent factors. Interobserver agreements were almost perfect for all analyses.

Conclusions

MRI features were useful in differentiating between intradural extramedullary schwannomas from meningiomas. The presence of cystic change and dural tail sign were independently significant discriminators.

A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma

STUDY DESIGN

Retrospective analysis of magnetic resonance imaging (MRI).

OBJECTIVE

The aim of this study was to evaluate the performance of our convolutional neural network (CNN) in differentiating between spinal schwannoma and meningioma on MRI. We compared the performance of the CNN and that of two expert radiologists.

SUMMARY OF BACKGROUND DATA

Preoperative discrimination between spinal schwannomas and meningiomas is crucial because different surgical procedures are required for their treatment. A deep-learning approach based on CNNs is gaining interest in the medical imaging field.

METHODS

We retrospectively reviewed data from patients with spinal schwannoma and meningioma who had undergone MRI and tumor resection. There were 50 patients with schwannoma and 34 patients with meningioma. Sagittal T2-weighted magnetic resonance imaging (T2WI) and sagittal contrast-enhanced T1-weighted magnetic resonance imaging (T1WI) were used for the CNN training and validation. The deep learning framework Tensorflow was used to construct the CNN architecture. To evaluate the performance of the CNN, we plotted the receiver-operating characteristic (ROC) curve and calculated the area under the curve (AUC). We calculated and compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN and two board-certified radiologists.

RESULTS

. The AUC of ROC curves of the CNN based on T2WI and contrast-enhanced T1WI were 0.876 and 0.870, respectively. The sensitivity of the CNN based on T2WI was 78%; 100% for radiologist 1; and 95% for radiologist 2. The specificity was 82%, 26%, and 42%, respectively. The accuracy was 80%, 69%, and 73%, respectively. By contrast, the sensitivity of the CNN based on contrast-enhanced T1WI was 85%; 100% for radiologist 1; and 96% for radiologist 2. The specificity was 75%, 56, and 58%, respectively. The accuracy was 81%, 82%, and 81%, respectively.

CONCLUSION

We have successfully differentiated spinal schwannomas and meningiomas using the CNN with high diagnostic accuracy comparable to that of experienced radiologists.

LEVEL OF EVIDENCE

4.

Imaging diagnosis for intervertebral disc

Various functional magnetic resonance imaging (MRI) techniques have been investigated in recent years and are being used in clinical practice for the patients with low back pain (LBP). MRI is an important modality for diagnosing intervertebral disc (IVD) degeneration. In recent years, there have been several reported attempts to use MRI T2 mapping and MRI T1ρ mapping to quantify lumbar disc degeneration. MRI T2 mapping involves digitizing water content, proteoglycan content, and collagen sequence breakdown as relaxation times (T2 values) at each site. These digitized values are used to create a map, that is, then used to quantitatively evaluate the metabolite concentrations within IVD tissues. MRI T2 mapping utilizes the T2 relaxation time to quantify moisture content and the collagen sequence breakdown. MRI T1ρ mapping digitizes water molecule dispersion within the cartilaginous matrix to evaluate the degree of cartilaginous degeneration. Magnetic resonance spectroscopy is a less-invasive diagnostic test that provides biochemical information. Adequate analysis of the IVD has not yet been performed, although there are indications of a relationship between the adipose content of the multifidus muscle in the low back and LBP. The ultra short TE technique has been recently used to investigate lumbar cartilaginous endplates. Unlike diagnosis based on contrast-enhanced images of the IVD, which depends on the recurrence of pain that is determined subjectively, MRI-based diagnosis is less-invasive and based on objective imaging findings. It is therefore expected to play a key role in the diagnostic imaging of IVD conditions in the future.

Differentiation between intraspinal schwannoma and meningioma by MR characteristics and clinic features

OBJECTIVES

To retrospectively review the MRI characteristics and clinic features and evaluate the effectiveness of MR imaging in differentiating intraspinal schwannomas and meningiomas, with the excised histopathologic findings as the reference standard.

MATERIALS AND METHODS

One hundred and four schwannomas (M/F, 57:47) and 53 meningiomas (M/F, 13:40) underwent MR examinations before surgical treatment. Simple clinic data and imaging findings were considered:(a) location (craniocaudal and axial), (b) size, (c) morphology, (d) dural contact, (e) signal characteristics, (f) enhancement degree and patterns. The usefulness of the algorithm for differential diagnosis was examined between the two tumors.

RESULTS

Interobserver agreement was good (κ = 0.7-0.9). Ten cases meningiomas demonstrated multiple lesions. There was a female predominance in the meningiomas (P < 0.001). Meningiomas predominantly were located in the ventral or anterolateral areas of thoracic regions, while schwannomas in the posterolateral areas of the thoracic and the lumbar regions (P < 0.001). Mean size of the lesions was 1.47 ± 0.36 cm for meningioma, and 2.02 ± 1.13 cm for schwannoma (P < 0.001). A dumbbell shape with intervertebral foramen widening could detect schwannomas, while the "dural tail sign" did meningiomas (P < 0.001). Hypointense and miscellaneous signal implied meningioma on T₁WIs (P < 0.001). Isointense was more frequently observed in the meningiomas, while the fluid signal intensity and miscellaneous signal in the schwannomas on T₂WIs (P < 0.001). Schwannomas usually manifested rim enhancement, while meningiomas diffuse enhancement (P = 0.005). There were six variables including the logistic equation (age, size, dural tail sign, morphology, T₂WI, and axial location). The accuracy of the algorithm in diagnosis of schwannomas was 87.1%.

CONCLUSIONS

Combination of clinic data and MRI performs significantly for differentiating between intraspinal meningiomas and schwannomas.