Magnetic resonance imaging characteristics and the prediction of outcome of vestibular schwannomas following Gamma Knife radiosurgery

Surgical and radiosurgical outcomes for Koos grade 3 vestibular schwannomas

This study aimed to reveal the preferred initial treatment for Koos grade 3 vestibular schwannomas (VS). We performed a two-institutional retrospective study on 21 patients with Koos grade 3 VS undergoing resection at Yokohama Medical Center and 37 patients undergoing radiosurgery at Yokohama Rosai Hospital from 2010 to 2021. Tumor control, complications, and functional preservation were compared. The median pre-treatment volume and follow-up duration were 2845 mm3 and 57.0 months, respectively, in the resection group and 2127 mm3 and 81.7 months, respectively, in the radiosurgery group. In the resection group, 16 (76.2%) underwent gross total resection, and three patients (14.3%) experienced regrowth; however, no one required additional treatment. In the radiosurgery group, the tumor control rate was 86.5%, and three cases (8.1%) required surgical resection because of symptomatic brainstem compression. Kaplan-Meier analyses revealed that tumors with delayed continuous enlargement and large thin-walled cysts were significantly associated with poor prognostic factors (p = 0.0027, p < 0.001). The pre-radiosurgery growth rate was also associated with the volume increase (p = 0.013). Two cases (9.5%) required additional operation due to complications such as post-operative hematoma and cerebrospinal fluid leaks in the resection group, whereas temporary cranial neuropathies were observed in the radiosurgery group. Two patients (9.5%) had poor facial nerve function (House-Brackmann grading grade 3) in the resection group, while no one developed facial paresis in the radiosurgery group. Trigeminal neuropathy improved only in the resection group.Radiosurgery can be considered for the treatment of Koos grade 3 VS for functional preservation. However, resection may also be considered for patients with severe trigeminal neuropathy or a high risk of volume increments, such as large thin-walled cysts and rapid pre-treatment growth.

Refining the predictive value of preoperative apparent diffusion coefficient (ADC) by whole-tumor analysis for facial nerve outcomes in vestibular schwannomas

BACKGROUND

Apparent diffusion coefficient (ADC) in MRI has been shown to correlate with postoperative House-Brackmann (HB) scores in patients with vestibular schwannoma despite limited methodology. To rectify limitations of single region of interest (ROI) sampling, we hypothesize that whole-tumor ADC histogram analysis will refine the predictive value of this preoperative biomarker related to postoperative facial nerve function.

METHODS

Of 155 patients who underwent resection of vestibular schwannoma (2014-2020), 125 patients were included with requisite clinical and radiographic data. After volumetric analysis and whole-tumor ADC histogram, regression tree analysis identified ADC cutoff for significant differences in HB grade. Outcomes were extent of resection, facial nerve function, hospital length of stay (LOS), and complications.

RESULTS

Regression tree analysis defined three quantitative ADC groups (× 10-6 mm2/s) as high (> 2248.77; HB 1.7), mid (1468.44-2248.77; HB 3.1), and low (< 1468.44; HB 2.3) range (p 0.04). The mid-range ADC group had significantly worse postoperative HB scores and longer hospital LOS. Large tumor volume was independently predictive of lower rates of gross total resection (p <0.0001), higher postoperative HB score (p 0.002), higher rate of complications (p 0.04), and longer LOS (p 0.003).

CONCLUSIONS

Whole-tumor histogram yielded a robust regression tree analysis that defined three ADC groups with significantly different facial nerve outcomes. This likely reflects tumor heterogeneity better than solid-tumor ROI sampling. Whole-tumor ADC warrants further study as a useful radiographic biomarker in patients with vestibular schwannoma who are considering surgical resection.

[Vestibular Schwannoma: Factors in Therapy Decision-Making]

The treatment of vestibular schwannomas (VS) has always posed a challenge for physicians. Three essential treatment principles are available: wait-and-scan, surgery, and stereotactic radiotherapy. In addition to the type of treatment, decisions must be made regarding the optimal timing of therapy, the combination of different treatment modalities, the potential surgical approach, and the type and intensity of radiation. Factors influencing the therapy decision include tumor location and size or stage, patient age, comorbidities, symptoms, postoperative hearing rehabilitation options, patient preferences, and, not least, the experience of the surgeons and the personnel and technical capabilities of the clinical site. This article begins with a brief overview of vestibular schwannomas, then outlines the fundamental interdisciplinary treatment options, and finally discusses the ENT (ear, nose, and throat)-relevant factors in the therapy decision.

Association Between Pseudoprogression of Vestibular Schwannoma After Radiosurgery and Radiological Features of Solid and Cystic Components

BACKGROUND AND OBJECTIVES

The pathophysiology of vestibular schwannoma (VS) pseudoprogression after Gamma Knife radiosurgery (GKRS) remains unclear. Radiological features in pretreatment magnetic resonance images may help predict VS pseudoprogression. This study used VS radiological features quantified using an automated segmentation algorithm to predict pseudoprogression after GKRS treatment.

METHODS

This is a retrospective study comprising 330 patients with VS who received GKRS. After image preprocessing and T2W/contrast-enhanced T1-weighted image (CET1W) image generation, with fuzzy C-means clustering, VSs were segmented into solid and cystic components and classified as solid and cystic. Relevant radiological features were then extracted. The response to GKRS was classified into "nonpseudoprogression" and "pseudoprogression/fluctuation". The Z test for two proportions was used to compare solid and cystic VS for the likelihood of pseudoprogression/fluctuation. Logistic regression was used to assess the correlation between clinical variables and radiological features and response to GKRS.

RESULTS

The likelihood of pseudoprogression/fluctuation after GKRS was significantly higher for solid VS compared with cystic VS (55% vs 31%, P < .001). For the entire VS cohort, multivariable logistic regression revealed that a lower mean tumor signal intensity (SI) in T2W/CET1W images was associated with pseudoprogression/fluctuation after GKRS ( P = .001). For the solid VS subgroup, a lower mean tumor SI in T2W/CET1W images ( P = .035) was associated with pseudoprogression/fluctuation after GKRS. For the cystic VS subgroup, a lower mean SI of the cystic component in T2W/CET1W images ( P = .040) was associated with pseudoprogression/fluctuation after GKRS.

CONCLUSION

Pseudoprogression is more likely to occur in solid VS compared with cystic VS. Quantitative radiological features in pretreatment magnetic resonance images were associated with pseudoprogression after GKRS. In T2W/CET1W images, solid VS with a lower mean tumor SI and cystic VS with a lower mean SI of cystic component were more likely to have pseudoprogression after GKRS. These radiological features can help predict the likelihood of pseudoprogression after GKRS.

Brazilian Society of Otology task force - Vestibular Schwannoma ‒ evaluation and treatment

OBJECTIVE

To review the literature on the diagnosis and treatment of vestibular schwannoma.

METHODS

Task force members were educated on knowledge synthesis methods, including electronic database search, review and selection of relevant citations, and critical appraisal of selected studies. Articles written in English or Portuguese on vestibular schwannoma were eligible for inclusion. The American College of Physicians' guideline grading system and the American Thyroid Association's guideline criteria were used for critical appraisal of evidence and recommendations for therapeutic interventions.

RESULTS

The topics were divided into 2 parts: (1) Diagnosis - audiologic, electrophysiologic tests, and imaging; (2) Treatment - wait and scan protocols, surgery, radiosurgery/radiotherapy, and systemic therapy.

CONCLUSIONS

Decision making in VS treatment has become more challenging. MRI can diagnose increasingly smaller tumors, which has disastrous consequences for the patients and their families. It is important to develop an individualized approach for each case, which highly depends on the experience of each surgical team.

Correlation between initial tumor enlargement and magnetic resonance imaging characteristics following linear accelerator-based stereotactic radiosurgery for acoustic neuromas

BACKGROUND

Initial tumor enlargement (or pseudoprogression) instead of true tumor progression is a common phenomenon in patients with acoustic neuromas who are treated with stereotactic radiosurgery (SRS). This phenomenon can affect clinical decision-making and patient management. This study assessed the correlation between initial tumor enlargement and magnetic resonance imaging characteristics in patients with acoustic neuromas who were treated with linear accelerator (LINAC)-based SRS. The long-term tumor control outcomes were also analyzed.

MATERIALS AND METHODS

In total, 330 patients with sporadic acoustic neuromas who were treated with LINAC SRS between March 2006 and March 2020 were retrospectively evaluated to assess their initial tumor enlargement. The tumors were divided into homogeneously enhanced, heterogeneously enhanced, and cystic types based on the morphological characteristics noted on magnetic resonance images. Tumor control was assessed in 275 patients with a follow-up duration of more than 2 years.

RESULTS

Initial enlargement was observed in 137 of 330 (41.5%) tumors as early as 3 months after LINAC SRS. Data analysis revealed that postoperative tumors with a residual volume lower than 2.5 cm3 had a lower incidence of initial enlargement (p = 0.039). No correlation was noted between the initial enlargement and morphological characteristics of tumors. In patients with a mean follow-up duration of 82.8 ± 37.2 months, heterogeneously enhanced tumors exhibited a lower control rate than homogeneously enhanced and cystic tumors (p = 0.045). No correlation was noted between initial enlargement and tumor control.

CONCLUSION

Initial enlargement can occur as early as 3 months after SRS. Postoperative residual tumors with a volume lower than 2.5 cm3 exhibit a lower incidence of initial enlargement. Heterogeneously enhanced tumors have a lower local control rate.

Using the deformity index of vital structures to predict outcome of patients with large vestibular schwannomas after Gamma Knife radiosurgery

PURPOSE

Microsurgery is the mainstay of treatment for large vestibular schwannomas (VS), but the benefits of radiosurgery remain incompletely defined. Here, we aim to use automated volumetric analysis software to quantify the degree of brain stem deformity to predict long-term outcomes of patients with large VS following GKRS.

METHODS

Between 2003 and 2020, 39 patients with large VS (volume > 8 cc) undergoing GKRS with a margin dose of 10-12 Gy were analyzed. The reconstruction 3D MRI was used to evaluate the extent of deformity for predicting the long-term outcome of patients.

RESULTS

Their mean tumor volume was 13.7 ± 6.3 cc, and their mean follow-up after GKRS was 86.7 ± 65.3 months. Favorable clinical outcome was observed in 26 (66.7%) patients, while 13 (33.3%) patients had treatment failure. Patients with small tumor volumes, low vital structure deformity indice [(TV/(BSV + CerV) and (TV + EV)/(BSV + CerV)], and long distance of tumor to the central line were more likely to have favorable clinical outcome after GKRS. Significant prognostic value was with tumor shrinkage ratio (< 50%) were CV, CV/TV, TV/CerV, (TV + EV)/(BSV + CerV), and the distance of tumor to the central line. In cox regression, favorable clinical outcome was correlated with the Charlson comorbidity index and cochlear dosage (both p < 0.05). In multivariant analysis, tumor regression was highly correlated with the CV/TV ratio (p < 0.001).

CONCLUSIONS

The brainstem deformity ratio is likely a useful index to assess the clinical and tumor regression outcomes. Clinical outcomes are multifactorial and the tumor regression was highly correlated with the ratio of cystic components.

Lesion delineation framework for vestibular schwannoma, meningioma and brain metastasis for gamma knife radiosurgery using stereotactic magnetic resonance images

BACKGROUND AND OBJECTIVE

GKRS is an effective treatment for smaller intracranial tumors with a high control rate and low risk of complications. Target delineation in medical MR images is essential in the planning of GKRS and follow-up. A deep learning-based algorithm can effectively segment the targets from medical images and has been widely explored. However, state-of-the-art deep learning-based target delineation uses fixed sizes, and the isotropic voxel size may not be suitable for stereotactic MR images which use different anisotropic voxel sizes and numbers of slices according to the lesion size and location for clinical GKRS planning. This study developed an automatic deep learning-based segmentation scheme for stereotactic MR images.

METHODS

We retrospectively collected stereotactic MR images from 506 patients with VS, 1,069 patients with meningioma and 574 patients with BM who had been treated using GKRS; the lesion contours and individual T1W+C and T2W MR images were extracted from the GammaPlan system. The three-dimensional patching-based training strategy and dual-pathway architecture were used to manage inconsistent FOVs and anisotropic voxel size. Furthermore, we used two-parametric MR image as training input to segment the regions with different image characteristics (e.g., cystic lesions) effectively.

RESULTS

Our results for VS and BM demonstrated that the model trained using two-parametric MR images significantly outperformed the model trained using single-parametric images with median Dice coefficients (0.91, 0.05 versus 0.90, 0.06, and 0.82, 0.23 versus 0.78, 0.34, respectively), whereas predicted delineations in meningiomas using the dual-pathway model were dominated by single-parametric images (median Dice coefficients 0.83, 0.17 versus 0.84, 0.22). Finally, we combined three data sets to train the models, achieving the comparable or even higher testing median Dice (VS: 0.91, 0.07; meningioma: 0.83, 0.22; BM: 0.84, 0.23) in three diseases while using two-parametric as input.

CONCLUSIONS

Our proposed deep learning-based tumor segmentation scheme was successfully applied to multiple types of intracranial tumor (VS, meningioma and BM) undergoing GKRS and for segmenting the tumor effectively from stereotactic MR image volumes for use in GKRS planning.

Long-term results of upfront, single-session Gamma Knife radiosurgery for large cystic vestibular schwannomas

Anecdotally, cystic vestibular schwannomas (cVSs) are regarded to have unpredictable biologic activity with poorer clinical results, and most studies showed a less favorable prognosis following surgery. While stereotactic radiosurgery (SRS) is a well-established therapeutic option for small- to medium-sized VSs, cVSs are often larger, thus making upfront SRS more complicated. The purpose of this retrospective study was to assess the efficacy and safety of upfront SRS for large cVSs. The authors reviewed the data of 54 patients who received upfront, single-session Gamma Knife radiosurgery (GKRS) with a diagnosis of large cVS (> 4 cm3). Patients with neurofibromatosis type 2, multiple VSs, or recurrent VSs and < 24 months of clinical and neuroimaging follow-up were excluded. Hearing loss (48.1%) was the primary presenting symptom. The majority of cVSs were Koos grade IV (66.7%), and the most prevalent cyst pattern was "mixed pattern of small and big cysts" (46.3%). The median time between diagnosis and GKRS was 12 months (range, 1-147 months). At GKRS, the median cVS volume was 6.95 cm3 (range, 4.1-22 cm3). The median marginal dose was 12 Gy (range, 10-12 Gy). The mean radiological and clinical follow-up periods were 62.2 ± 34.04 months (range, 24-169 months) and 94.9 ± 45.41 months (range, 24-175 months), respectively. At 2, 6, and 12 years, the tumor control rates were 100%, 95.7%, and 85.0%, respectively. Tumor shrinkage occurred in 92.6% of patients (n = 50), tumor volume remained stable in 5.6% of patients (n = 3), and tumor growth occurred in 1.9% of patients (n = 1). At a median follow-up of 53.5 months, the pre-GKRS tumor volume significantly decreased to 2.35 cm3 (p < 0.001). While Koos grade 3 patients had a greater possibility of attaining higher volume reduction, "multiple small thick-walled cyst pattern" and smaller tumor volumes decreased the likelihood of achieving higher volume reduction. Serviceable hearing (Gardner-Robertson Scale I-II) was present in 16.7% of patients prior to GKRS and it was preserved in all of these patients following GKRS. After GKRS, 1.9% of patients (n = 1) had new-onset trigeminal neuralgia. There was no new-onset facial palsy, hemifacial spasm, or hydrocephalus. Contrary to what was believed, our findings suggest that upfront GKRS seems to be a safe and effective treatment option for large cVSs.

Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review

The application of machine learning (ML) techniques to otolaryngology remains a topic of interest and prevalence in the literature, though no previous articles have summarized the current state of ML application to management and the diagnosis of lateral skull base (LSB) tumors. Subsequently, we present a systematic overview of previous applications of ML techniques to the management of LSB tumors. Independent searches were conducted on PubMed and Web of Science between August 2020 and February 2021 to identify the literature pertaining to the use of ML techniques in LSB tumor surgery written in the English language. All articles were assessed in regard to their application task, ML methodology, and their outcomes. A total of 32 articles were examined. The number of articles involving applications of ML techniques to LSB tumor surgeries has significantly increased since the first article relevant to this field was published in 1994. The most commonly employed ML category was tree-based algorithms. Most articles were included in the category of surgical management (13; 40.6%), followed by those in disease classification (8; 25%). Overall, the application of ML techniques to the management of LSB tumor has evolved rapidly over the past two decades, and the anticipated growth in the future could significantly augment the surgical outcomes and management of LSB tumors.

Quantification of tumor response of cystic vestibular schwannoma to Gamma Knife radiosurgery by using artificial intelligence

OBJECTIVE

Gamma Knife radiosurgery (GKRS) is a common treatment modality for vestibular schwannoma (VS). The ability to predict treatment response is important in patient counseling and decision-making. The authors developed an algorithm that can automatically segment and differentiate cystic and solid tumor components of VS. They also investigated associations between the quantified radiological features of each component and tumor response after GKRS.

METHODS

This is a retrospective study comprising 323 patients with VS treated with GKRS. After preprocessing and generation of pretreatment T2-weighted (T2W)/T1-weighted with contrast (T1WC) images, the authors segmented VSs into cystic and solid components by using fuzzy C-means clustering. Quantitative radiological features of the entire tumor and its cystic and solid components were extracted. Linear regression models were implemented to correlate clinical variables and radiological features with the specific growth rate (SGR) of VS after GKRS.

RESULTS

A multivariable linear regression model of radiological features of the entire tumor demonstrated that a higher tumor mean signal intensity (SI) on T2W/T1WC images (p < 0.001) was associated with a lower SGR after GKRS. Similarly, a multivariable linear regression model using radiological features of cystic and solid tumor components demonstrated that a higher solid component mean SI (p = 0.039) and a higher cystic component mean SI (p = 0.004) on T2W/T1WC images were associated with a lower SGR after GKRS. A larger cystic component proportion (p = 0.085) was associated with a trend toward a lower SGR after GKRS.

CONCLUSIONS

Radiological features of VSs on pretreatment MRI that were quantified using fuzzy C-means were associated with tumor response after GKRS. Tumors with a higher tumor mean SI, a higher solid component mean SI, and a higher cystic component mean SI on T2W/T1WC images were more likely to regress in volume after GKRS. Those with a larger cystic component proportion also trended toward regression after GKRS. Further refinement of the algorithm may allow direct prediction of tumor response.

Response prediction of vestibular schwannoma after gamma-knife radiosurgery using pretreatment dynamic contrast-enhanced MRI: a prospective study

OBJECTIVES

There are few known predictive factors for response to gamma-knife radiosurgery (GKRS) in vestibular schwannoma (VS). We investigated the predictive role of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters regarding the tumor response after GKRS in sporadic VS.

METHODS

This single-center prospective study enrolled participants between April 2017 and February 2019. We performed a volumetric measurement of DCE-MRI-derived parameters before GKRS. The tumor volume was measured in a follow-up MRI. The pharmacokinetic parameters were compared between responders and nonresponders according to 20% or more tumor volume reduction. Stepwise multivariable logistic regression analyses were performed, and the diagnostic performance of DCE-MRI parameters for the prediction of tumor response was evaluated by receiver operating characteristic curve analysis.

RESULTS

Ultimately, 35 participants (21 women, 52 ± 12 years) were included. There were 22 (62.9%) responders with a mean follow-up interval of 30.2 ± 5.7 months. K^trans (0.036 min^-1 vs. 0.057 min^-1, p = .008) and initial area under the time-concentration curve within 90 s (IAUC90) (84.4 vs. 143.6, p = .003) showed significant differences between responders and nonresponders. K^trans (OR = 0.96, p = .021) and IAUC90 (OR = 0.97, p = .004) were significant differentiating variables in each multivariable model with clinical variables for tumor response prediction. K^trans showed a sensitivity of 81.8% and a specificity of 69.2%, and IAUC90 showed a sensitivity of 100% and a specificity of 53.8% for tumor response prediction.

CONCLUSION

DCE-MRI (particularly K^trans and IAUC90) has the potential to be a predictive factor for tumor response in VS after GKRS.

KEY POINTS

•Pretreatment prediction of gamma-knife radiosurgery response in vestibular schwannoma is still challenging. •Dynamic contrast-enhanced MRI could have predictive value for the response of vestibular schwannoma after gamma-knife radiosurgery.

Long-term volumetric analysis of vestibular schwannomas following stereotactic radiotherapy: Practical implications for follow-up

•

Pseudoprogression may be a late phenomenon after radiosurgery.

•

Loss of central contrast enhancement is not predictive of tumor control.

•

No decision of salvage therapy should be made until the 6th year post-treatment.

Background and purpose

Transient tumor swelling is a well-known phenomenon following radiotherapy for vestibular schwannomas (VS). We analyzed the long-term volumetric changes of VS after LINAC radiosurgery, in order to determine a time interval during which a true tumor progression can be distinguished from a pseudoprogression.

Methods

Among 63 patients with VS treated by one fraction or fractionated radiotherapy, we selected 52 of them who had a minimal follow-up of 5 years. Maximal axial diameter and three-dimensional tumor volume were measured on each MRI scan. Volume changes were interpreted using different error margins ranging from 10 to 20%. Patients were categorized according to the tumor evolution pattern over time.

Results

Median follow-up was 83 months. One tumor (1.9%) remained stable and 26.9% had continuous shrinkage. Applying an error margin of 13%, a transient tumor enlargement was observed in 63.5% of patients, with a first peak at 6–12 months and a late peak at 3–4 years. A true progression was suspected in 4 (7.7%) patients, tumor regrowth starting after the 3rd or 4th year post-treatment. Only one patient required salvage radiotherapy.

Conclusion

Transient swelling of VS following radiotherapy is generally an early phenomenon but may occur late. In the first 5 years, a true tumor progression cannot be differentiated from a pseudoprogression. A significant tumor expansion observed on 3 sequential MRI scans after the 3rd year may be suggestive of treatment failure. Long-term follow-up is therefore mandatory and no decision of salvage treatment should be made until the 6th year.

Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks

Purpose

The first step in typical treatment of vestibular schwannoma (VS) is to localize the tumor region, which is time-consuming and subjective because it relies on repeatedly reviewing different parametric magnetic resonance (MR) images. A reliable, automatic VS detection method can streamline the process.

Methods

A convolutional neural network architecture, namely YOLO-v2 with a residual network as a backbone, was used to detect VS tumors from MR images. To heighten performance, T1-weighted–contrast-enhanced, T2-weighted, and T1-weighted images were combined into triple-channel images for feature learning. The triple-channel images were cropped into three sizes to serve as input images of YOLO-v2. The VS detection effectiveness levels were evaluated for two backbone residual networks that downsampled the inputs by 16 and 32.

Results

The results demonstrated the VS detection capability of YOLO-v2 with a residual network as a backbone model. The average precision was 0.7953 for a model with 416 × 416-pixel input images and 16 instances of downsampling, when both the thresholds of confidence score and intersection-over-union were set to 0.5. In addition, under an appropriate threshold of confidence score, a high average precision, namely 0.8171, was attained by using a model with 448 × 448-pixel input images and 16 instances of downsampling.

Conclusion

We demonstrated successful VS tumor detection by using a YOLO-v2 with a residual network as a backbone model on resized triple-parametric MR images. The results indicated the influence of image size, downsampling strategy, and confidence score threshold on VS tumor detection.

Pretreatment ADC predicts tumor control after Gamma Knife radiosurgery in solid vestibular schwannomas

BACKGROUND

Radiosurgery is a well-established treatment for vestibular schwannomas (VSs), but it is often difficult to identify which tumors will respond to treatment. We sought to determine whether pretreatment or posttreatment tumor apparent diffusion coefficient (ADC) values could predict tumor control in patients undergoing Gamma Knife radiosurgery (GKRS) and whether these values could differentiate between cases of pseudoprogression and cases of true progression in the early posttreatment period.

METHODS

We retrospectively identified patients who underwent GKRS for solid VSs between June 2008 and November 2016 and who had a minimum follow-up of 36 months. Pretreatment and posttreatment minimum, mean, and maximum ADC values were measured for the whole tumor volume and were compared between patients with tumor control and those with tumor progression. In patients with early posttreatment tumor enlargement, ADC values were compared between patients with pseudoprogression and those with true progression.

RESULTS

Of the 44 study patients, 34 (77.3%) demonstrated tumor control at final follow-up. Patients with tumor control had higher pretreatment minimum (1.35 vs 1.09; p = 0.008), mean (1.80 vs 1.45; p = 0.004), and maximum (2.41 vs 1.91; p = 0.011) ADC values than patients with tumor progression. ADC values did not differ between patients with pseudoprogression and those with true progression at early posttreatment follow-up.

CONCLUSIONS

ADC values may be helpful in predicting response to GKRS in patients with solid VSs but cannot predict which tumors will undergo pseudoprogression. Patients with higher pretreatment ADC values may be more likely to demonstrate posttreatment tumor control.

EANO guideline on the diagnosis and treatment of vestibular schwannoma

The level of evidence to provide treatment recommendations for vestibular schwannoma is low compared with other intracranial neoplasms. Therefore, the vestibular schwannoma task force of the European Association of Neuro-Oncology assessed the data available in the literature and composed a set of recommendations for health care professionals. The radiological diagnosis of vestibular schwannoma is made by magnetic resonance imaging. Histological verification of the diagnosis is not always required. Current treatment options include observation, surgical resection, fractionated radiotherapy, and radiosurgery. The choice of treatment depends on clinical presentation, tumor size, and expertise of the treating center. In small tumors, observation has to be weighed against radiosurgery, in large tumors surgical decompression is mandatory, potentially followed by fractionated radiotherapy or radiosurgery. Except for bevacizumab in neurofibromatosis type 2, there is no role for pharmacotherapy.

Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after Gamma Knife radiosurgery based on preradiosurgical MR radiomics

BACKGROUND AND PURPOSE

Gamma Knife radiosurgery (GKRS) is a safe and effective treatment modality with a long-term tumor control rate over 90% for vestibular schwannoma (VS). However, numerous tumors may undergo a transient pseudoprogression during 6-18 months after GKRS followed by a long-term volume reduction. The aim of this study is to determine whether the radiomics analysis based on preradiosurgical MRI data could predict the pseudoprogression and long-term outcome of VS after GKRS.

MATERIALS AND METHODS

A longitudinal dataset of patients with VS treated by single GKRS were retrospectively collected. Overall 336 patients with no previous craniotomy for tumor removal and a median of 65-month follow-up period after radiosurgery were finally included in this study. In total 1763 radiomic features were extracted from the multiparameteric MRI data before GKRS followed by the machine-learning classification.

RESULTS

We constructed a two-level machine-learning model to predict the long-term outcome and the occurrence of transient pseudoprogression after GKRS separately. The prediction of long-term outcome achieved an accuracy of 88.4% based on five radiomic features describing the variation of T2-weighted intensity and inhomogeneity of contrast enhancement in tumor. The prediction of transient pseudoprogression achieved an accuracy of 85.0% based on another five radiomic features associated with the inhomogeneous hypointensity pattern of contrast enhancement and the variation of T2-weighted intensity.

CONCLUSION

The proposed machine-learning model based on the preradiosurgical MR radiomics provides a potential to predict the pseudoprogression and long-term outcome of VS after GKRS, which can benefit the treatment strategy in clinical practice.

Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning

Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution of standard VS MR scan protocols and because some patients have non-homogeneous cystic areas within their tumors. In this study, we retrospectively collected multi-parametric MR images from 516 patients with VS; these were extracted from the Gamma Knife radiosurgery planning system and consisted of T1-weighted (T1W), T2-weighted (T2W), and T1W with contrast (T1W + C) images. We developed an end-to-end deep-learning-based method via an automatic preprocessing pipeline. A two-pathway U-Net model involving two sizes of convolution kernel (i.e., 3 × 3 × 1 and 1 × 1 × 3) was used to extract the in-plane and through-plane features of the anisotropic MR images. A single-pathway model that adopted the same architecture as the two-pathway model, but used a kernel size of 3 × 3 × 3, was also developed for comparison purposes. In addition, we used multi-parametric MR images with different image contrasts as the model training input in order to effectively segment tumors with solid as well as cystic parts. The results of the automatic segmentation demonstrated that (1) the two-pathway model outperformed single-pathway model in terms of dice scores (0.90 ± 0.05 versus 0.87 ± 0.07); both of them having been trained using the T1W, T1W + C and T2W anisotropic MR images, (2) the optimal single-parametric two-pathway model (dice score: 0.88 ± 0.06) was then trained using the T1W + C images, and (3) the two-pathway models trained using bi-parametric (T1W + C and T2W) and tri-parametric (T1W, T2W, and T1W + C) images outperformed the model trained using the single-parametric (T1W + C) images (dice scores: 0.89 ± 0.05 and 0.90 ± 0.05, respectively, larger than 0.88 ± 0.06) because it showed improved segmentation of the non-homogeneous parts of the tumors. The proposed two-pathway U-Net model outperformed the single-pathway U-Net model when segmenting VS using anisotropic MR images. The multi-parametric models effectively improved on the defective segmentation obtained using the single-parametric models by separating the non-homogeneous tumors into their solid and cystic parts.

How to radiologically identify a spontaneous regression of sporadic vestibular schwannoma?

Background

The natural history of sporadic vestibular schwannoma is unpredictable, with tumors growing, non-growing and even showing spontaneous regression in some rare cases.

Objective

This retrospective study aims to describe the radiologic signs characterizing and identifying the shrinking vestibular schwannoma.

Methods

Involution was considered to have occurred if tumor size had decreased by 2 mm or more on its largest diameter. All magnetic resonance imaging scans were reviewed for tumor size, internal auditory meatus size, and tumor characteristics. Volumetric measurements were performed on the first and last scan. Audiometric data were collected at the first and last visit.

Results

Fourteen patients with a confirmed spontaneous regression were included, with a mean follow-up of 5 ± 2.6 years. The mean shrinkage rate was 0.9 ± 0.59 mm/year on 2D measurements, and 0.2 ± 0.17 cm³/year on volumetric measurements, with a relative shrinkage of 40 ± 16.9%. Two remarkable radiologic features were observed: First, a festooned aspect, defined by multiple curves in the tumor outline, noticed in 12 cases (86%); second, the appearance of cerebrospinal fluid filling the internal auditory meatus, associated with an enlargement of the internal auditory meatus compared to the contralateral side, and observed in 10 out of 13 cases with internal auditory meatus invasion (77%). Those two aspects were associated in 64% of cases.

Conclusion

These two newly reported radiologic features could help neurosurgeons, oto-neurosurgeons and neuroradiologists to identify a spontaneous vestibular schwannoma involution at first visit. This could allow any treatment to be postponed, monitoring to be more widely spaced, and patients to be reassured.