Prediction of transient tumor enlargement using MRI tumor texture after radiosurgery on vestibular schwannoma

Emerging strategies for the prediction of behaviour, growth, and treatment response in vestibular schwannoma

Vestibular schwannoma (VS) can present several management challenges for the clinician. Their unpredictable potential for growth creates uncertainty regarding when active treatment should be initiated, and once growth is confirmed which treatment option should be adopted, notably surgery or radiotherapy, and in particular stereotactic radiosurgery (SRS). The obvious benefits of SRS would ideally come with the ability to reliably predict long-term radiosurgery response/failure. Differentiation from temporary post-treatment phenomena such as transient tumour expansion or reactive swelling remains an unmet need. More powerful again would be the pre-treatment identification of which tumours will respond to radiosurgery and which will not. Over the past decade, there has been emerging interest in the development of non-invasive biomarkers, including imaging, for predicting growth and treatment response in VS. Alongside clinical radiographic predictors for VS growth such as extracanalicular tumour location and growth in the first year, studies have shown potential promise for advanced MRI and blood-based biomarkers that capture pathophysiological mechanism behind VS growth. Emerging interest in radiomics-based analyses of routinely acquired MRI, and the use of physiological imaging techniques such as dynamic-contrast enhanced MRI for pre- and post-treatment evaluation of tumour microvasculature and microstructure holds promise for revolutionizing this area. This article explores the current state of identifying VS growth at initial presentation, predicting treatment response to SRS and detecting early treatment failure, and finally the potential for developing more personalized patient selection for drug therapies, including bevacizumab, as well as emerging novel therapeutics for these tumours.

Dynamics of tumor evolution after Gamma Knife radiosurgery for sporadic vestibular schwannoma: Defining volumetric patterns characterizing individual trajectory

BACKGROUND

The definition of tumor control and treatment failure after Gamma Knife radiosurgery (GKRS) for vestibular schwannoma (VS) is still debated. The lack of knowledge on the dynamics of tumor evolution can lead to misinterpretation and subsequent inappropriate second treatment. The aim of this study was to evaluate the post-GKRS dynamics of the evolution of tumor volume and characterize volumetric patterns.

METHODS

We included patients with sporadic VS treated by GKRS with an MRI follow-up of a minimum of 3 years. A clustering was performed in 2 steps: Definition of the patterns of evolution based on a subset of patients with the most comprehensive follow-up, and then the assignment of the remaining patients on a best-fit basis. The minimum length of follow-up was assessed by measuring the consistency of the clusters over time (adjusted rand index and normalized mutual information). An analysis of the discriminant variables was finally performed.

RESULTS

A total of 1607 patients were included (median follow-up: 67 months). Five patterns were defined with 1 pattern gathering almost all cases of treatment failure. The clustering at 5 years afforded the highest consistency with long-term follow-up. Discriminant variables for clusters were as follows: sex, initial symptoms, delay of diagnosis, Koos grading, fundus invasion, and number of isocenters.

CONCLUSIONS

The definition of these robust distinct patterns is likely to help the physicians tremendously to distinguish tumor control from potential failure. We advocate for no retreatment decision before 5 years post-GKRS. Further investigations are required to decide if the dynamics of evolution can be predicted at GKRS on an individual basis.

Applications and Integration of Radiomics for Skull Base Oncology

Radiomics, a quantitative approach to extracting features from medical images, represents a new frontier in skull base oncology. Novel image analysis approaches have enabled us to capture patterns from images imperceptible by the human eye. This rich source of data can be combined with a range of clinical features, holding the potential to be a noninvasive source of biomarkers. Applications of radiomics in skull base pathologies have centered around three common tumor classes: meningioma, sellar/parasellar tumors, and vestibular schwannomas. Radiomic investigations can be categorized into five domains: tumor detection/segmentation, classification between tumor types, tumor grading, detection of tumor features, and prognostication. Various computational architectures have been employed across these domains, with deep-learning methods becoming more common versus machine learning. Across radiomic applications, contrast-enhanced T1-weighted MRI images remain the most utilized sequence for model development. Efforts to standardize and connect radiomic features to tumor biology have facilitated more clinically applicable radiomic models. Despite the advancement in model performance, several challenges continue to hinder translatability, including small sample sizes and model training on homogenous single institution data. To recognize the potential of radiomics for skull base oncology, prospective, multi-institutional collaboration will be the cornerstone for a validated radiomic technology.

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of spontaneous tumors and the automated delineation or segmentation of lesions. These applications show potential for extension to longitudinal treatment follow-up. Additionally, the chapter highlights the established use of machine learning algorithms, particularly those incorporating radiomic-based analysis, in predicting treatment outcomes. The discussion encompasses current achievements, existing limitations, and the need for further investigation in the dynamic intersection of AI and radiosurgery.

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.

The study of predictive factors for the evolution of vestibular schwannomas

PURPOSE

The primary objective was to determine whether the analysis of textural heterogeneity of vestibular schwannomas on MRI at diagnosis was predictive of their radiological evolutivity. The secondary objective was to determine whether some clinical or radiological factors could also be predictive of growth.

METHODS

We conducted a pilot, observational and retrospective study of patients with a vestibular schwannoma, initially monitored, between April 2001 and November 2019 within the Oto-Neurosurgical Institute of Champagne Ardenne, Texture analysis was performed on gadolinium injected T1 and CISS T2 MRI sequences and six parameters were extracted: mean greyscale intensity, standard deviation of the greyscale histogram distribution, entropy, mean positive pixels, skewness and kurtosis, which were analysed by the Lasso method, using statistically penalised Cox models. Extrameatal location, tumour necrosis, perceived hearing loss < 2 years with objectified tone audiometry asymmetry, tinnitus at diagnosis, were investigated by the Log-Rank test to obtain univariate survival analyses.

RESULTS

78 patients were included and divided into 2 groups: group A comprising 39 "stable patients", and B comprising the remaining 39 "progressive patients". Independent analysis of the texture factors did not predict the growth potential of vestibular schwannomas. Among the clinical or radiological signs of interest, hearing loss < 2 years was identified as a prognostic factor for tumour progression with a significant trend (p = 0.05).

CONCLUSIONS

This study did not identify an association between texture analysis and vestibular schwannomas growth. Decreased hearing in the 2 years prior to diagnosis appears to predict potential radiological progression.

Pseudoprogression of Vestibular Schwannoma after Stereotactic Radiosurgery with Cyberknife®: Proposal for New Response Criteria

(1) Background: Transient increase in volume of vestibular schwannomas (VS) after stereotactic radiosurgery (SRS) is common and complicates differentiation between treatment-related changes (pseudoprogression, PP) and tumor recurrence (progressive disease, PD). (2) Methods: Patients with unilateral VS (n = 63) underwent single fraction robotic-guided SRS. Volume changes were classified according to existing RANO criteria. A new response type, PP, with a >20% transient increase in volume was defined and divided into early (within the first 12 months) and late (>12 months) occurrence. (3) Results: The median age was 56 (range: 20-82) years, the median initial tumor volume was 1.5 (range: 0.1-8.6) cm³. The median radiological and clinical follow-up time was 66 (range: 24-103) months. Partial response was observed in 36% (n = 23), stable disease in 35% (n = 22) and PP in 29% (n = 18) of patients. The latter occurred early (16%, n = 10) or late (13%, n = 8). Using these criteria, no case of PD was observed. (4) Conclusion: Any volume increase after SRS for vs. assumed to be PD turned out to be early or late PP. Therefore, we propose modifying RANO criteria for SRS of VS, which may affect the management of vs. during follow-up in favor of further observation.

Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction

BACKGROUND

Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery.

METHODS

Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed.

RESULTS

AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application.

CONCLUSIONS

Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.

The effect of tumor shape irregularity on Gamma Knife treatment plan quality and treatment outcome: an analysis of 234 vestibular schwannomas

The primary aim of Gamma Knife (GK) radiosurgery is to deliver high-dose radiation precisely to a target while conforming to the target shape. In this study, the effects of tumor shape irregularity (TSI) on GK dose-plan quality and treatment outcomes were analyzed in 234 vestibular schwannomas. TSI was quantified using seven different metrics including volumetric index of sphericity (VioS). GK treatment plans were created on a single GK-Perfexion/ICON platform. The plan quality was measured using selectivity index (SI), gradient index (GI), Paddick's conformity index (PCI), and efficiency index (EI). Correlation and linear regression analyses were conducted between shape irregularity features and dose plan indices. Machine learning was employed to identify the shape feature that predicted dose plan quality most effectively. The treatment outcome analysis including tumor growth control and serviceable hearing preservation at 2 years, were conducted using Cox regression analyses. All TSI features correlated significantly with the dose plan indices (P < 0.0012). With increasing tumor volume, vestibular schwannomas became more spherical (P < 0.05) and the dose plan indices varied significantly between tumor volume subgroups (P < 0.001 and P < 0.01). VioS was the most effective predictor of GK indices (P < 0.001) and we obtained 89.36% accuracy (79.17% sensitivity and 100% specificity) for predicting PCI. Our results indicated that TSI had significant effects on the plan quality however did not adversely affect treatment outcomes.

RAD51 Inhibitor and Radiation Toxicity in Vestibular Schwannoma

OBJECTIVE

To describe the RAD51 response (DNA repair) to radiation-induced DNA damage in patient-derived vestibular schwannoma (VS) cells and investigate the utility of RAD51 inhibitor (RI-1) in enhancing radiation toxicity.

STUDY DESIGN

Basic and translational science.

SETTING

Tertiary academic facility.

METHODS

VS tumors (n = 10) were cultured on 96-well plates and 16-well slides, exposed to radiation (0, 6, 12, or 18 Gy), and treated with RI-1 (0, 5, or 10 µM). Immunofluorescence was performed at 6 hours for γ-H2AX (DNA damage marker), RAD51 (DNA repair protein), and p21 (cell cycle arrest protein). Viability assays were performed at 96 hours, and capillary Western blotting was utilized to determine RAD51 expression in naïve VS tumors (n = 5).

RESULTS

VS tumors expressed RAD51. In cultured VS cells, radiation initiated dose-dependent increases in γ-H2AX and p21 expression. VS cells upregulated RAD51 to repair DNA damage following radiation. Addition of RI-1 reduced RAD51 expression in a dose-dependent manner and was associated with increased γ-H2AX levels and decreased viability in a majority of cultured VS tumors.

CONCLUSION

VS may evade radiation injury by entering cell cycle arrest and upregulating RAD51-dependent repair of radiation-induced double-stranded breaks in DNA. Although there was variability in responses among individual primary VS cells, RAD51 inhibition with RI-1 reduced RAD51-dependent DNA repair to enhance radiation toxicity in VS cells. Further investigations are warranted to understand the mechanisms of radiation resistance in VS and determine whether RI-1 is an effective radiosensitizer in patients with VS.

Whole Tumor Radiomics Analysis for Risk Factors Associated With Rapid Growth of Vestibular Schwannoma in Contrast-Enhanced T1-Weighted Images

OBJECTIVE

To investigate the features associated with rapid growth of vestibular schwannoma using radiomics analysis on magnetic resonance imaging (MRI) together with clinical factors.

METHODS

From August 2005 to February 2019, 67 patients with vestibular schwannoma underwent contrast-enhanced T1-weighted MRI at least twice as part of their diagnosis. After excluding 3 cases with an extremely short follow-up period of 15 days or less, 64 patients were finally enrolled in this study. Ninety-three texture features were extracted from the tumor image data using 3D Slicer software (http://www.slicer.org/). We determined the texture features that significantly affected maximal tumor diameter growth of more than 2 mm/year using Random Forest and Bounty. We also analyzed age and tumor size as clinical factors. We calculated the areas under the curve (AUCs) using receiver operating characteristic analysis for prediction models using texture, clinical, and mixed factors by Random Forest and 5-fold cross-validation.

RESULTS

Two texture features, low minimum signal and high inverse difference moment normalized (Idmn), were significantly associated with rapid growth of vestibular schwannoma. The mixed model of texture features and clinical factors offered the highest AUC (0.69), followed by the pure texture (0.67), and pure clinical (0.63) models. The minimum signal was the most important variable followed by tumor size, Idmn, and age.

CONCLUSIONS

Our radiomics analysis found that texture features were significantly associated with the rapid growth of vestibular schwannoma in contrast-enhanced T1-weighted images. The mixed model offered a higher diagnostic performance than the pure texture or clinical models.

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.

Pseudoprogression and peritumoral edema due to intratumoral necrosis after Gamma knife radiosurgery for meningioma

Peritumoral cerebral edema is reported to be a side effect that can occur after stereotactic radiosurgery. We aimed to determine whether intratumoral necrosis (ITN) is a risk factor for peritumoral edema (PTE) when gamma knife radiosurgery (GKRS) is performed in patients with meningioma. In addition, we propose the concept of pseudoprogression: a temporary volume expansion that can occur after GKRS in the natural course of meningioma with ITN. This retrospective study included 127 patients who underwent GKRS for convexity meningioma between January 2019 and December 2020. Risk factors for PTE and ITN were investigated using logistic regression analysis. Analysis of variance was used to determine whether changes in tumor volume were statistically significant. After GKRS, ITN was observed in 34 (26.8%) patients, and PTE was observed in 10 (7.9%) patients. When postoperative ITN occurred after GKRS, the incidence of postoperative PTE was 18.970-fold (p = 0.009) greater. When a 70% dose volume ≥ 1 cc was used, the possibility of ITN was 5.892-fold (p < 0.001) higher. On average, meningiomas with ITN increased in volume by 128.5% at 6 months after GKRS and then decreased to 94.6% at 12 months. When performing GKRS in meningioma, a 70% dose volume ≥ 1 cc is a risk factor for ITN. At 6 months after GKRS, meningiomas with ITN may experience a transient volume expansion and PTE, which are characteristics of pseudoprogression. These characteristics typically improve at 12 months following GKRS.

Fate of Residual Tumor After Subtotal Resection of a Previously Irradiated Vestibular Schwannoma: Long-Term Follow-Up of a Single-Institutional Series

BACKGROUND

The fate of residual tumor after salvage surgery for recurrent vestibular schwannoma (VS) after radiosurgery has not been elucidated so far. We reviewed our surgical series of salvage surgery for recurrent VS, with focus on the natural history of the residual tumor after salvage surgery.

METHODS

This study enrolled 14 patients who underwent salvage surgical resection in our institute and were followed up for >12 months.

RESULTS

The study included 3 men and 11 women with a median age of 55 years (range: 16-70 years). The median pre-stereotactic radiosurgery tumor volume was 6591 mm³. All patients were treated using gamma knife radiosurgery. The median duration from gamma knife radiosurgery to surgery was 52 months (range: 10-116 months). Solid tumor growth and cyst formation were observed in 6 (42.9%) and 8 (57.1%) patients, respectively. Subtotal resection and partial resection were performed in 13 (92.8%) patients, and gross total resection was achieved in only one (7.2%) patient. Postoperative facial paresis and surgical complication occurred in 5 (35.7%) and 2 (14.3%) patients, respectively. After salvage resection for irradiated VS, no patient showed tumor progression or recurrence during the follow-up period (13 subtotal/partial resections and 1 total resection). In addition, 2 patients in the subtotal resection group showed residual tumor shrinkage after salvage surgery during the follow-up period.

CONCLUSIONS

The behavior of residual tumors after salvage surgery for irradiated VS was stable. Adjuvant treatment for these residual tumors may not be necessary.

Vestibular Schwannomas

Vestibular Schwannomas are well treated by GKNS. This can be used alone for tumors up to 20cm³. For larger tumors subtotal, intracapsular resection followed by GKNS a few months later would seem to give the best results. While there remain disagreements relating to optimal treatment for VSs among colleagues using different techniques, there are indications that these are becoming less confrontational. The evidence in this chapter suggests that early GKNS intervention results in better hearing preservation and tumor control in small tumors. The evidence in favor of "wait and see" depends on series reporting on changes in tumor size using suboptimal measurements. It is more important to record the fate of hearing, and this would seem to be better preserved following early GKNS. The results of GKNS for NF2 are by no means as good as could be wished but would seem to be superior to those of microsurgery. The importance of screening of family members cannot be over emphasized.

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

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Pseudoprogression may be a late phenomenon after radiosurgery.

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

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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.

The Effectiveness of Diffusion Tensor Imaging in Determining Radiological Response after Radiosurgery in Patients with Vestibular Schwannoma

BACKGROUND

The effectiveness of Diffusion Tensor Imaging (DTI) in demonstrating functional changes in the tumor in determining the response to treatment after radiosurgery in patients with vestibular schwannoma (VS) is not clear yet.

OBJECTIVE

The study aimed to determine the change in total tumor volume (TTV) in terms of radiological response in patients who had VS and were treated with radiosurgery and investigated the relationship between the TTV, follow-up times and DTI parameters.

METHODS

Thirty-one patients were assessed using DTI and MRI. TTV, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) were calculated. Patients were divided into three groups: those who responded to the treatment (group 1) (n=11), those who did not (group 0) (n=9) and those who remained stable (group 2) (n=11).

RESULTS

The mean duration of follow-up was 28.81±14 months. ADC values increased in patients with VS after radiosurgery (p=0.004). There was no statistical difference in the FA values. A significant reduction in TTV after radiosurgery was detected in group 1 (p=0.003). ADC values increased significantly after radiosurgery in group 2 (p=0.04). Although there were no significant differences, ADC values after radiosurgery increased in group 1 and group 0.

CONCLUSIONS

ADC values continuously increase due to radiation damage in the period before the tumor volume shrinks after radiosurgery. We think that it is not appropriate to diagnose inadequate treatment or progression only when TTV is evaluated in terms of response to treatment in the early period after radiosurgery.

Radiomics-Based Prediction of Long-Term Treatment Response of Vestibular Schwannomas Following Stereotactic Radiosurgery

OBJECTIVE

Stereotactic radiosurgery (SRS) is one of the treatment modalities for vestibular schwannomas (VSs). However, tumor progression can still occur after treatment. Currently, it remains unknown how to predict long-term SRS treatment outcome. This study investigates possible magnetic resonance imaging (MRI)-based predictors of long-term tumor control following SRS.

STUDY DESIGN

Retrospective cohort study.

SETTING

Tertiary referral center.

PATIENTS

Analysis was performed on a database containing 735 patients with unilateral VS, treated with SRS between June 2002 and December 2014. Using strict volumetric criteria for long-term tumor control and tumor progression, a total of 85 patients were included for tumor texture analysis.

INTERVENTION(S)

All patients underwent SRS and had at least 2 years of follow-up.

MAIN OUTCOME MEASURE(S)

Quantitative tumor texture features were extracted from conventional MRI scans. These features were supplied to a machine learning stage to train prediction models. Prediction accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC) are evaluated.

RESULTS

Gray-level co-occurrence matrices, which capture statistics from specific MRI tumor texture features, obtained the best prediction scores: 0.77 accuracy, 0.71 sensitivity, 0.83 specificity, and 0.93 AUC. These prediction scores further improved to 0.83, 0.83, 0.82, and 0.99, respectively, for tumors larger than 5 cm.

CONCLUSIONS

Results of this study show the feasibility of predicting the long-term SRS treatment response of VS tumors on an individual basis, using MRI-based tumor texture features. These results can be exploited for further research into creating a clinical decision support system, facilitating physicians, and patients to select a personalized optimal treatment strategy.

Diffusion Tensor Imaging Features of the Auditory Pathways in Patients With Vestibular Schwannoma After Gamma Knife Radiosurgery

Objective

In this study, we aimed to investigate whether there is any change in diffusion tensor imaging (DTI) parameters in ipsilateral and contralateral auditory pathways after Gamma Knife radiosurgery (GKR) in patients with vestibular schwannoma (VS) and the relationship between radiosurgery variables.

Methods

Sixty-six patients were evaluated with MRI and DTI before and after GKR. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were measured from the bilateral lateral lemniscus (LL), inferior colliculus (IC), medial geniculate body (MGB), and Heschl's gyrus (HG).

Results

There was no significant difference in ADC and FA values obtained from bilateral LL, IC, and MGB before and after radiosurgery. However, there was a significant difference between pretreatment and post-radiosurgery contralateral HG ADC values. The ADC values obtained from the contralateral HG and IC positively correlated with the duration after radiosurgery. As the duration after radiosurgery increases, the difference between the ADC values obtained from ipsilateral and contralateral HG also increases.

Conclusion

The high ADC values in the contralateral HG after radiosurgery may indicate microstructural alterations such as demyelination and axonal loss. Radiation exposure doses to the brainstem and cochlea are the most important factors that can cause microstructural damage to the auditory pathways. When planning radiosurgery, extreme care should be taken to prevent the harmful effects of radiation on the auditory pathways.

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery

Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, - 0.31%, - 0.44%, - 0.19%, - 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.

MRI Texture Features are Associated with Vestibular Schwannoma Histology

OBJECTIVES/HYPOTHESIS

To determine if commonly used radiomics features have an association with histological findings in vestibular schwannomas (VS).

STUDY DESIGN

Retrospective case-series.

METHODS

Patients were selected from an internal database of those who had a gadolinium-enhanced T1-weighted MRI scan captured prior to surgical resection of VS. Texture features from the presurgical magnetic resonance image (MRI) were extracted, and pathologists examined the resected tumors to assess for the presence of mucin, lymphocytes, necrosis, and hemosiderin and used a validated computational tool to determine cellularity. Sensitivity, specificity, and positive likelihood ratios were also computed for selected features using the Youden index to determine the optimal cut-off value.

RESULTS

A total of 45 patients were included. We found significant associations between multiple MRI texture features and the presence of mucin, lymphocytes, hemosiderin, and cellularity. No significant associations between MRI texture features and necrosis were identified. We were able to identify significant positive likelihood ratios using Youden index cut-off values for mucin (2.3; 95% CI 1.2-4.3), hemosiderin (1.5; 95% CI 1.04-2.1), lymphocytes (3.8; 95% CI 1.2-11.7), and necrosis (1.5; 95% CI 1.1-2.2).

CONCLUSIONS

MRI texture features are associated with underlying histology in VS.

LEVEL OF EVIDENCE

3 Laryngoscope, 131:E2000-E2006, 2021.

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.