Fully automated tissue segmentation of the prescription isodose region delineated through the Gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clustering

Artificial intelligence-based automatic nidus segmentation of cerebral arteriovenous malformation on time-of-flight magnetic resonance angiography

OBJECTIVE

Accurate nidus segmentation and quantification have long been challenging but important tasks in the clinical management of Cerebral Arteriovenous Malformation (CAVM). However, there are still dilemmas in nidus segmentation, such as difficulty defining the demarcation of the nidus, observer-dependent variation and time consumption. The aim of this study isto develop an artificial intelligence model to automatically segment the nidus on Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) images.

METHODS

A total of 92patients with CAVM who underwent both TOF-MRA and DSA examinations were enrolled. Two neurosurgeonsmanually segmented the nidusonTOF-MRA images,which were regarded as theground-truth reference. AU-Net-basedAImodelwascreatedfor automatic nidus detectionand segmentationonTOF-MRA images.

RESULTS

The meannidus volumes of the AI segmentationmodeland the ground truthwere 5.427 ± 4.996 and 4.824 ± 4.567 mL,respectively. The meandifference in the nidus volume between the two groups was0.603 ± 1.514 mL,which wasnot statisticallysignificant (P = 0.693). The DSC,precision and recallofthe testset were 0.754 ± 0.074, 0.713 ± 0.102 and 0.816 ± 0.098, respectively. The linear correlation coefficient of the nidus volume betweenthesetwo groupswas 0.988, p < 0.001.

CONCLUSION

The performance of the AI segmentationmodel is moderate consistent with that of manual segmentation. This AI model has great potential in clinical settings, such as preoperative planning, treatment efficacy evaluation, riskstratification and follow-up.

The current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment

Background

Arteriovenous malformations (AVMs) are rare vascular anomalies involving a disorganization of arteries and veins with no intervening capillaries. In the past 10 years, radiomics and machine learning (ML) models became increasingly popular for analyzing diagnostic medical images. The goal of this review was to provide a comprehensive summary of current radiomic models being employed for the diagnostic, therapeutic, prognostic, and predictive outcomes in AVM management.

Methods

A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, in which the PubMed and Embase databases were searched using the following terms: (cerebral OR brain OR intracranial OR central nervous system OR spine OR spinal) AND (AVM OR arteriovenous malformation OR arteriovenous malformations) AND (radiomics OR radiogenomics OR machine learning OR artificial intelligence OR deep learning OR computer-aided detection OR computer-aided prediction OR computer-aided treatment decision). A radiomics quality score (RQS) was calculated for all included studies.

Results

Thirteen studies were included, which were all retrospective in nature. Three studies (23%) dealt with AVM diagnosis and grading, 1 study (8%) gauged treatment response, 8 (62%) predicted outcomes, and the last one (8%) addressed prognosis. No radiomics model had undergone external validation. The mean RQS was 15.92 (range: 10-18).

Conclusion

We demonstrated that radiomics is currently being studied in different facets of AVM management. While not ready for clinical use, radiomics is a rapidly emerging field expected to play a significant future role in medical imaging. More prospective studies are warranted to determine the role of radiomics in the diagnosis, prediction of comorbidities, and treatment selection in AVM management.

The new era of artificial intelligence in neuroradiology: current research and promising tools

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

Application of artificial intelligence in brain arteriovenous malformations: Angioarchitectures, clinical symptoms and prognosis prediction

BACKGROUND

Artificial intelligence (AI) has rapidly advanced in the medical field, leveraging its intelligence and automation for the management of various diseases. Brain arteriovenous malformations (AVM) are particularly noteworthy, experiencing rapid development in recent years and yielding remarkable results. This paper aims to summarize the applications of AI in the management of AVMs management.

METHODS

Literatures published in PubMed during 1999-2022, discussing AI application in AVMs management were reviewed.

RESULTS

AI algorithms have been applied in various aspects of AVM management, particularly in machine learning and deep learning models. Automatic lesion segmentation or delineation is a promising application that can be further developed and verified. Prognosis prediction using machine learning algorithms with radiomic-based analysis is another meaningful application.

CONCLUSIONS

AI has been widely used in AVMs management. This article summarizes the current research progress, limitations and future research directions.

Vascular compactness of unruptured brain arteriovenous malformation predicts risk of hemorrhage after stereotactic radiosurgery

The aim of the study was to investigate whether morphology (i.e. compact/diffuse) of brain arteriovenous malformations (bAVMs) correlates with the incidence of hemorrhagic events in patients receiving Stereotactic Radiosurgery (SRS) for unruptured bAVMs. This retrospective study included 262 adult patients with unruptured bAVMs who underwent upfront SRS. Hemorrhagic events were defined as evidence of blood on CT or MRI. The morphology of bAVMs was evaluated using automated segmentation which calculated the proportion of vessel, brain tissue, and cerebrospinal fluid in bAVMs on T2-weighted MRI. Compactness index, defined as the ratio of vessel to brain tissue, categorized bAVMs into compact and diffuse types based on the optimal cutoff. Cox proportional hazard model was used to identify the independent factors for post-SRS hemorrhage. The median clinical follow-ups was 62.1 months. Post-SRS hemorrhage occurred in 13 (5.0%) patients and one of them had two bleeds, resulting in an annual bleeding rate of 0.8%. Multivariable analysis revealed bAVM morphology (compact versus diffuse), bAVM volume, and prescribed margin dose were significant predictors. The post-SRS hemorrhage rate increased with larger bAVM volume only among the diffuse nidi (1.7 versus 14.9 versus 30.6 hemorrhage per 1000 person-years in bAVM volume < 20 cm3 versus 20-40 cm3 versus > 40 cm3; p = 0.022). The significantly higher post-SRS hemorrhage rate of Spetzler-Martin grade IV-V compared with grade I-III bAVMs (20.0 versus 3.3 hemorrhages per 1000 person-years; p = 0.001) mainly originated from the diffuse bAVMs rather than the compact subgroup (30.9 versus 4.8 hemorrhages per 1000 person-years; p = 0.035). Compact and smaller bAVMs, with higher prescribed margin dose harbor lower risks of post-SRS hemorrhage. The post-SRS hemorrhage rate exceeded 2.2% annually within the diffuse and large (> 40 cm3) bAVMs and the diffuse Spetzler-Martin IV-V bAVMs. These findings may help guide patient selection of SRS for the unruptured bAVMs.

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.

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.

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.

Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations

OBJECTIVE

The goal of the study was to define and quantify brain arteriovenous malformation (bAVM) compactness and to assess its effect on outcomes after Gamma Knife radiosurgery (GKRS) for unruptured bAVMs.

METHODS

Unsupervised machine learning with fuzzy c-means clustering was used to differentiate the tissue constituents of bAVMs on T2-weighted MR images. The percentages of vessel, brain, and CSF were quantified. The proposed compactness index, defined as the ratio of vasculature tissue to brain tissue, categorized bAVM morphology into compact, intermediate, and diffuse types according to the tertiles of this index. The outcomes of interest were complete obliteration and radiation-induced changes (RICs).

RESULTS

A total of 209 unruptured bAVMs treated with GKRS were retrospectively included. The median imaging and clinical follow-up periods were 49.2 and 72.3 months, respectively. One hundred seventy-three bAVMs (82.8%) achieved complete obliteration after a median latency period of 43.3 months. The rates of RIC and permanent RIC were 76.1% and 3.8%, respectively. Post-GKRS hemorrhage occurred in 14 patients (6.7%), resulting in an annual bleeding risk of 1.0%. Compact bAVM, smaller bAVM volume, and exclusively superficial venous drainage were independent predictors of complete obliteration. Diffuse bAVM morphology, larger bAVM volume, and higher margin dose were independently associated with RICs.

CONCLUSIONS

The compactness index quantitatively describes the compactness of unruptured bAVMs. Moreover, compact bAVMs may have a higher obliteration rate and a smaller risk of RICs than diffuse bAVMs. This finding could help guide decision-making regarding GKRS treatment for patients with unruptured bAVMs.

Segmentation techniques of brain arteriovenous malformations for 3D visualization: a systematic review

BACKGROUND

Visualization, analysis and characterization of the angioarchitecture of a brain arteriovenous malformation (bAVM) present crucial steps for understanding and management of these complex lesions. Three-dimensional (3D) segmentation and 3D visualization of bAVMs play hereby a significant role. We performed a systematic review regarding currently available 3D segmentation and visualization techniques for bAVMs.

METHODS

PubMed, Embase and Google Scholar were searched to identify studies reporting 3D segmentation techniques applied to bAVM characterization. Category of input scan, segmentation (automatic, semiautomatic, manual), time needed for segmentation and 3D visualization techniques were noted.

RESULTS

Thirty-three studies were included. Thirteen (39%) used MRI as baseline imaging modality, 9 used DSA (27%), and 7 used CT (21%). Segmentation through automatic algorithms was used in 20 (61%), semiautomatic segmentation in 6 (18%), and manual segmentation in 7 (21%) studies. Median automatic segmentation time was 10 min (IQR 33), semiautomatic 25 min (IQR 73). Manual segmentation time was reported in only one study, with the mean of 5-10 min. Thirty-two (97%) studies used screens to visualize the 3D segmentations outcomes and 1 (3%) study utilized a heads-up display (HUD). Integration with mixed reality was used in 4 studies (12%).

CONCLUSIONS

A golden standard for 3D visualization of bAVMs does not exist. This review describes a tendency over time to base segmentation on algorithms trained with machine learning. Unsupervised fuzzy-based algorithms thereby stand out as potential preferred strategy. Continued efforts will be necessary to improve algorithms, integrate complete hemodynamic assessment and find innovative tools for tridimensional visualization.

Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis

In the field of biomedicine, enormous data are generated in a structured and unstructured form every day. Soft computing techniques play a major role in the interpretation and classification of the data to make appropriate decisions for making policies. The field of medical science and biomedicine needs efficient soft computing-based methods which can process all kind of data such as structured data, categorical data, and unstructured data to generate meaningful outcome for decision-making. The soft-computing methods allow clustering of similar data, classification of data, predictions from big-data analysis, and decision-making on the basis of analysis of data. A novel method is proposed in the paper using soft-computing methods where clustering mechanisms and classification mechanisms are used to process the biomedicine data for productive outcomes. Fuzzy logic and C-means clustering are devised as a collaborative approach to analyze the biomedicine data by reducing the time and space complexity of the clustering solutions. This research work is considering categorical data, numeric data, and structured data for the interpretation of data to make further decisions. Timely decisions are very important especially in the field of biomedicine because human health and human lives are involved in this field and delays in decision-making may cause threats to human lives. The COVID-19 situation was a recent example where timely diagnosis and interpretations played significant roles in saving the lives of people. Therefore, this research work has attempted to use soft computing techniques for the successful clustering of similar medical data and for quicker interpretation of data to support the decision-making processes related to medical fields.

Assessment of gamma knife radiosurgery for unruptured cerebral arterioveneus malformations based on multi-parameter radiomics of MRI

OBJECTIVE

The treatment of Gamma knife radiosurgery (GKS) for unruptured Arteriovenous Malformations (AVM) remains controversial. A safe, effective and non-invasive method to predict outcome seems attractive for GKS. The purpose of this study was to develop and validate a MRI based multi-parameter radiomics model predicting the outcome of GKS for unruptured AVM.

METHODS

Eighty-eight unruptured AVM patients who initial underwent GKS between January 2011 and December 2016 in our hospital were included in this retrospective study. Patients were divided into two groups named as favourable and unfavourable outcome, according to the clinical outcome. Favourable outcome was defined as obliteration without post-SRS hemorrhage or permanent radiation-induced changes (RIC). Multivariate logistic regression analysis was used to select appropriate clinical features and construct a clinical predicting model. In terms of radiomic model, manually segmentation and radiomics extracted were performed on each AVM lesions. Finally, 1684 radiomics features were extracted and Recursive Feature Elimination (RFE) method combined with Random forest classifier were used for feature selection and model construction. The performance of the radiomics model was evaluated by the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the favourable group was further divided into early and late respond subgroup according to the time of obliteration evaluated by 2 years. The selected features were further compared according the respond time.

RESULTS

The median duration of neuroimaging follow-up was 65 months, 56 patients showed favourable outcome and 17 patients were observed obliteration within 2 years. The radiomics model constructed by 12 selected features achieved significant higher AUC of 0.88 (95% confidence interval 0.87-0.90) than traditional scoring system for predicting AVM outcome. Two selected radiomics features named "Dependence Variance" and "firstorder-Skewness" were found significant difference between the patients with early or late-respond.

CONCLUSIONS

The results suggest that the radiomics features could be successfully used for the pretreatment prediction of outcome for GKS in unruptured AVMs, which is helpful for decision-making process on unruptured AVM patients.

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

BACKGROUND

A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences.

OBJECTIVE

This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases.

METHODS

Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases.

RESULTS

For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion.

CONCLUSION

Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

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.

Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach

Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging/angiography (MRI/MRA) sequences in AVM patients, with the goal of facilitating target delineation. Twenty-three AVM patients who were evaluated for radiosurgery and underwent multi-parametric MRI/MRA were included. A hybrid semi-automated and manual approach was used to label MRI/MRAs with arteries, veins, brain parenchyma, cerebral spinal fluid (CSF), and embolized vessels. Next, these labels were used to train a convolutional neural network to perform this task. Imaging from 17 patients (6362 image slices) was used for training, and 6 patients (1224 slices) for validation. Performance was evaluated by Dice Similarity Coefficient (DSC). Classification performance was good for arteries, veins, brain parenchyma, and CSF, with DSCs of 0.86, 0.91, 0.98, and 0.91, respectively in the validation image set. Performance was lower for embolized vessels, with a DSC of 0.75. This demonstrates the proof of principle that accurate, high-resolution cerebrovascular-anatomical maps can be generated from multiparametric MRI/MRA. Clinical validation of their utility in radiosurgery planning is warranted.

The Irradiated Brain Volume Within 12 Gy Is a Predictor for Radiation-Induced Changes After Stereotactic Radiosurgery in Patients With Unruptured Cerebral Arteriovenous Malformations

PURPOSE

Our purpose was to determine whether the coverage of brain parenchyma within the 12 Gy radiosurgical volume (V12) correlates with the development of radiation-induced changes (RICs) in patients with unruptured cerebral arteriovenous malformations (AVM) after undergoing stereotactic radiosurgery (SRS).

METHODS AND MATERIALS

This study conducted regular follow-up examinations of 165 patients with unruptured AVMs who had previously undergone SRS. The RICs identified in T2-weighted magnetic resonance imaging (MRI) scans at any time point in the first 3 years after SRS were labeled "early RICs." The RICs identified in T2-weighted MRI scans at 5-year follow-up brain images were labeled "late RICs." Fully automated segmentation was used to analyze the MRI scans from these patients, whereupon the volume and proportion of brain parenchyma within the V12 was calculated. Logistic regression analysis was used to characterize the factors affecting the incidence of early and late RICs of any grade after SRS.

RESULTS

The median duration of follow-up was 70 months (range, 36-222). Early RICs were identified in 124 of the 165 patients with the highest grades as followed: grade 1 (103 patients), grade 2 (19 patients), and grade 3 (2 patients). Only 103 patients had more than 5 years follow-up, and late RICs were identified in 70 of 103 patients. Seventeen of 70 patients with late RICs were symptomatic. The median volume and proportion of brain parenchyma within the V12 was 22.4 cm³ (range, 0.6-63.9) and 58.7% (range, 18.4-76.8). Univariate analysis revealed that AVM volume and the brain volume within the V12 were correlated with the incidence of both early and late RICs after SRS. Multivariable analysis revealed that only the brain volume within the V12 was significantly associated with the incidence of early and late RICs after SRS.

CONCLUSIONS

In patients with unruptured AVM, the volume of brain parenchyma within the V12 was an important factor associated with the incidence of early and late RICs after SRS. Before SRS, meticulous radiosurgical planning to reduce brain parenchyma coverage within the V12 could reduce the risk of complications.

Predictive Factors of Radiation-Induced Changes Following Single-Session Gamma Knife Radiosurgery for Arteriovenous Malformations

We evaluated for possible predictors of radiation-induced changes (RICs) after gamma knife radiosurgery (GKRS) for arteriovenous malformations (AVMs). We identified the nidal component within AVMs to analyze the correlation between the volume of brain parenchyma within the 50% isodose line (IDL) and RICs. We retrospectively reviewed patients with AVMs who underwent a single-session of GKRS at our institution between 2007 and 2017 with at least a 2-year minimum follow-up. Follow-up magnetic resonance images were evaluated for newly developed T2 signal changes and the proportions of nidus and intervening parenchyma were quantified. A total of 180 AVM patients (98 males and 82 females) with a median age of 34 years were included in the present study. The overall obliteration rate was 67.8%. The median target volume was 3.65 cc. The median nidus and parenchyma volumes within the 50% IDL were 1.54 cc and 2.41 cc, respectively. RICs were identified in 79 of the 180 patients (43.9%). AVMs associated with previous hemorrhages showed a significant inverse correlation with RICs. In a multivariate analysis, RICs were associated with a higher proportion of brain parenchyma within the 50% IDL (hazard ratio (HR) 169.033; p < 0.001) and inversely correlated with the proportion of nidus volume within the 50% IDL (HR 0.006; p < 0.001). Our study identified that a greater proportion of brain tissue between the nidus within the 50% IDL was significantly correlated with RICs. Nidus angioarchitectural complexity and the absence of a prior hemorrhage were also associated with RICs. The identification of possible predictors of RICs could facilitate radiosurgical planning and treatment decisions as well as the planning of appropriate follow-up after GKRS; this could minimize the risk of RICs, which would be particularly beneficial for the treatment of incidentally found asymptomatic AVMs.

Does the Diffuseness of the Nidus Affect the Outcome of Stereotactic Radiosurgery in Patients with Unruptured Cerebral Arteriovenous Malformations?

BACKGROUND

We proposed an algorithm to automate the components within the identification of components within the nidus of cerebral arteriovenous malformations (AVMs) which may be used to analyze the relationship between its diffuseness and treatment outcomes following stereotactic radiosurgery (SRS).

OBJECTIVES

to determine the impact of the diffuseness of the AVM nidus on SRS outcomes.

METHODS

This study conducted regular follow-ups of 209 patients with unruptured AVMs who underwent SRS. The diffuseness of the AVM nidus was estimated by quantifying the proportions of vascular nidal component, brain parenchyma, and cerebrospinal fluid in T2-weighted MRIs. We used Cox regression analysis to characterize the association between nidal diffuseness and treatment outcomes in terms of obliteration rate and radiation-induced change (RICs) rate following SRS.

RESULTS

The median AVM volume was 20.7 cm3. The median duration of imaging follow-up was 51 months after SRS. The overall AVM obliteration rate was 68.4%. RICs were identified in 156 of the 209 patients (74.6%). The median proportions of the nidus of AVM and brain parenchyma components within the prescription isodose range were 30.2 and 52.2%, respectively. Cox regression multivariate analysis revealed that the only factor associated with AVM obliteration rate after SRS was AVM volume. However, a larger AVM volume (>20 mL) and a larger proportion of brain parenchyma (>50%) within the prescription isodose range were both correlated with a higher RIC rate following SRS.

CONCLUSIONS

The diffuseness of the nidus indeed appears to affect the RIC rate following SRS in patients with unruptured AVMs.