A Machine Learning Model Predicts the Outcome of SRS for Residual Arteriovenous malformations after partial embolization- A Real-World Clinical Obstacle

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.

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.

Current perspectives and trends in the treatment of brain arteriovenous malformations: a review and bibliometric analysis

Background

Currently, there is a lack of intuitive analysis regarding the development trend, main authors, and research hotspots in the field of cerebral arteriovenous malformation treatment, as well as a detailed elaboration of possible research hotspots.

Methods

A bibliometric analysis was conducted on data retrieved from the Web of Science core collection database between 2000 and 2022. The analysis was performed using R, VOSviewer, CiteSpace software, and an online bibliometric platform.

Results

A total of 1,356 articles were collected, and the number of publications has increased over time. The United States and the University of Pittsburgh are the most prolific countries and institutions in the field. The top three cited authors are Kondziolka D, Sheehan JP, and Lunsford LD. The Journal of Neurosurgery and Neurosurgery are two of the most influential journals in the field of brain arteriovenous malformation treatment research, with higher H-index, total citations, and number of publications. Furthermore, the analysis of keywords indicates that "aruba trial," "randomised trial," "microsurgery," "onyx embolization," and "Spetzler-Martin grade" may become research focal points. Additionally, this paper discusses the current research status, existing issues, and potential future research directions for the treatment of brain arteriovenous malformations.

Conclusion

This bibliometric study comprehensively analyses the publication trend of cerebral arteriovenous malformation treatment in the past 20 years. It covers the trend of international cooperation, publications, and research hotspots. This information provides an important reference for scholars to further study cerebral arteriovenous malformation.

Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS)

A cerebral arteriovenous malformation (AVM) is a tangle of abnormal blood vessels that irregularly connects arteries and veins. Stereotactic radiosurgery (SRS) has been shown to be an effective treatment for AVM patients, but the factors associated with AVM obliteration remains a matter of debate. In this study, we aimed to develop a model that can predict whether patients with AVM will be cured 36 months after intervention by means of SRS and identify the most important predictors that explain the probability of being cured. A machine learning (ML) approach was applied using decision tree (DT) and logistic regression (LR) techniques on historical data (sociodemographic, clinical, treatment, angioarchitecture, and radiosurgery procedure) of 202 patients with AVM who underwent SRS at the Instituto de Radiocirugía del Perú (IRP) between 2005 and 2018. The LR model obtained the best results for predicting AVM cure with an accuracy of 0.92, sensitivity of 0.93, specificity of 0.89, and an area under the curve (AUC) of 0.98, which shows that ML models are suitable for predicting the prognosis of medical conditions such as AVM and can be a support tool for medical decision-making. In addition, several factors were identified that could explain whether patients with AVM would be cured at 36 months with the highest likelihood: the location of the AVM, the occupation of the patient, and the presence of hemorrhage.

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.

Long-term outcome of a tailored embolization strategy with Gamma Knife radiosurgery for high-grade brain arteriovenous malformations: a single-center experience

OBJECTIVE

The safety and efficacy of embolization with Gamma Knife radiosurgery (GKRS) for high-grade brain arteriovenous malformations (bAVMs) are uncertain. The purpose of this study was to elucidate the long-term outcome of a tailored embolization strategy with GKRS and identify the independent factors associated with bAVM obliteration.

METHODS

Between January 2014 and January 2017, a consecutive cohort of 159 patients with high-grade bAVMs who underwent embolization with GKRS was enrolled in this prospective single-center cohort study. All patients received a tailored embolization strategy with GKRS. The primary outcome was defined as bAVM obliteration. Secondary outcomes were neurological function and complications.

RESULTS

After a mean follow-up of 40.4 months, 5 patients were lost to follow-up. One hundred eighteen of the remaining 154 patients had favorable neurological outcomes with complete bAVM obliteration. A decrease in bAVM nidus size was observed in 36 patients. Five patients developed intracranial hemorrhage during the latency period, and 2 patients died. The Kaplan-Meier analysis showed that the obliteration rate increased each year and reached the peak point at approximately 3 years. The multivariate Cox regression analysis of factors affecting bAVM obliteration revealed that postembolization bAVM volume < 10 cm3 (p = 0.02), supratentorial location (p < 0.01), staged embolization prior to GKRS (p < 0.01), and mean Spetzler-Martin (SM) grade (p < 0.01) were independent factors associated with a high obliteration rate.

CONCLUSIONS

These data suggested that high-grade bAVMs treated using a tailored embolization strategy with GKRS were associated with a favorable clinical outcome and obliteration rate. Postembolization bAVM volume < 10 cm3, supratentorial location, staged embolization prior to GKRS, and low mean SM grade were associated with a high obliteration rate.