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.

Fractal Dimension Analysis in Neurological Disorders: An Overview

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

Fractals, Pattern Recognition, Memetics, and AI: A Personal Journal in the Computational Neurosurgery

In this chapter, the personal journey of the author in many countries, including Italy, Germany, Austria, the United Kingdom, Switzerland, the United States, Canada, and Australia, is summarized, aimed to merge different translational fields (such as neurosurgery and the clinical neurosciences in general, biomedical engineering, mathematics, computer science, and cognitive sciences) and lay the foundations of a new field defined computational neurosurgery, with fractals, pattern recognition, memetics, and artificial intelligence as the common key words of the journey.

Computational Fractal-Based Neurosurgery

Fractal geometry is a branch of mathematics used to characterize and quantify the geometrical complexity of natural objects, with many applications in different fields, including physics, astronomy, geology, meteorology, finances, social sciences, and computer graphics. In the biomedical sciences, the use of fractal parameters has allowed the introduction of novel morphometric parameters, which have been shown to be useful to characterize any biomedical images as well as any time series within different domains of applications. Specifically, in the neurosciences and neurosurgery, the use of the fractal dimension and other computationally inferred fractal parameters has offered robust morphometric quantitators to characterize the brain in its wholeness, from neurons to the cortical structure and connections, and introduced new prognostic, diagnostic, and eventually therapeutic markers of many diseases of neurosurgical interest, including brain tumors and cerebrovascular malformations, as summarized in this chapter.

Fractal-Based Analysis of Arteriovenous Malformations (AVMs)

Arteriovenous malformations (AVMs) are cerebrovascular lesions consisting of a pathologic tangle of the vessels characterized by a core termed the nidus, which is the "nest" where the fistulous connections occur. AVMs can cause headache, stroke, and/or seizures. Their treatment can be challenging requiring surgery, endovascular embolization, and/or radiosurgery as well. AVMs' morphology varies greatly among patients, and there is still a lack of standardization of angioarchitectural parameters, which can be used as morphometric parameters as well as potential clinical biomarkers (e.g., related to prognosis).In search of new diagnostic and prognostic neuroimaging biomarkers of AVMs, computational fractal-based models have been proposed for describing and quantifying the angioarchitecture of the nidus. In fact, the fractal dimension (FD) can be used to quantify AVMs' branching pattern. Higher FD values are related to AVMs characterized by an increased number and tortuosity of the intranidal vessels or to an increasing angioarchitectural complexity as a whole. Moreover, FD has been investigated in relation to the outcome after Gamma Knife radiosurgery, and an inverse relationship between FD and AVM obliteration was found.Taken altogether, FD is able to quantify in a single and objective value what neuroradiologists describe in qualitative and/or semiquantitative way, thus confirming FD as a reliable morphometric neuroimaging biomarker of AVMs and as a potential surrogate imaging biomarker. Moreover, computational fractal-based techniques are under investigation for the automatic segmentation and extraction of the edges of the nidus in neuroimaging, which can be relevant for surgery and/or radiosurgery planning.

Longitudinal Effects of Combination Antiretroviral Therapy on Cognition and Neuroimaging Biomarkers in Treatment-Naïve People with HIV

BACKGROUND AND OBJECTIVES

While combination antiretroviral therapy (cART) has dramatically increased the life expectancy of people with HIV (PWH), nearly 50% develop HIV-associated neurocognitive disorders (HAND)¹. This may be due to previously uncontrolled HIV viral replication, immune activation maintained by residual viral replication² or activation from other sources^{3, 4}, or cART-associated neurotoxicity⁵. The aim of this study was to determine the effect of cART on cognition and neuroimaging biomarkers markers in people with HIV (PWH) before and after initiation of cART compared to HIV negative controls (HC) and HIV elite controllers (EC) who remain untreated.

METHODS

We recruited three groups of participants from the University of Rochester, McGovern Medical School and SUNY Upstate Medical University: 1) ART-treatment-naïve PWH; 2) age-matched HC; and 3) EC. Participants underwent brain MRI and clinical and neuropsychological assessments at baseline, one year, and two years. PWH were also assessed 12 weeks after initiating cART. Volumetric analysis and fractal dimensionality (FD) were calculated for cortical and subcortical regions. Mixed effect regressions examined the effect of group and imaging variables on cognition.

RESULTS

We enrolled 47 PWH, 58 HC, and 10 EC. At baseline, PWH had worse cognition and lower cortical volumes than HC. Cognition improved following initiation of cART and remained stable over time. Greater cortical thickness was associated with better cognition at baseline; greater FD of parietal, temporal and occipital lobes was associated with better cognition at baseline and longitudinally. At baseline, EC had worse cognition, lower cortical thickness and lower FD in all four lobes and caudate than PWH and HC. Greater cortical thickness, hippocampal volumes and FD of frontal, temporal and occipital lobes were associated with better cognition longitudinally.

CONCLUSIONS

Initiation of cART in PWH is associated with improvement in brain structure and cognition. However, significant differences persist over time compared to HC. Similar trends in EC suggest that results are due to HIV infection rather than treatment. Stronger associations between cognition and FD suggest this imaging metric may be a more sensitive marker of neuronal injury than cortical thickness and volumetric measures.

Molecular Biomarkers and Drug Targets in Brain Arteriovenous and Cavernous Malformations: Where Are We?

Vascular malformations of the brain (VMB) comprise abnormal development of blood vessels. A small fraction of VMBs causes hemorrhages with neurological morbidity and risk of mortality in patients. Most often, they are symptomatically silent and are detected at advanced stages of disease progression. The most common forms of VMBs are arteriovenous and cavernous malformations in the brain. Radiopathological features of these diseases are complex with high phenotypic variability. Early detection of these malformations followed by preclusion of severe neurological deficits such as hemorrhage and stroke is crucial in the clinical management of patients with VMBs. The technological advances in high-throughput omics platforms have currently infused a zest in translational research in VMBs. Besides finding novel biomarkers and therapeutic targets, these studies have withal contributed significantly to the understanding of the etiopathogenesis of VMBs. Here we discuss the recent advances in predictive and prognostic biomarker research in sporadic and familial arteriovenous malformations as well as cerebral cavernous malformations. Furthermore, we analyze the clinical applicability of protein and noncoding RNA-based molecular-targeted therapies which may have a potentially key role in disease management.

Brain volumetric and fractal analysis of synthetic MRI: A comparative study with conventional 3D T1-weighted images

PURPOSE

The estimation of brain volumetric measurements based on Synthetic MRI (SyMRI) is easy and fast, however, the consistency of brain volumetric and morphologic measurements based on SyMRI and 3D T1WI should be further addressed. The current study evaluated the impact of spatial resolution on brain volumetric and morphologic measurements using SyMRI, and test whether the brain measurements derived from SyMRI were consistent with those resulted from 3D T1WI.

METHOD

Brain volumetric and fractal analysis were applied to thirty healthy subjects, each underwent four SyMRI acquisitions with different spatial resolutions (1 × 1 × 2 mm, 1 × 1x3mm, 1 × 1 × 4 mm, 2 × 2 × 2 mm) and a 3D T1WI (1 × 1 × 1 mm isotropic). The consistency of the SyMRI measurements was tested using one-way non-parametric Kruskal-Wallis test and post hoc Dwass-Steel-Critchlow-Fligner test. The association between SyMRI and 3D T1WI derived measurements was evaluated using linear regression models.

RESULTS

Our results demonstrated that both in- and through-plane resolutions show an impact on brain volumetric measurements, while brain parenchymal volume showed high consistency across the SyMRI acquisitions, and high association with the measurements from 3D T1WI. In addition, SyMRI with 1 × 1 × 4 mm resolution showed the strongest association with 3D T1WI compared to other SyMRI acquisitions in both volumetric and fractal analyses. Moreover, substantial differences were found in fractal dimension of both gray and white matter between the SyMRI and 3D T1WI tissue segmentations.

CONCLUSIONS

Our results suggested that the measurements from SyMRI with relatively higher in-plane and lower through-plane resolution (1 × 1 × 4 mm) are much closer to 3D T1WI.

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open-access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated-acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2-weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced (a) repeated-measurement deviation clusters around the registration target, (b) strong bidirectional correlations among image analysis groups, and (c) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics.

Diagnostic Value of Fractal Analysis for the Differentiation of Brain Tumors Using 3-Tesla Magnetic Resonance Susceptibility-Weighted Imaging

BACKGROUND

Susceptibility-weighted imaging (SWI) of brain tumors provides information about neoplastic vasculature and intratumoral micro- and macrobleedings. Low- and high-grade gliomas can be distinguished by SWI due to their different vascular characteristics. Fractal analysis allows for quantification of these radiological differences by a computer-based morphological assessment of SWI patterns.

OBJECTIVE

To show the feasibility of SWI analysis on 3-T magnetic resonance imaging to distinguish different kinds of brain tumors.

METHODS

Seventy-eight patients affected by brain tumors of different histopathology (low- and high-grade gliomas, metastases, meningiomas, lymphomas) were included. All patients underwent preoperative 3-T magnetic resonance imaging including SWI, on which the lesions were contoured. The images underwent automated computation, extracting 2 quantitative parameters: the volume fraction of SWI signals within the tumors (signal ratio) and the morphological self-similar features (fractal dimension [FD]). The results were then correlated with each histopathological type of tumor.

RESULTS

Signal ratio and FD were able to differentiate low-grade gliomas from grade III and IV gliomas, metastases, and meningiomas (P < .05). FD was statistically different between lymphomas and high-grade gliomas (P < .05). A receiver-operating characteristic analysis showed that the optimal cutoff value for differentiating low- from high-grade gliomas was 1.75 for FD (sensitivity, 81%; specificity, 89%) and 0.03 for signal ratio (sensitivity, 80%; specificity, 86%).

CONCLUSION

FD of SWI on 3-T magnetic resonance imaging is a novel image biomarker for glioma grading and brain tumor characterization. Computational models offer promising results that may improve diagnosis and open perspectives in the radiological assessment of brain tumors.

ABBREVIATIONS

FD, fractal dimensionSR, signal ratioSWI, susceptibility-weighted imaging.

Geometrical Complexity of Cortical Microvascularization in Moyamoya Disease

BACKGROUND

Dilatation of the microvascular diameter is recognized in moyamoya disease and referred to as microvascularization. The purpose of this study was to characterize the cortical microvascularization in moyamoya disease using imaging analysis, and to explore the developmental mechanism of the collateral network around the cortical surface.

METHODS

A total of 20 hemispheric sides of 14 patients with moyamoya disease were included in this study. From the intraoperative images, cortical surface images were extracted, and binary images were subsequently created. Then the ratio of the microvessels of the brain surface (vascular fraction; VF) and the box-counting fractal dimension (Db) values were calculated. The VF and Db values in the moyamoya disease group were then compared with those in atherosclerotic disease and nonischemic disease groups, and assessed in terms of clinical and radiologic factors.

RESULTS

VF was significantly higher in the moyamoya disease group compared with the atherosclerotic disease group, and Db was significantly higher in the moyamoya disease group compared with the atherosclerotic disease and nonischemic disease groups. In the moyamoya disease group, VF showed a moderate correlation with magnetic resonance angiography (MRA) score. Moreover, Db was significantly higher in the pediatric patients, in the presence of ischemic symptoms, and in the presence of ivy sign, and Db showed a moderate correlation with MRA score and cerebral blood flow in moyamoya disease.

CONCLUSIONS

In the patients with moyamoya disease, the cortical microvascularization exhibited increased Db and dilatation of the pial arteries. In moyamoya disease, cortical microvascularization is associated with clinical and radiologic factors. This microvascularization might be a compensatory mechanism in the ischemic condition in moyamoya disease.

Numerical modeling of vessel geometry to measure hemodynamics parameters non-invasively in cerebral arteriovenous malformation

Cerebral arteriovenous malformations (CAVM) are congenital lesions that contain a cluster of multiple arteriovenous shunts (NIDUS). Cardiac arrhythmia in CAVM patients causes irregular changes in blood flow and pressure in the NIDUS area. This paper proposes the framework for creating the lumped model of tortuous vessel structure near NIDUS based on radiological images. This lumped model is to analyze flow variations, with various combinations of the transient electrical signals, which simulate similar conditions of cardiac arrhythmia in CAVM patients. This results in flow variation at different nodes of the lumped model. Here we present two AVM patients with evaluation of 150 vessels locations as node points, with an accuracy of 93%, the sensitivity of 95%, and specificity of 94%. The calculated p-value is smaller than the significance level of 0.05.