Fractal Analysis of the Susceptibility Weighted Imaging Patterns in Malignant Brain Tumors During Antiangiogenic Treatment: Technical Report on Four Cases Serially Imaged by 7 T Magnetic Resonance During a Period of Four Weeks

Clinical 7 Tesla magnetic resonance imaging: Impact and patient value in neurological disorders

Magnetic resonance imaging (MRI) is a cornerstone of non-invasive diagnostics and treatment monitoring, particularly for diseases of the central nervous system. Although 1.5- and 3 Tesla (T) field strengths remain the clinical standard, the advent of 7 T MRI represents a transformative step forward, offering superior spatial resolution, contrast, and sensitivity for visualizing neuroanatomy, metabolism, and function. Recent innovations, including parallel transmission and deep learning-based reconstruction, have resolved many prior technical challenges of 7 T MRI, enabling its routine clinical use. This review examines the diagnostic impact, patient value, and practical considerations of 7 T MRI, emphasizing its role in facilitating earlier diagnoses and improving care in conditions, such as amyotrophic lateral sclerosis (ALS), epilepsy, multiple sclerosis (MS), dementia, parkinsonism, tumors, and vascular diseases. Based on insights from over 1200 clinical scans with a second-generation 7 T system, the review highlights disease-specific biomarkers such as the motor band sign in ALS and the new diagnostic markers in MS, the central vein sign, and paramagnetic rim lesions. The unparalleled ability of 7 T MRI to study neurological diseases ex vivo at ultra-high resolution is also explored, offering new opportunities to understand pathophysiology and identify novel treatment targets. Additionally, the review provides a clinical perspective on patient handling and safety considerations, addressing challenges and practicalities associated with clinical 7 T MRI. By bridging research and clinical practice, 7 T MRI has the potential to redefine neuroimaging and advance the understanding and management of complex neurological disorders.

Computational Fractal-Based Analysis of MR Susceptibility-Weighted Imaging (SWI) in Neuro-Oncology and Neurotraumatology

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique able to depict the magnetic susceptibility produced by different substances, such as deoxyhemoglobin, calcium, and iron. The main application of SWI in clinical neuroimaging is detecting microbleedings and venous vasculature. Quantitative analyses of SWI have been developed over the last few years, aimed to offer new parameters, which could be used as neuroimaging biomarkers. Each technique has shown pros and cons, but no gold standard exists yet. The fractal dimension (FD) has been investigated as a novel potential objective parameter for monitoring intratumoral space-filling properties of SWI patterns. We showed that SWI patterns found in different tumors or different glioma grades can be represented by a gradient in the fractal dimension, thereby enabling each tumor to be assigned a specific SWI fingerprint. Such results were especially relevant in the differentiation of low-grade versus high-grade gliomas, as well as from high-grade gliomas versus lymphomas.Therefore, FD has been suggested as a potential image biomarker to analyze intrinsic neoplastic architecture in order to improve the differential diagnosis within clinical neuroimaging, determine appropriate therapy, and improve outcome in patients.These promising preliminary findings could be extended into the field of neurotraumatology, by means of the application of computational fractal-based analysis for the qualitative and quantitative imaging of microbleedings in traumatic brain injury patients. In consideration of some evidences showing that SWI signals are correlated with trauma clinical severity, FD might offer some objective prognostic biomarkers.In conclusion, fractal-based morphometrics of SWI could be further investigated to be used in a complementary way with other techniques, in order to form a holistic understanding of the temporal evolution of brain tumors and follow-up response to treatment, with several further applications in other fields, such as neurotraumatology and cerebrovascular neurosurgery as well.

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.

Anatomical variations and flow alterations of the uncal vein and its clinical implications in petroclival meningiomas

BACKGROUND

The Uncal vein (UV), downstream of the deep middle cerebral vein (DMCV), has a similar drainage pattern to the superficial middle cerebral vein (SMCV) and may be involved in venous complications during the anterior transpetrosal approach (ATPA). However, in petroclival meningioma (PCM), where the ATPA is frequently used, there are no reports evaluating drainage patterns of the UV and the risk of venous complications associated with the UV during the ATPA.

METHODS

Forty-three patients with petroclival meningioma (PCM) and 20 with unruptured intracranial aneurysm (control group) were included. Preoperative digital subtraction angiography was used to evaluate UV and DMCV drainage patterns on the side of the tumor and bilaterally in patients with PCM and the control group, respectively.

RESULTS

In the control group, the DMCV drained to the UV, UV and BVR, and BVR in 24 (60.0%), eight (20.0%), and eight (20.0%) hemispheres, respectively. Conversely, the DMCV in the patients with PCM drained to the UV, UV and BVR, and BVR in 12 (27.9%), 19 (44.2%), and 12 (27.9%) patients, respectively. The DMCV was more likely to be drained to the BVR in the PCM group (p < 0.01). In three patients with PCM (7.0%), the DMCV drained only to the UV, and furthermore, the UV drained to the pterygoid plexus via the foramen ovale, posing a risk for venous complications during the ATPA.

CONCLUSIONS

In the patients with PCM, the BVR functioned as a collateral venous pathway of the UV. Preoperative evaluation of the UV drainage patterns is recommended to reduce venous complications during the ATPA.

Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.

Morphological and Fractal Properties of Brain Tumors

Tumor interface dynamics is a complex process determined by cell proliferation and invasion to neighboring tissues. Parameters extracted from the tumor interface fluctuations allow for the characterization of the particular growth model, which could be relevant for an appropriate diagnosis and the correspondent therapeutic strategy. Previous work, based on scaling analysis of the tumor interface, demonstrated that gliomas strictly behave as it is proposed by the Family-Vicsek ansatz, which corresponds to a proliferative-invasive growth model, while for meningiomas and acoustic schwannomas, a proliferative growth model is more suitable. In the present work, other morphological and dynamical descriptors are used as a complementary view, such as surface regularity, one-dimensional fluctuations represented as ordered series and bi-dimensional fluctuations of the tumor interface. These fluctuations were analyzed by Detrended Fluctuation Analysis to determine generalized fractal dimensions. Results indicate that tumor interface fractal dimension, local roughness exponent and surface regularity are parameters that discriminate between gliomas and meningiomas/schwannomas.

Ultra-High-Field MRI in the Diagnosis and Management of Gliomas: A Systematic Review

Importance:

Gliomas, tumors of the central nervous system, are classically diagnosed through invasive surgical biopsy and subsequent histopathological study. Innovations in ultra-high field (UHF) imaging, namely 7-Tesla magnetic resonance imaging (7T MRI) are advancing preoperative tumor grading, visualization of intratumoral structures, and appreciation of small brain structures and lesions.

Objective

Summarize current innovative uses of UHF imaging techniques in glioma diagnostics and treatment.

Methods

A systematic review in accordance with PRISMA guidelines was performed utilizing PubMed. Case reports and series, observational clinical trials, and randomized clinical trials written in English were included. After removing unrelated studies and those with non-human subjects, only those related to 7T MRI were independently reviewed and summarized for data extraction. Some preclinical animal models are briefly described to demonstrate future usages of ultra-high-field imaging.

Results

We reviewed 46 studies (43 human and 3 animal models) which reported clinical usages of UHF MRI in the diagnosis and management of gliomas. Current literature generally supports greater resolution imaging from 7T compared to 1.5T or 3T MRI, improving visualization of cerebral microbleeds and white and gray matter, and providing more precise localization for radiotherapy targeting. Additionally, studies found that diffusion or susceptibility-weighted imaging techniques applied to 7T MRI, may be used to predict tumor grade, reveal intratumoral structures such as neovasculature and microstructures like axons, and indicate isocitrate dehydrogenase 1 mutation status in preoperative imaging. Similarly, newer imaging techniques such as magnetic resonance spectroscopy and chemical exchange saturation transfer imaging can be performed on 7T MRI to predict tumor grading and treatment efficacy. Geometrical distortion, a known challenge of 7T MRI, was at a tolerable level in all included studies.

Conclusion

UHF imaging has the potential to preoperatively and non-invasively grade gliomas, provide precise therapy target areas, and visualize lesions not seen on conventional MRI.

The fractal and textural analysis of glomeruli in obese and non-obese patients

Background

Fractal dimension is an indirect indicator of signal complexity. The aim was to evaluate the fractal and textural analysis parameters of glomeruli in obese and non-obese patients with glomerular diseases and association of these parameters with clinical features.

Methods

The study included 125 patients mean age 46 ± 15.2 years: obese (BMI ≥ 27 kg/m²—63 patients) and non-obese (BMI < 27 kg/m²—62 patients). Serum concentration of creatinine, protein, albumin, cholesterol, trygliceride, and daily proteinuria were measured. Formula Chronic Kidney Disease Epidemiology Colaboration (CKD-EPI) equation was calculated. Fractal (fractal dimension, lacunarity) and textural (angular second moment (ASM), textural correlation (COR), inverse difference moment (IDM), textural contrast (CON), variance) analysis parameters were compared between two groups.

Results

Obese patients had higher mean value of variance (t = 1.867), ASM (t = 1.532) and CON (t = 0.394) but without significant difference (P > 0.05) compared to non-obese. Mean value of COR (t = 0.108) and IDM (t = 0.185) were almost the same in two patient groups. Obese patients had higher value of lacunarity (t = 0.499) in comparison with non-obese, the mean value of fractal dimension (t = 0.225) was almost the same in two groups. Significantly positive association between variance and creatinine concentration (r = 0.499, P < 0.01), significantly negative association between variance and CKD-EPI (r = -0.448, P < 0.01), variance and sex (r = -0.339, P < 0.05) were found.

Conclusions

Variance showed significant correlation with serum creatinine concentration, CKD-EPI and sex. CON and IDM were significantly related to sex. Fractal and textural analysis parameters of glomeruli could become a supplement to histopathologic analysis of kidney tissue.

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Variance showed significant correlation with eGFR calculated by CKD- EPI formula.

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Significant correlation between variance and serum creatinine was found.

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Textural contrast and inverse difference moment were significantly related to sex.

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Fractal analysis of glomeruli could become supplement to histopathologic analysis.

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Textural analyses of kidney tissue should become useful to histopathologic analysis.

Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

Application of multimodal MRI and radiologic features for stereotactic brain biopsy: insights from a series of 208 patients

OBJECTIVES

We reviewed our institutional experience during a 10-year period for improvement of safety and efficacy of stereotactic biopsy procedures.

METHODS

We performed a retrospective review of inpatient summaries, stereotactic worksheets and radiologic investigations of 208 consecutive patients, who underwent MRI-guided stereotactic biopsies between March 2010 and March 2020.

RESULTS

The overall diagnostic yield was 96.2%. CT-confirmed intracranial hemorrhage occurred in 17 patients (8.2%), and the overall mortality rate was 0.5%. Combined MRS and PWI helped target selection in 27 cases (13.0%), the diagnostic yield was 100%. The results of the regression analysis revealed that non-diagnostic biopsy specimen significantly correlated with the cystic trait (p<.01) and edema of lesions (p<.05). Enhancement (p<.01) is shown to be an important factor for obtaining a diagnostic biopsy. Furthermore, the edema trait of lesions (p<.01) showed the important factors of hemorrhage.

CONCLUSIONS

The radiological features of lesions and use of the most suitable MRI sequences during biopsy planning are recommended ways to improve the diagnostic yield and safety of this technique.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma

BACKGROUND

Susceptibility weighted imaging (SWI) provides vascular information and plays an important role in improving the diagnostic accuracy of preoperative glioma grading. Intratumoral susceptibility signal intensities (ITSS) obtained from SWI has been used in glioma grading. However, the current method for estimation of ITSS is semiquantitative, manual count-dependent, and includes hemorrhage as well as vasculature.

PURPOSE

To develop a quantitative approach that calculates the vasculature volume within tumors by filtering out the hemorrhage from ITSS using R₂ * values and connected component analysis-based segmentation algorithm; to evaluate the accuracy of the proposed ITSS vasculature volume (IVV) for differentiating various grades of glioma; and compare it with reported semiquantitative ITSS approach.

STUDY TYPE

Retrospective.

SUBJECTS

Histopathologically confirmed 41 grade IV, 19 grade III, and 15 grade II glioma patients.Field Strength/Sequence: SWI (four echoes: 5.6, 11.8, 18, 24.2 msec) along with conventional MRI sequences (T₂ -weighted, T₁ -weighted, 3D-fluid-attenuated inversion recovery [FLAIR], and diffusion-weighted imaging [DWI]) at 3.0T.

ASSESSMENT

R₂ * relaxation maps were calculated from multiecho SWI. The R₂ * cutoff value for hemorrhage ITSS was determined. A segmentation algorithm was designed, based on this R₂ * hemorrhage combined with connected component shape analysis, to quantify the IVV from all slices containing tumor by filtering out hemorrhages. Semiquantitative ITSS scoring as well as total ITSS volume (TIV) including hemorrhages were also calculated.

STATISTICAL TESTS

One-way analysis of variance (ANOVA) and Tukey-Kramer post-hoc tests were performed to see the difference among the three grades of the tumor (II, III, and IV) in terms of semiquantitative ITSS scoring, TIV, and IVV. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the three methods individually in discriminating between grades of glioma.

RESULTS

One-way ANOVA showed that only the proposed IVV significantly differentiated different grades of gliomas having visible ITSS. ROC analysis showed that IVV provided the highest AUC for the discrimination of grade II vs. III (0.93), grade III vs. IV (0.98), and grade II vs. IV glioma (0.94). IVV also provided the highest sensitivity and specificity for differentiating grade II vs. III (87.44, 98.41), grade III vs. IV (97.15, 94.12), and grade II vs. IV (98.72, 92.31).

DATA CONCLUSION

The proposed quantitative method segregates hemorrhage from tumor vasculature. It scores above the existing semiquantitative method in terms of ITSS estimation and grading accuracy.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:225-233.

Susceptibility-Weighted Imaging of Deep Venous Congestion in Petroclival Meningioma

BACKGROUND

Protecting the venous drainage route during surgery in cases of petroclival meningioma (PCM) is important. Identifying venous congestion preoperatively can be valuable in reducing the risks associated with venous congestion during surgery. In this study, we examined the utility of susceptibility-weighted imaging (SWI) in identifying the presence of venous congestion in PCM cases preoperatively and identified the factors associated with it.

METHODS

We retrospectively examined 24 patients who had undergone surgery for primary PCM. The areas of the basal and internal cerebral veins on the affected and unaffected sides, obtained using SWI, were compared to identify venous congestion. We further examined the association between multiple candidate factors that are thought to be related to venous congestion and venous congestion using statistical analyses.

RESULTS

SWI could successfully identify venous congestion in 11 of 24 PCM cases. Among the 12 factors examined, those associated with venous congestion were an extension of the tumor, over the midline or upward, which is known to disturb the venous flow at the brainstem surface; anastomosis of the superficial cerebral vein (i.e., bypass route for venous congestion); and a high ABC Surgical Risk Scale score, an indicator of postoperative neurologic deterioration.

CONCLUSIONS

We showed that SWI is useful for evaluating venous congestion in PCM cases preoperatively and for identifying factors reflecting the risk of venous congestion. Taken together, our findings provide a multimodal strategy for the preoperative prediction of venous congestion, which could facilitate the treatment of PCM.

Preoperative assessment of haemostasis in patients undergoing stereotactic brain biopsy

Parenchymal hemorrhage is considered a major risk factor for perioperative morbidity in patients undergoing stereotactic brain biopsy. Studies on patients undergoing surgical procedures have suggested that evaluation of prothrombin time (PT) and activated partial thromboplastin time (aPTT) is of limited value with regard to prevention of haemorrhagic complications. However, this issue has not yet been addressed in patients undergoing stereotactic biopsy of intracranial lesions. We retrospectively analysed the medical records of 159 consecutive patients undergoing stereotactic biopsy of supratentorial intracranial lesions during a three-year period. Laboratory values (PT, aPTT, platelet count) were reviewed as well as clinical characteristics, modalities of surgical treatment, histopathological results and the postoperative course of patients. The overall diagnostic yield was 93.7%. Histopathological examination revealed glioma (WHO°I: 5, WHO°II: 25, WHO°III: 23, WHO°IV: 65), lymphoma (n = 14), inflammation (n = 8) and other entities (n = 6). Surgery-associated neurological deficits occurred in 7 patients (4.4%) and completely resolved in 6 of these patients. CT-confirmed intracranial hemorrhage occurred in 2 patients (1.3%) and in both cases, histopathological examination revealed glioblastoma. Results of hemostatic parameters (PT: 99 ± 13%, aPTT: 24 ± 3s, platelet count: 274 ± 87 10³/μL) were within normal range values in all patients and did not correlate with postsurgical morbidity. Standard assessment of haemostasis seems to be of limited value in patients with intracranial lesions undergoing stereotactic biopsy. Further studies regarding the intratumoural vasculature's impact on the risk of biopsy-related bleeding are necessary.

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.

Susceptibility-Weighted Imaging of Glioma: Update on Current Imaging Status and Future Directions

Susceptibility-weighted imaging (SWI) provides invaluable insight into glioma pathophysiology and internal tumoral architecture. The physical contribution of intratumoral susceptibility signal (ITSS) may correspond to intralesional hemorrhage, calcification, or tumoral neovascularity. In this review, we present emerging evidence of ITSS for assessment of intratumoral calcification, grading of glioma, and factors influencing the pattern of ITSS in glioblastoma. SWI phase imaging assists in identification of intratumoral calcification that aids in narrowing the differential diagnosis. Development of intratumoral calcification posttreatment of glioma serves as an imaging marker of positive therapy response. Grading of tumors with ITSS using information attributed to microhemorrhage and neovascularity in SWI correlates with MR perfusion parameters and histologic grading of glioma and enriches preoperative prognosis. Quantitative susceptibility mapping may provide a means to discriminate subtle calcifications and hemorrhage in tumor imaging. Recent data suggest ITSS patterns in glioblastoma vary depending on tumoral volume and sublocation and correlate with degree of intratumoral necrosis and neovascularity. Increasingly, there is a recognized role of obtaining contrast-enhanced SWI (CE-SWI) for assessment of tumoral margin in high-grade glioma. Significant higher concentration of gadolinium accumulates at the border of the tumoral invasion zone as seen on the SWI sequence; this results from contrast-induced phase shift that clearly delineates the tumor margin. Lastly, absence of ITSS may aid in differentiation between high-grade glioma and primary CNS lymphoma, which typically shows absence of ITSS. We conclude that SWI and CE-SWI are indispensable tools for diagnosis, preoperative grading, posttherapy surveillance, and assessment of glioma.

Application of Ultrahigh Field Magnetic Resonance Imaging in the Treatment of Brain Tumors: A Meta-Analysis

BACKGROUND

Magnetic resonance imaging (MRI) is the imaging modality of choice for the clinical management of brain tumors, and the majority of scanners operate with static magnetic field strengths of 1.5 or 3.0 Tesla (T). During the past decade, ultrahigh field (UHF) MRI has been investigated for its clinical applicability. This meta-analysis evaluates studies pertaining to the application of UHF MRI to patients with brain tumors.

METHODS

The authors performed a systematic review of the literature. Articles relating to application of UHF MRI to brain anatomy and brain tumors with living subjects were included. Studies were grouped into 1 of 3 categories based on area of focus: "Anatomical Structures Involved with Brain Tumors," "Tumor characterization," and "Treatment Monitoring." Comparison studies with extractable outcomes measure data were analyzed for performance of UHF MRI versus clinical field strengths (1.5 T and 3 T).

RESULTS

Twenty-four studies (361 subjects) met inclusion criteria. The field of study was heterogeneous and rigorous statistical analysis was not possible. Overall, 279 patients with brain tumors scanned at UHF MRI have been reported. Of these, glioma and glioblastoma multiforme are the most commonly studied lesions (38.9% and 24.4%, respectively). In comparison studies between UHF MRI and clinical field strengths, 24 of 51 patients had outcome measures that were better with UHF MRI, 17 of 24 were equivalent at both field strengths, and 9 were worse at UHF MRI. The most common causes of a worse performance were susceptibility artifacts and magnetic field inhomogeneities (3 of 9). Imaging of the pituitary gland, pineal gland veins, cranial nerves, and tumor microvasculature were all shown to be feasible.

CONCLUSIONS

UHF MRI shows promise to improve detection and characterization of brain tumors, preoperative planning for neurosurgical resection, and longitudinal monitoring of the effects of radiation and antibody-based therapies. Technical innovations are needed to overcome field inhomogeneity and susceptibility artifacts in certain regions of the skull. Finally, larger studies comparing 1.5 T, 3.0 T, and 7.0 T or greater will determine whether UHF MRI gains acceptance as a clinical standard.

Magnetic resonance susceptibility weighted imaging in neurosurgery: current applications and future perspectives

Susceptibility weighted imaging (SWI) is a relatively new imaging technique. Its high sensitivity to hemorrhagic components and ability to depict microvasculature by means of susceptibility effects within the veins allow for the accurate detection, grading, and monitoring of brain tumors. This imaging modality can also detect changes in blood flow to monitor stroke recovery and reveal specific subtypes of vascular malformations. In addition, small punctate lesions can be demonstrated with SWI, suggesting diffuse axonal injury, and the location of these lesions can help predict neurological outcome in patients. This imaging technique is also beneficial for applications in functional neurosurgery given its ability to clearly depict and differentiate deep midbrain nuclei and close submillimeter veins, both of which are necessary for presurgical planning of deep brain stimulation. By exploiting the magnetic susceptibilities of substances within the body, such as deoxyhemoglobin, calcium, and iron, SWI can clearly visualize the vasculature and hemorrhagic components even without the use of contrast agents. The high sensitivity of SWI relative to other imaging techniques in showing tumor vasculature and microhemorrhages suggests that it is an effective imaging modality that provides additional information not shown using conventional MRI. Despite SWI's clinical advantages, its implementation in MRI protocols is still far from consistent in clinical usage. To develop a deeper appreciation for SWI, the authors here review the clinical applications in 4 major fields of neurosurgery: neurooncology, vascular neurosurgery, neurotraumatology, and functional neurosurgery. Finally, they address the limitations of and future perspectives on SWI in neurosurgery.

Fractals in the neurosciences, Part II: clinical applications and future perspectives

It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.

Multimodal imaging of gliomas in the context of evolving cellular and molecular therapies

The vast majority of malignant gliomas relapse after surgery and standard radio-chemotherapy. Novel molecular and cellular therapies are thus being developed, targeting specific aspects of tumor growth. While histopathology remains the gold standard for tumor classification, neuroimaging has over the years taken a central role in the diagnosis and treatment follow up of brain tumors. It is used to detect and localize lesions, define the target area for biopsies, plan surgical and radiation interventions and assess tumor progression and treatment outcome. In recent years the application of novel drugs including anti-angiogenic agents that affect the tumor vasculature, has drastically modulated the outcome of brain tumor imaging. To properly evaluate the effects of emerging experimental therapies and successfully support treatment decisions, neuroimaging will have to evolve. Multi-modal imaging systems with existing and new contrast agents, molecular tracers, technological advances and advanced data analysis can all contribute to the establishment of disease relevant biomarkers that will improve disease management and patient care. In this review, we address the challenges of glioma imaging in the context of novel molecular and cellular therapies, and take a prospective look at emerging experimental and pre-clinical imaging techniques that bear the promise of meeting these challenges.

Computational Fractal-Based Analysis of Brain Arteriovenous Malformation Angioarchitecture

BACKGROUND:

Neuroimaging is the gold standard for diagnosis and follow-up of brain arteriovenous malformations (bAVMs), but no objective parameter has been validated for the assessment of the nidus angioarchitecture and for prognostication following treatment. The fractal dimension (FD), which is a mathematical parameter able to quantify the space-filling properties and roughness of natural objects, may be useful in quantifying the geometrical complexity of bAVMs nidus.

OBJECTIVE:

To propose FD as a neuroimaging biomarker of the nidus angioarchitecture, which might be related to radiosurgical outcome.

METHODS:

We retrospectively analyzed 54 patients who had undergone stereotactic radiosurgery for the treatment of bAVMs. The quantification of the geometric complexity of the vessels forming the nidus, imaged in magnetic resonance imaging, was assessed by means of the box-counting method to obtain the fractal dimension.

RESULTS:

FD was found to be significantly associated with the size (P = .03) and volume (P &lt; .001) of the nidus, in addition to several angioarchitectural parameters. A nonsignificant association between clinical outcome and FD was observed (area under the curve, 0.637 [95% confidence interval, 0.49-0.79]), indicative of a potential inverse relationship between FD and bAVM obliteration.

CONCLUSION:

In our exploratory methodological research, we showed that the FD is an objective computer-aided parameter for quantifying the geometrical complexity and roughness of the bAVM nidus. The results suggest that more complex bAVM angioarchitecture, having higher FD values, might be related to decreased response to radiosurgery and that the FD of the bAVM nidus could be used as a morphometric neuroimaging biomarker.

Microvascular morphometrics of the hypophysis and pituitary tumors: from bench to operating theatre

The idea that microvasculature might be a histopathological biomarker in the prognosis and treatment of tumors is garnering even more attention in the scientific community. The roles of neovascularity in tumor progression and metastasis, have become a hot-topic of investigation in cancer research. A number of methods of quantitatively analyzing pituitary adenoma microvasculature have been applied, and fractal analysis is emerging as a potential effective model for this aim. Additionally, new and more specific immunological techniques have been developed for the detection of microvessels. CD105 (Endoglin) has been proposed as a valuable antigen that marks only newly formed vessels, rather than the entire tumor microvascular system. The combination of different types of immunostaining techniques for the detection of microvessels in pituitary adenomas with fractal analysis as an objective and computer-aided technique to quantify and describe morphological aspects of microvessels has potential implications in future clinical and surgical applications. Tumor treatments, such as anti-angiogenic therapy, as well as intraoperative tools, stand to be enhanced by increasing advances in microvascular research. We here review the methods used for the quantitative analysis of microvessels of the pituitary in its physiopathological states, with the aim to show the pituitary adenoma as a model for the study of neoplastic angioarchitecture and the importance of the introduction of new techniques for the study of angiogenesis, with the relative scientific, medical and surgical implications.

Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review

OBJECTIVES

To provide an overview of recent research in fractal analysis of tissue perfusion imaging, using standard radiological and nuclear medicine imaging techniques including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and to discuss implications for different fields of application.

METHODS

A systematic review of fractal analysis for tissue perfusion imaging was performed by searching the databases MEDLINE (via PubMed), EMBASE (via Ovid) and ISI Web of Science.

RESULTS

Thirty-seven eligible studies were identified. Fractal analysis was performed on perfusion imaging of tumours, lung, myocardium, kidney, skeletal muscle and cerebral diseases. Clinically, different aspects of tumour perfusion and cerebral diseases were successfully evaluated including detection and classification. In physiological settings, it was shown that perfusion under different conditions and in various organs can be properly described using fractal analysis.

CONCLUSIONS

Fractal analysis is a suitable method for quantifying heterogeneity from radiological and nuclear medicine perfusion images under a variety of conditions and in different organs. Further research is required to exploit physiologically proven fractal behaviour in the clinical setting.

KEY POINTS

• Fractal analysis of perfusion images can be successfully performed. • Tumour, pulmonary, myocardial, renal, skeletal muscle and cerebral perfusion have already been examined. • Clinical applications of fractal analysis include tumour and brain perfusion assessment. • Fractal analysis is a suitable method for quantifying perfusion heterogeneity. • Fractal analysis requires further research concerning the development of clinical applications.

Clinical applications of susceptibility-weighted imaging in detecting and grading intracranial gliomas: a review

Susceptibility-weighted imaging (SWI) is a technique that exploits the susceptibility difference between tissues to provide contrast for different regions of the brain. In essence, it uses the deoxygenated hemoglobin of veins, hemosiderin of hemorrhage, etc. as intrinsic contrast agents, allowing for much better visualization of blood and microvessels even without administration of an external contrast agent. It is a fast-evolving field that is being constantly improved and increasingly implemented with updates in relevant technology. Multiple studies have been done on the role of SWI in the management of various neurologic disorders and it is also being seen as a further step in the neuroradiologist’s goal of being able to noninvasively grade tumors in order to influence therapy. This article briefly reviews the evolution of SWI since its conception and provides the reader with a comprehensive summary of various studies that have been done on its application for detecting and grading intraaxial brain tumors, specifically gliomas. Other useful magnetic resonance techniques that have shown promise in grading gliomas are also discussed.

Three-dimensional susceptibility-weighted imaging at 7 T using fractal-based quantitative analysis to grade gliomas

INTRODUCTION

Susceptibility-weighted imaging (SWI) with high- and ultra-high-field magnetic resonance is a very helpful tool for evaluating brain gliomas and intratumoral structures, including microvasculature. Here, we test whether objective quantification of intratumoral SWI patterns by applying fractal analysis can offer reliable indexes capable of differentiating glial tumor grades.

METHODS

Thirty-six patients affected by brain gliomas (grades II-IV, according to the WHO classification system) underwent MRI at 7 T using a SWI protocol. All images were collected and analyzed by applying a computer-aided fractal image analysis, which applies the fractal dimension as a measure of geometrical complexity of intratumoral SWI patterns. The results were subsequently statistically correlated to the histopathological tumor grade.

RESULTS

The mean value of the fractal dimension of the intratumoral SWI patterns was 2.086 ± 0.413. We found a trend of higher fractal dimension values in groups of higher histologic grade. The values ranged from a mean value of 1.682 ± 0.278 for grade II gliomas to 2.247 ± 0.358 for grade IV gliomas (p = 0.013); there was an overall statistically significant difference between histopathological groups.

CONCLUSION

The present study confirms that SWI at 7 T is a useful method for detecting intratumoral vascular architecture of brain gliomas and that SWI pattern quantification by means of fractal dimension offers a potential objective morphometric image biomarker of tumor grade.