Tag: Neuroimaging

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Predictive models for assessing the risk of brain aneurysm rupture

J Neurosurg 143:607–614, 2025

This study evaluated predictive models for brain aneurysm rupture risk using clinical, morphological, and advanced MRI radiomics data. Models combining these factors, especially radiomics, significantly improved accuracy in identifying symptomatic aneurysms, supporting a comprehensive, personalized approach for aneurysm risk stratification and treatment decisions.

• The study evaluated predictive models for assessing the risk of brain aneurysm rupture using high-resolution MRI, clinical, morphological, and radiomic data.

• 129 intracranial aneurysms were analyzed; 26% were symptomatic (ruptured or with warning symptoms).

• The PHASES score alone had low predictive ability (AUC 0.61–0.62) for symptomatic aneurysms.

• Adding morphological metrics (especially size ratio) and smoking status improved prediction (AUC up to 0.79).

• Incorporating aneurysm wall enhancement (AWE) metrics further increased model performance (AUC 0.82).

• The best model included age and radiomics features, achieving the highest predictive accuracy (AUC 0.87, 88% sensitivity).

• Younger age, current smoking, larger size ratio, and higher wall enhancement were associated with symptomatic aneurysms.

• A comprehensive approach using clinical, morphological, and advanced imaging/radiomics data improves aneurysm risk stratification.

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Comprehensive Morphometric Analysis to Identify Key Neuroimaging Biomarkers for the Diagnosis of Adult Hydrocephalus Using Artificial Intelligence

Neurosurgery 96:1386–1396, 2025

This study used AI and SHAP analysis to identify five key, easily measurable 1-D neuroimaging biomarkers—FTHR, MEI, MCMI, SMLH, and CPCA—for accurately diagnosing adult non-normal pressure hydrocephalus, offering a practical, standardized, and interpretable approach to improve early detection and clinical decision-making.

• Hydrocephalus diagnosis is often inconsistent due to reliance on clinical and qualitative radiological assessments.

• This study used artificial intelligence (AI) and SHAP analysis to identify key, easily measurable neuroimaging biomarkers for adult non-normal pressure hydrocephalus (non-NPH).

• A comprehensive set of 21 morphometric features was analyzed from MRI images of 42 adult non-NPH patients and 40 healthy controls.

• Gradient Boosting was the best-performing AI classifier, achieving 0.94 accuracy and 0.97 AUC.

• Ventricular volume is the most important biomarker, but measurement is complex for clinicians.

• Five key 1-D biomarkers identified are: frontal-temporal horn ratio (FTHR), modified Evans index (MEI), modified cella media index (MCMI), sagittal maximum lateral ventricle height (SMLH), and coronal posterior callosal angle (CPCA).

• These five markers are easily measurable and provide high diagnostic accuracy, supporting practical clinical use.

• The approach addresses multicollinearity and improves diagnostic standardization, but further validation with larger datasets is needed.

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Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning

Neurosurgery 96:1181–1192, 2025

This review highlights how advanced neuroimaging and machine learning, especially radiomics and deep learning models, are transforming the noninvasive diagnosis, molecular characterization, and prognosis prediction in IDH-wildtype glioblastoma, offering improved patient stratification and personalized treatment strategies while emphasizing the need for further clinical integration.

• Neuroimaging and machine learning have greatly improved diagnosis, classification, and prognosis of IDH-wildtype glioblastoma, a highly heterogeneous and aggressive brain tumor.

• Advanced MRI techniques, including diffusion tensor imaging (DTI) and radiomics, provide noninvasive insights into tumor infiltration, metabolic profiles, and microstructural changes.

• Machine learning algorithms, especially CNNs, enhance glioblastoma characterization, enabling accurate prediction of genetic mutations, IDH status, tumor subtypes, and survival outcomes.

• Radiomics extracts quantitative features from neuroimages, serving as potential biomarkers for tumor classification, prognosis, and guiding treatment strategies.

• Integration of radiomics and machine learning helps differentiate pseudoprogression from true tumor progression and predicts patterns of tumor invasion and recurrence.

• Imaging biomarkers and machine learning models are promising but remain complementary to molecular diagnostics and are not yet standard in clinical practice.

• Ongoing research aims to refine models, integrate emerging imaging techniques, and better link imaging features to underlying molecular processes for personalized therapy.

• The synergy of neuroimaging and AI is expected to enable noninvasive, precision management and better outcomes for glioblastoma patients.

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Blood Biomarkers and Structural Imaging Correlations Post-Traumatic Brain Injury: A Systematic Review

Neurosurgery 90:170–179, 2022

Blood biomarkers are of increasing importance in the diagnosis and assessment of traumatic brain injury (TBI). However, the relationship between them and lesions seen on imaging remains unclear.

OBJECTIVE: To perform a systematic review of the relationship between blood biomarkers and intracranial lesion types, intracranial lesion injury patterns, volume/number of intracranial lesions, and imaging classification systems.

METHODS: We searched Medical Literature Analysis and Retrieval System Online, Excerpta Medica dataBASE, and Cumulative Index to Nursing and Allied Health Literature from inception to May 2021, and the references of included studies were also screened. Heterogeneity in study design, biomarker types, imaging modalities, and analyses inhibited quantitative analysis, with a qualitative synthesis presented.

RESULTS: Fifty-nine papers were included assessing one or more biomarker to imaging comparisons per paper: 30 assessed imaging classifications or injury patterns, 28 assessed lesion type, and 11 assessed lesion volume or number. Biomarker concentrations were associated with the burden of brain injury, as assessed by increasing intracranial lesion volume, increasing numbers of traumatic intracranial lesions, and positive correlations with imaging classification scores. There were inconsistent findings associating different biomarkers with specific imaging phenotypes including diffuse axonal injury, cerebral edema, and intracranial hemorrhage.

CONCLUSION: Blood-based biomarker concentrations after TBI are consistently demonstrated to correlate burden of intracranial disease. The relation with specific injury types is unclear suggesting a lack of diagnostic specificity and/or is the result of the complex and heterogeneous nature of TBI.

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Neuroimaging of Intracerebral Hemorrhage

Neurosurgery 86:E414–E423, 2020

Intracerebral hemorrhage (ICH) accounts for 10% to 20% of strokes worldwide and is associated with high morbidity and mortality rates. Neuroimaging is indispensable for rapid diagnosis of ICH and identification of the underlying etiology, thus facilitating triage and appropriate treatment of patients.

The most common neuroimaging modalities include noncontrast computed tomography (CT), CT angiography (CTA), digital subtraction angiography, andmagnetic resonance imaging (MRI). The strengths and disadvantages of each modality will be reviewed.

Novel technologies such as dual-energy CT/CTA, rapid MRI techniques, near-infrared spectroscopy, and automated ICH detection hold promise for faster pre- and in-hospital ICH diagnosis that may impact patient management.

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Role of the petrous ridge and angulation of the trigeminal nerve in the pathogenesis of trigeminal neuralgia, with implications for microvascular decompression

Acta Neurochirurgica (2018) 160:971–976

Vascular compression is the main pathogenetic factor in apparently primary trigeminal neuralgia; however some patients may present with clinically classical neuralgia but no vascular conflict on MRI or even at surgery. Several factors have been cited as alternative or supplementary factors that may cause neuralgia. This work focuses on the shape of the petrous ridge at the point of exit from the cavum trigeminus as well as the angulation of the nerve at this point.

Methods Patients with trigeminal neuralgia that had performed a complete imagery workup according to our protocol and had microvascular decompression were included as well as ten controls. In all subjects, the angle of the petrous ridge as well as the angle of the nerve on passing over the ridge were measured. These were compared from between the neuralgic and the nonneuralgic side and with the measures performed in controls.

Results In 42 patients, the bony angle of the petrous ridge was measured to be 86° on the neuralgic side, significantly more acute than that of controls (98°, p = 0.004) and with a trend to be more acute than the non-neuralgic side (90°, p = 0.06). The angle of the nerve on the side of the neuralgia was measured to be on average 141°, not significantly different either from the other side (144°, p=0.2) or from controls (142°, p = 0.4). However, when taking into account the grade of the conflict, the angle was significantly more acute in patients with grade II/III conflict than on the contralateral side, especially when the superior cerebellar artery was the conflicting vessel.

Conclusion This pilot study analyzes factors other than NVC that may contribute to the pathogenesis of the neuralgia. It appears that aggressive bony edges may contribute—at least indirectly—to the neuralgia. This should be considered for surgical indication and conduct of surgery when patients undergo MVD.

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Neurosurgery for schizophrenia

Neurosurgery for schizophrenia

J Neurosurg 124:917–928, 2016

The main objectives of this review were to provide an update on the progress made in understanding specific circuit abnormalities leading to psychotic symptoms in schizophrenia and to propose rational targets for therapeutic deep brain stimulation (DBS).

Refractory schizophrenia remains a major unsolved clinical problem, with 10%–30% of patients not responding to standard treatment options. Progress made over the last decade was analyzed through reviewing structural and functional neuroimaging studies in humans, along with studies of animal models of schizophrenia. The authors reviewed theories implicating dysfunction in dopaminergic and glutamatergic signaling in the pathophysiology of the disorder, paying particular attention to neurosurgically relevant nodes in the circuit. In this context, the authors focused on an important pathological circuit involving the associative striatum, anterior hippocampus, and ventral striatum, and discuss the possibility of targeting these nodes for therapeutic neuromodulation with DBS. Finally, the authors examined ethical considerations in the treatment of these vulnerable patients.

The functional anatomy of neural circuits relevant to schizophrenia remains of great interest to neurosurgeons and psychiatrists and lends itself to the development of specific targets for neuromodulation. Ongoing progress in the understanding of these structures will be critical to the development of potential neurosurgical treatments of schizophrenia.

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Strategies for Computed Tomography Radiation Dose Reduction in Pediatric Neuroimaging

Strategies for Computed Tomography Radiation Dose Reduction in Pediatric Neuroimaging

Neurosurgery 77:228–232, 2015

Radiation exposure from diagnostic imaging is a significant concern, particularly in the care of pediatric patients. Computed tomography (CT) scanning is a significant source of radiation.

OBJECTIVE: To demonstrate that diagnostic quality CT images can be obtained while minimizing the effective radiation dose to the patient.

METHODS: In this retrospective cross-sectional study, noncontrast head CT scan data were reviewed, and indications for scans and estimated radiation dose delivered were recorded. The estimated effective radiation dose (EERD) for each CT protocol was reviewed.

RESULTS: We identified 251 head CT scans in a single month. Of these, 96 scans were using a low-dose shunt protocol with a mean EERD of 0.82 mSv. The remaining 155 scans were performed using the standard protocol, and the mean EERD was 1.65 mSv. Overall, the EERD was minimized while maintaining diagnostic scan quality.

CONCLUSION: Although replacing a CT with magnetic resonance imaging is ideal to completely avoid ionizing radiation, this is not always practical or preferred. Therefore, it is important to have CT protocols in place that minimize radiation dose without sacrificing diagnostic quality. The protocols in place at our institution could be replicated at other academic and community hospitals and imaging centers.

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Computational Fractal-Based Analysis of Brain Arteriovenous Malformation Angioarchitecture

Computational Fractal-Based Analysis of Brain Arteriovenous Malformation Angioarchitecture

Neurosurgery 75:72–79, 2014

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 , .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.

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