Transient deoxyhemoglobin formation as a contrast for perfusion MRI studies in patients with brain tumors: a feasibility study.

Background: Transient hypoxia-induced deoxyhemoglobin (dOHb) has recently been shown to represent a comparable contrast to gadolinium-based contrast agents for generating resting perfusion measures in healthy subjects. Here, we investigate the feasibility of translating this non-invasive approach to patients with brain tumors. Methods: A computer-controlled gas blender was used to induce transient precise isocapnic lung hypoxia and thereby transient arterial dOHb during echo-planar-imaging acquisition in a cohort of patients with different types of brain tumors (n = 9). We calculated relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit time (MTT) using a standard model-based analysis. The transient hypoxia induced-dOHb MRI perfusion maps were compared to available clinical DSC-MRI. Results: Transient hypoxia induced-dOHb based maps of resting perfusion displayed perfusion patterns consistent with underlying tumor histology and showed high spatial coherence to gadolinium-based DSC MR perfusion maps. Conclusion: Non-invasive transient hypoxia induced-dOHb was well-tolerated in patients with different types of brain tumors, and the generated rCBV, rCBF and MTT maps appear in good agreement with perfusion maps generated with gadolinium-based DSC MR perfusion.

The volume of steal phenomenon is associated with neurological deterioration in patients with large-vessel occlusion minor stroke not eligible for thrombectomy.

INTRODUCTION: A significant number of patients who present with mild symptoms following large-vessel occlusion acute ischemic stroke (LVO-AIS) are currently considered ineligible for EVT. However, they frequently experience neurological deterioration during hospitalization. This study aimed to investigate the association between neurological deterioration and hemodynamic impairment by assessing steal phenomenon derived from blood oxygenation-level dependent cerebrovascular reactivity (BOLD-CVR) in this specific patient cohort. PATIENTS AND METHODS: From the database of our single-center BOLD-CVR observational cohort study (June 2015-October 2023) we retrospectively identified acute ischemic stroke patients with admission NIHSS < 6, a newly detected large vessel occlusion of the anterior circulation and ineligible for EVT. Neurological deterioration during hospitalization as well as outcome at hospital discharge were rated with NIHSS score. We analyzed the association between these two outcomes and BOLD-CVR-derived steal phenomenon volume through regression analysis. Additionally, we investigated the discriminatory accuracy of steal phenomenon volume for predicting neurological deterioration. RESULTS: Forty patients were included in the final analysis. Neurological deterioration occurred in 35% of patients. In the regression analysis, a strong association between steal phenomenon volume and neurological deterioration (OR 4.80, 95% CI 1.32-31.04, p = 0.04) as well as poorer NIHSS score at hospital discharge (OR 3.73, 95% CI 1.52-10.78, p = 0.007) was found. The discriminatory accuracy of steal phenomenon for neurological deterioration prediction had an AUC of 0.791 (95% CI 0.653-0.930). DISCUSSION: Based on our results we may distinguish two groups of patients with minor stroke currently ineligible for EVT, however, showing hemodynamic impairment and exhibiting neurological deterioration during hospitalization: (1) patients exhibiting steal phenomenon on BOLD-CVR imaging as well as hemodynamic impairment on resting perfusion imaging; (2) patients exhibiting steal phenomenon on BOLD-CVR imaging, however, no relevant hemodynamic impairment on resting perfusion imaging. CONCLUSION: The presence of BOLD-CVR derived steal phenomenon may aid to further study hemodynamic impairment in patients with minor LVO-AIS not eligible for EVT.

Increased Risk of Recurrent Stroke in Symptomatic Large Vessel Disease With Impaired BOLD Cerebrovascular Reactivity.

BACKGROUND: Impaired cerebrovascular reactivity (CVR) has been correlated with recurrent ischemic stroke. However, for clinical purposes, most CVR techniques are rather complex, time-consuming, and lack validation for quantitative measurements. The recent adaptation of a standardized hypercapnic stimulus in combination with a blood-oxygenation-level-dependent (BOLD) magnetic resonance imaging signal as a surrogate for cerebral blood flow offers a potential universally comparable CVR assessment. We investigated the association between impaired BOLD-CVR and risk for recurrent ischemic events. METHODS: We conducted a retrospective analysis of patients with symptomatic cerebrovascular large vessel disease who had undergone a prospective hypercapnic-challenged BOLD-CVR protocol at a single tertiary stroke referral center between June 2014 and April 2020. These patients were followed up for recurrent acute ischemic events for up to 3 years. BOLD-CVR (%BOLD signal change per mm Hg CO2) was calculated on a voxel-by-voxel basis. Impaired BOLD-CVR of the affected (ipsilateral to the vascular pathology) hemisphere was defined as an average BOLD-CVR, falling 2 SD below the mean BOLD-CVR of the right hemisphere in a healthy age-matched reference cohort (n=20). Using a multivariate Cox proportional hazards model, the association between impaired BOLD-CVR and ischemic stroke recurrence was assessed and Kaplan-Meier survival curves to visualize the acute ischemic stroke event rate. RESULTS: Of 130 eligible patients, 28 experienced recurrent strokes (median, 85 days, interquartile range, 5-166 days). Risk factors associated with an increased recurrent stroke rate included impaired BOLD-CVR, a history of atrial fibrillation, and heart insufficiency. After adjusting for sex, age group, and atrial fibrillation, impaired BOLD-CVR exhibited a hazard ratio of 10.73 (95% CI, 4.14-27.81; P<0.001) for recurrent ischemic stroke. CONCLUSIONS: Among patients with symptomatic cerebrovascular large vessel disease, those exhibiting impaired BOLD-CVR in the affected hemisphere had a 10.7-fold higher risk of recurrent ischemic stroke events compared with individuals with nonimpaired BOLD-CVR.

Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning.

OBJECTIVES: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an adjunctive dimension in patient assessment. It is conceivable that there are different subsets of patients with OFI and degenerative lumbar disease. We aim to identify clusters of objectively functionally impaired individuals based on 5R-STS and unsupervised machine learning (ML). METHODS: Data from two prospective cohort studies on patients with surgery for degenerative lumbar disease and 5R-STS times of ≥ 10.5 s-indicating presence of OFI. K-means clustering-an unsupervised ML algorithm-was applied to identify clusters of OFI. Cluster hallmarks were then identified using descriptive and inferential statistical analyses. RESULTS: We included 173 patients (mean age [standard deviation]: 46.7 [12.7] years, 45% male) and identified three types of OFI. OFI Type 1 (57 pts., 32.9%), Type 2 (81 pts., 46.8%), and Type 3 (35 pts., 20.2%) exhibited mean 5R-STS test times of 14.0 (3.2), 14.5 (3.3), and 27.1 (4.4) seconds, respectively. The grades of OFI according to the validated baseline severity stratification of the 5R-STS increased significantly with each OFI type, as did extreme anxiety and depression symptoms, issues with mobility and daily activities. Types 1 and 2 are characterized by mild to moderate OFI-with female gender, lower body mass index, and less smokers as Type I hallmarks. CONCLUSIONS: Unsupervised learning techniques identified three distinct clusters of patients with OFI that may represent a more holistic clinical classification of patients with OFI than test-time stratifications alone, by accounting for individual patient characteristics.

Safety of microneurosurgical interventions for superficial and deep-seated brain metastases: single-center cohort study of 637 consecutive cases.

PURPOSE: Microneurosurgical techniques have greatly improved over the past years due to the introduction of new technology and surgical concepts. To reevaluate the role of micro-neurosurgery in brain metastases (BM) resection in the era of new systemic and local treatment options, its safety profile needs to be reassessed. The aim of this study was to analyze the rate of adverse events (AEs) according to a systematic, comprehensive and reliably reproducible grading system after microneurosurgical BM resection in a large and modern microneurosurgical series with special emphasis on anatomical location. METHODS: Prospectively collected cases of BM resection between 2013 and 2022 were retrospectively analyzed. Number of AEs, defined as any deviations from the expected postoperative course according to Clavien-Dindo-Grade (CDG) were evaluated. Patient, surgical, and lesion characteristics, including exact anatomic tumor locations, were analyzed using uni- and multivariate logistic regression and survival analysis to identify predictive factors for AEs. RESULTS: We identified 664 eligible patients with lung cancer being the most common primary tumor (44%), followed by melanoma (25%) and breast cancer (11%). 29 patients (4%) underwent biopsy only whereas BM were resected in 637 (96%) of cases. The overall rate of AEs was 8% at discharge. However, severe AEs (≥ CDG 3a; requiring surgical intervention under local/general anesthesia or ICU treatment) occurred in only 1.9% (n = 12) of cases with a perioperative mortality of 0.6% (n = 4). Infratentorial tumor location (OR 5.46, 95% 2.31-13.8, p = .001), reoperation (OR 2.31, 95% 1.07-4.81, p = .033) and central region tumor location (OR 3.03, 95% 1.03-8.60) showed to be significant predictors in a multivariate analysis for major AEs (CDG ≥ 2 or new neurological deficits). Neither deep supratentorial nor central region tumors were associated with more major AEs compared to convexity lesions. CONCLUSIONS: Modern microneurosurgical resection can be considered an excellent option in the management of BM in terms of safety, as the overall rate of major AEs are very rare even in eloquent and deep-seated lesions.

Blood Oxygenation Level-Dependent Cerebrovascular Reactivity-Derived Steal Phenomenon May Indicate Tissue Reperfusion Failure After Successful Endovascular Thrombectomy.

In acute ischemic stroke due to large-vessel occlusion (LVO), the clinical outcome after endovascular thrombectomy (EVT) is influenced by the extent of autoregulatory hemodynamic impairment, which can be derived from blood oxygenation level-dependent cerebrovascular reactivity (BOLD-CVR). BOLD-CVR imaging identifies brain areas influenced by hemodynamic steal. We sought to investigate the presence of steal phenomenon and its relationship to DWI lesions and clinical deficit in the acute phase of ischemic stroke following successful vessel recanalization.From the prospective longitudinal IMPreST (Interplay of Microcirculation and Plasticity after ischemic Stroke) cohort study, patients with acute ischemic unilateral LVO stroke of the anterior circulation with successful endovascular thrombectomy (EVT; mTICI scale ≥ 2b) and subsequent BOLD-CVR examination were included for this analysis. We analyzed the spatial correlation between brain areas exhibiting BOLD-CVR-associated steal phenomenon and DWI infarct lesion as well as the relationship between steal phenomenon and NIHSS score at hospital discharge.Included patients (n = 21) exhibited steal phenomenon to different extents, whereas there was only a partial spatial overlap with the DWI lesion (median 19%; IQR, 8-59). The volume of steal phenomenon outside the DWI lesion showed a positive correlation with overall DWI lesion volume and was a significant predictor for the NIHSS score at hospital discharge.Patients with acute ischemic unilateral LVO stroke exhibited hemodynamic steal identified by BOLD-CVR after successful EVT. Steal volume was associated with DWI infarct lesion size and with poor clinical outcome at hospital discharge. BOLD-CVR may further aid in better understanding persisting hemodynamic impairment following reperfusion therapy.

Tailored therapy for recurrent glioblastoma: report of a personalized molecular approach.

BACKGROUND: Failure of clinical trials with targeted therapies in glioblastoma (GBM) is probably related to the enrollment of molecularly unselected patients. In this study we report the results of a precision medicine protocol in recurrent GBM. METHODS: We prospectively evaluated 34 patients with recurrent GBM. We determined the expression of vascular endothelial growth factor (VEGF), epidermal growth factor receptor variant III (EGFRvIII), and phosphatase and tensin homolog (PTEN). According to the molecular pattern we administered bevacizumab alone in patients with VEGF overexpression, absence of EGFRvIII, and normal PTEN (group A; N.=16); bevacizumab + erlotinib in patients with VEGF overexpression, expression of EGFRvIII, and normal PTEN (group B; N.=14); and bevacizumab + sirolimus in patients with VEGF overexpression and loss of PTEN, irrespective of the EGFRvIII status (group C; N.=4). We evaluated the response rate, the clinical benefit rate, the 6-month progression-free survival (PFS-6), the 12-month PFS (PFS-12) and the safety profile of the treatment. Moreover, we compared our results with the ones of EORTC 26101 trial. RESULTS: Response rate was 50% in the whole cohort with the highest rate in group C (75%). Clinical benefit rate was 71% with the highest rate in group C (75%). PFS-6 was 56% in the whole cohort with the highest rate in group B (64%). PFS-12 was 21% in the whole cohort with the highest rate in group B (29%). When comparing our results with those from the combination arm of the EORTC 26101 trial we found a significantly higher PFS-6 and PFS-12 in our cohort. CONCLUSIONS: The precision medicine protocol for recurrent GBM is feasible and leads to improved results if compared with studies lacking molecular selection.

Assessing Perfusion in Steno-Occlusive Cerebrovascular Disease Using Transient Hypoxia-Induced Deoxyhemoglobin as a Dynamic Susceptibility Contrast Agent.

BACKGROUND AND PURPOSE: Resting brain tissue perfusion in cerebral steno-occlusive vascular disease can be assessed by MR imaging using gadolinium-based susceptibility contrast agents. Recently, transient hypoxia-induced deoxyhemoglobin has been investigated as a noninvasive MR imaging contrast agent. Here we present a comparison of resting perfusion metrics using transient hypoxia-induced deoxyhemoglobin and gadolinium-based contrast agents in patients with known cerebrovascular steno-occlusive disease. MATERIALS AND METHODS: Twelve patients with steno-occlusive disease underwent DSC MR imaging using a standard bolus of gadolinium-based contrast agent compared with transient hypoxia-induced deoxyhemoglobin generated in the lungs using an automated gas blender. A conventional multi-slice 2D gradient echo sequence was used to acquire the perfusion data and analyzed using a standard tracer kinetic model. MTT, relative CBF, and relative CBV maps were generated and compared between contrast agents. RESULTS: The spatial distributions of the perfusion metrics generated with both contrast agents were consistent. Perfusion metrics in GM and WM were not statistically different except for WM MTT. CONCLUSIONS: Cerebral perfusion metrics generated with noninvasive transient hypoxia-induced changes in deoxyhemoglobin are very similar to those generated using a gadolinium-based contrast agent in patients with cerebrovascular steno-occlusive disease.

Sex-related differences in postoperative complications following elective craniotomy for intracranial lesions: An observational study.

INTRODUCTION: The integration of sex-related differences in neurosurgery is crucial for new, possible sex-specific, therapeutic approaches. In neurosurgical emergencies, such as traumatic brain injury and aneurysmal subarachnoid hemorrhage, these differences have been investigated. So far, little is known concerning the impact of sex on frequency of postoperative complications after elective craniotomy. This study investigates whether sex-related differences exist in frequency of postoperative complications in patients who underwent elective craniotomy for intracranial lesion. MATERIAL AND METHODS: All consecutive patients who underwent an elective intracranial procedure over a 2-year period at our center were eligible for inclusion in this retrospective study. Demographic data, comorbidities, frequency of postoperative complications at 24 hours following surgery and at discharge, and hospital length of stay were compared among females and males. RESULTS: Overall, 664 patients were considered for the analysis. Of those, 339 (50.2%) were females. Demographic data were comparable among females and males. More females than males suffered from allergic, muscular, and rheumatic disorders. No differences in frequency of postoperative complications at 24 hours after surgery and at discharge were observed among females and males. Similarly, the hospital length of stay was comparable. CONCLUSIONS: In the present study, no sex-related differences in frequency of early postoperative complications and at discharge following elective craniotomy for intracranial lesions were observed.

Comparison between rupture/growth risk scores and treatment recommendation scores application to aneurysmal subarachnoid hemorrhage patients: A multicenter cross-reliability assessment study.

BACKGROUND: Two score families were introduced to help clinicians about the decision-making regarding intracranial aneurysms management. The first family estimates the growth/rupture risk (GRS), whereas the second provides straightforward recommendation (RS) for treatment decisions. However, both remain poorly validated and little is known about their agreement. In this paper, we performed a retrospective concordance analysis among the two scores families through their application to a multicenter cohort of SAH patients. METHODS: Demographical, clinical and radiological data were extracted in conformance with the variables included in PHASES, UCAS, ELAPSS, Juvela's growth score (JGS), UIATS and Juvela's treatment score (JTS). Individual patients' score were calculated for both score families, and pooled data were then analyzed. RESULTS: Overall, 146 patients were included. True positive rates were: 51.4% for PHASES; 71.9% for UCAS; between 60.3% and 90.4% for JTS; and between 27.4% and 68.5% for UIATS. In patients showing UIATS unclear recommendation and low JTS score (RS), UCAS outperformed PHASES (GRS) in identifying aneurysms at higher risk of rupture. Same results we found for patients with conservative UIATS recommendation and very low JTS score. Forty-to-sixty percent of aneurysms with unclear or conservative RS recommendation would have been identified as at high risk with GRS. CONCLUSIONS: Retrospectively applied, JTS appeared outperforming UIATS in correctly recommending treatment in a higher percentage of patients. UIATS and JTS appeared agreeing more with UCAS than PHASES predictions. Around 50% of patients with unclear or conservative UIATS/JTS recommendations were been classified as at higher growth risk by ELAPSS and JGS.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions.

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading.

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease.

BACKGROUND: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. METHODS: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. RESULTS: Models were developed and integrated into a web-app ( https://neurosurgery.shinyapps.io/fuseml/ ) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59-0.74], back pain (0.72, 95%CI: 0.64-0.79), and leg pain (0.64, 95%CI: 0.54-0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. CONCLUSIONS: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk-benefit estimation, truly impacting clinical practice in the era of "personalized medicine" necessitates more robust tools in this patient population.

Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge.

OBJECTIVES: Innovative physiologic MRI development focuses on depiction of heterogenous vascular and metabolic features in glioblastoma. For this feasibility study, we employed blood oxygenation level-dependent (BOLD) MRI with standardized and precise carbon dioxide (CO2) and oxygen (O2) modulation to investigate specific tumor tissue response patterns in patients with newly diagnosed glioblastoma. MATERIALS AND METHODS: Seven newly diagnosed untreated patients with suspected glioblastoma were prospectively included to undergo a BOLD study with combined CO2 and O2 standardized protocol. %BOLD signal change/mmHg during hypercapnic, hypoxic, and hyperoxic stimulus was calculated in the whole brain, tumor lesion and segmented volumes of interest (VOI) [contrast-enhancing (CE) - tumor, necrosis and edema] to analyze their tissue response patterns. RESULTS: Quantification of BOLD signal change after gas challenges can be used to identify specific responses to standardized stimuli in glioblastoma patients. Integration of this approach with automatic VOI segmentation grants improved characterization of tumor subzones and edema. Magnitude of BOLD signal change during the 3 stimuli can be visualized at voxel precision through color-coded maps overlayed onto whole brain and identified VOIs. CONCLUSIONS: Our preliminary investigation shows good feasibility of BOLD with standardized and precise CO2 and O2 modulation as an emerging physiologic imaging technique to detail specific glioblastoma characteristics. The unique tissue response patterns generated can be further investigated to better detail glioblastoma lesions and gauge treatment response.

Foundations of Feature Selection in Clinical Prediction Modeling.

Selecting a set of features to include in a clinical prediction model is not always a simple task. The goals of creating parsimonious models with low complexity while, at the same time, upholding predictive performance by explaining a large proportion of the variance within the dependent variable must be balanced. With this aim, one must consider the clinical setting and what data are readily available to clinicians at specific timepoints, as well as more obvious aspects such as the availability of computational power and size of the training dataset. This chapter elucidates the importance and pitfalls in feature selection, focusing on applications in clinical prediction modeling. We demonstrate simple methods such as correlation-, significance-, and variable importance-based filtering, as well as intrinsic feature selection methods such as Lasso and tree- or rule-based methods. Finally, we focus on two algorithmic wrapper methods for feature selection that are commonly used in machine learning: Recursive Feature Elimination (RFE), which can be applied regardless of data and model type, as well as Purposeful Variable Selection as described by Hosmer and Lemeshow, specifically for generalized linear models.

Machine Learning Algorithms in Neuroimaging: An Overview.

Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. This chapter summarizes the basic concepts underlying ML application for neuroimaging and discusses technical aspects of the most promising algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in order to provide the readership with the fundamental theoretical tools to better understand ML in neuroimaging. Applications are highlighted from a practical standpoint in the last section of the chapter, including: image reconstruction and restoration, image synthesis and super-resolution, registration, segmentation, classification, and outcome prediction.

Machine Learning in Pituitary Surgery.

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

Reassessing the Role of Brain Tumor Biopsy in the Era of Advanced Surgical, Molecular, and Imaging Techniques-A Single-Center Experience with Long-Term Follow-Up.

Brain biopsy is the gold standard in order to establish the diagnosis of unresectable brain tumors. Few studies have investigated the long-term outcomes of biopsy patients. The aim of this single-institution-based study was to assess the concordance between radiological and histopathological diagnoses, and the long-term patient outcome. Ninety-three patients who underwent brain biopsy in the last 5 years were analyzed. We included patients treated with stereotactically guided needle, open, and neuroendoscopic biopsies. Most patients (86%) received needle biopsy. Gliomas and primary brain lymphomas comprised 88.2% of cases. The diagnostic yield was 95.7%. Serious complication and death rates were 3.2% and 2.1%, respectively. The concordance rate between radiological and histological diagnoses was 93%. Notably, the positive predictive value of radiological diagnosis of lymphoma was 100%. Biopsy allowed specific treatment in 72% of cases. Disease-related neurological worsening was the main reason that precluded adjuvant treatment. Adjuvant treatment, in turn, was the strongest prognostic factor, since the median overall survival was 11 months with vs. 2 months without treatment (p = 0.0002). Finally, advanced molecular evaluations can be obtained on glioma biopsy specimens to provide integrated diagnoses and individually tailored treatments. We conclude that, despite the huge advances in imaging techniques, biopsy is required when an adjuvant treatment is recommended, particularly in gliomas.