[Economic burden assessment for the management of asthma patients at Mexico's National Institute for Respiratory Diseases]. / Evaluación de la carga económica para el tratamiento de pacientes con asma en el Instituto Nacional de Enfermedades Respiratorias de México.

OBJETIVO: Determinar la carga económica anual del asma, desde una perspectiva institucional y con base en la clasificación recomendada por GINA, en una cohorte retrospectiva de adultos atendidos en el Instituto Nacional de Enfermedades Respiratorias (INER) de México. MÉTODOS: Estudio observacional, longitudinal y retrospectivo, llevado a cabo a partir de la información recabada de 247 pacientes femeninas con asma. Se estimaron los costos directos anuales: visitas, pruebas de laboratorio, tratamiento farmacológico y de las crisis o exacerbaciones, para determinar la carga anual de la enfermedad desde una perspectiva institucional, y según la clasificación de la Iniciativa Global para el Asma. RESULTADOS: El costo promedio anual fue de $43,813,92, que aumentó en relación con la necesidad de aumento de dosis de corticoides inhalados y beta-agonistas de acción prolongada. El costo promedio de la consulta médica fue de $2004.57, $982.82 por gestión de crisis y $2645.95 por pruebas de laboratorio. El tratamiento farmacológico representó la principal carga económica, con un costo promedio anual de $38,180.58. CONCLUSIONES: Los resultados resaltan una carga económica del asma estimada en un costo anual por paciente de $43,813.92 MXN (DE=93,348.85), en el contexto del tercer nivel de atención en el sistema de salud público mexicano. La gravedad del asma, los tratamientos y los biológicos fueron los principales factores que aumentaron los costos directos de la atención.

OBJECTIVE: Determine the annual economic burden of the disease from an institutional perspective and based on GINA's recommended classification in a retrospective cohort of adults treated at Instituto Nacional de Enfermedades Respiratorias (INER) of Mexico City. METHODS: A retrospective, longitudinal observational study comprised by data from 247 female asthma patients, annual direct costs were estimated including: visits, laboratory tests, pharmacological treatment and management of crisis or exacerbations, to determine the annual burden of the disease from an institutional perspective and according to Global Initiative for Asthma classification. RESULTS: The average annual cost was $43,813.92, which increased in relation to the need of inhaled corticosteroids and long-acting beta agonists dosage increase. The average doctor's appointment cost was $2,004.57, $982.82 for crisis management and $2,645.95 for laboratory testing. Pharmacological treatment represented the main economic burden with an annual average cost of $38,180.58. CONCLUSIONS: The results highlight an economic burden of asthma estimated at an annual cost per patient of $43,813.92 MXN (SD=93,348.85) in the context of the third level of care in the Mexican public health system. The asthma severity and treatments such as biologics were the main factors that increased direct costs of care.

Non-navigated 2D intraoperative ultrasound: An unsophisticated surgical tool to achieve high standards of care in glioma surgery.

PURPOSE: In an era characterized by rapid progression in neurosurgical technologies, traditional tools such as the non-navigated two-dimensional intraoperative ultrasound (nn-2D-IOUS) risk being overshadowed. Against this backdrop, this study endeavors to provide a comprehensive assessment of the clinical efficacy and surgical relevance of nn-2D-IOUS, specifically in the context of glioma resections. METHODS: This retrospective study undertaken at a single center evaluated 99 consecutive, non-selected patients diagnosed with both high-grade and low-grade gliomas. The primary objective was to assess the proficiency of nn-2D-IOUS in generating satisfactory image quality, identifying residual tumor tissue, and its influence on the extent of resection. To validate these results, early postoperative MRI data served as the reference standard. RESULTS: The nn-2D-IOUS exhibited a high level of effectiveness, successfully generating good quality images in 79% of the patients evaluated. With a sensitivity rate of 68% and a perfect specificity of 100%, nn-2D-IOUS unequivocally demonstrated its utility in intraoperative residual tumor detection. Notably, when total tumor removal was the surgical objective, a resection exceeding 95% of the initial tumor volume was achieved in 86% of patients. Additionally, patients in whom residual tumor was not detected by nn-2D-IOUS, the mean volume of undetected tumor tissue was remarkably minimal, averaging at 0.29 cm3. CONCLUSION: Our study supports nn-2D-IOUS's invaluable role in glioma surgery. The results highlight the utility of traditional technologies for enhanced surgical outcomes, even when compared to advanced alternatives. This is particularly relevant for resource-constrained settings and emphasizes optimizing existing tools for efficient patient care. NCT05873946 - 24/05/2023 - Retrospectively registered.

The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM).

Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.

Posterior cervical triangle approach for carotid endarterectomy: Technical note and results / El triángulo cervical posterior como abordaje en la endarterectomía carotídea: nota técnica y resultados

Introduction Carotid endarterectomy (CEA) is usually performed using the anterior cervical triangle as a surgical corridor but, when needed, the retromandibular space makes dissection of higher structures difficult in some cases. The posterior cervical triangle (PCT) can be useful in these demanding cases. Methods We retrospectively reviewed cases from July 2013 to November 2019 in which PCT was used as an approach for CEA. The surgical technique used was explained, and the complications and evolution of the patients were analysed. Results We found 7 CEAs performed through this approach, of which 2 presented transient trapezius paresis. There were no cases of severe complications in this series. Conclusion The PCT approach for performing CEA represents a useful and easy technique that avoids the need for mandibular mobilisation or osteotomies for lesions located in anatomically high carotid bifurcations (AU)

Introducción La endarterectomía carotídea (ECA) se suele realizar utilizando el triángulo cervical anterior como corredor quirúrgico, pero el espacio retromandibular dificulta en algunos casos la disección de estructuras de localización superior. El triángulo cervical posterior (TCP) puede ser útil en estos casos complejos. Métodos Se ha realizado un estudio retrospectivo de los casos atendidos en nuestra unidad utilizando el TCP como abordaje en ECA desde julio de 2013 hasta noviembre de 2019. Se explica la técnica quirúrgica empleada y se han revisado las complicaciones y evolución de los pacientes. Resultados Se realizaron 7 ECA a través de este abordaje. Dos pacientes presentaron paresia transitoria del trapecio. No hubo casos de complicaciones graves en esta serie. Conclusión El abordaje por el TCP para la realización de ECA representa una técnica útil y técnicamente simple, que evita procedimientos de movilización mandibular para lesiones localizadas en bifurcaciones carotídeas anatómicamente altas (AU)

Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI.

The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.

Posterior cervical triangle approach for carotid endarterectomy: Technical note and results.

INTRODUCTION: Carotid endarterectomy (CEA) is usually performed using the anterior cervical triangle as a surgical corridor but, when needed, the retromandibular space makes dissection of higher structures difficult in some cases. The posterior cervical triangle (PCT) can be useful in these demanding cases. METHODS: We retrospectively reviewed cases from July 2013 to November 2019 in which PCT was used as an approach for CEA. The surgical technique used was explained, and the complications and evolution of the patients were analysed. RESULTS: We found 7 CEAs performed through this approach, of which 2 presented transient trapezius paresis. There were no cases of severe complications in this series. CONCLUSION: The PCT approach for performing CEA represents a useful and easy technique that avoids the need for mandibular mobilisation or osteotomies for lesions located in anatomically high carotid bifurcations.

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan.

BACKGROUND: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm. METHODS: We conducted a retrospective multicentric study of a consecutive cohort of patients with SAH. Demographic, clinical and radiological variables were analyzed. Preprocessed baseline CT scan images were used as the input for training using the AUCMEDI framework. Our model's architecture leveraged a DenseNet121 structure, employing transfer learning principles. The output variable was mortality in the first three months. RESULTS: Images from 219 patients were processed; 175 for training and validation and 44 for the model's evaluation. Of the patients, 52% (115/219) were female and the median age was 58 (SD = 13.06) years. In total, 18.5% (39/219) had idiopathic SAH. The mortality rate was 28.5% (63/219). The model showed good accuracy at predicting mortality in SAH patients when exclusively using the images of the initial CT scan (accuracy = 74%, F1 = 75% and AUC = 82%). CONCLUSION: Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge.

Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review.

Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor's biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.

Treatment of unruptured middle cerebral artery aneurysms: Systematic review in an attempt to perform a network meta-analysis.

Objective: Open surgical clipping has been generally considered the best treatment option for unruptured middle cerebral artery aneurysms (uMCAAs). However, this type of aneurysm is being treated endovascularly with the appearance of new devices. We have carried out a systematic review of randomized and quasi-experimental studies to conduct a network meta-analysis (NMA) to assess the safety and efficacy of the different treatment methods currently used in uMCAAs. Methods: The literature was searched by using PubMed and Google Scholar databases. Eligibility criteria were randomized or quasi-experimental studies including at least five cases per arm and reporting duration of follow-up and number of lost cases. The end points were: angiographic success, final neurological outcome, and the need for retreatments. Results: We could only analyze four quasi-experimental studies with 398 uMCAAs. All of them compared clipping vs. coiling. Clipping showed better results than coiling in all analyzed end points. We could not conduct the proposed NMA because of the absence of randomized or quasi-experimental studies. Instead, a systematic review is further discussed. Conclusions: There is an urgent need for comparative studies on the treatment of uMCAAs.

Validation of Presurgical Simulation of White Matter Damage Using Diffusion Tensor Imaging.

BACKGROUND: The understanding of white matter (WM) was revolutionized by the emergence of tractography based on diffusion tensor imaging (DTI). Currently, DTI simulations are implemented in preoperative planning to optimize surgical approaches. The reliability of these simulations has been questioned and investigated seeking for correlation between neurological performance and anomalies in DTI parameters. However, the ability of preoperative WM simulations to predict a surgical injury has not been thoroughly evaluated. Our objective was to assess the reliability of preoperatively simulated WM injuries for conventional neurosurgical procedures. METHODS: WM surgical damage was preoperatively simulated by creating a 3-dimensional volume representing the endoscope or the surgical trajectory. This volume was used as an additional region of interest in the fascicle reconstruction to be subtracted from the original fascicle. Simulated, injured fascicles were compared in terms of the number of fibers and volume to those created from postoperative DTI studies. Reliability was assimilated into the correlation between the simulation and the postoperative reconstruction; evaluated using the intraclass correlation coefficient or Lin's Concordance correlation coefficient (CCC), and represented on Bland-Altman plots. RESULTS: The preoperative and postoperative DTI studies of 30 patients undergoing various neurosurgical approaches were processed. The correlation between simulated injuries and postoperative studies was high in terms of fibers (Concordance correlation coefficient = Rho.C = 0.989 [95% confidence interval = 0.979-0.995]) and volume (intraclass correlation coefficient = 0.95 [95% CI = 0.89-0.97]). Bland-Altman plots demonstrated that the great majority of cases fell within the mean ± 2 Standard deviations. CONCLUSIONS: Presurgical simulation of WM fascicles based on DTI is consistent with postoperative DTI studies. These findings require further validation by neurophysiological and clinical correlation.

Brain dural arteriovenous fistulas in the COVID-19 Era: A warning and rationale for association.

OBJECTIVES: Brain dural arteriovenous fistulas(bDAVFs) are anomalous connections between dural arteries and cerebral veins or sinuses. Cerebral venous thrombosis(CVT) often precedes or coincides with bDAVFs and is considered a risk factor for these vascular malformations. Recently, vaccine-induced thrombotic thrombocytopenia causing CVTs has been associated with COVID-19 vaccines. Concurrently with the start of massive vaccination in our region, we have observed a fivefold increase in the average incidence of bDAVFs. Our objective is to raise awareness of the potential involvement of COVID-19 vaccines in the pathogenesis of bDAVF. METHODS: A retrospective review of demographic, clinical, radiological, COVID-19 infection and vaccination data of patients diagnosed with bDAVFs between 2011 and 2021 was conducted. Patients were divided into two cohorts according to their belonging to pre- or post-COVID-19 vaccination times. Cohorts were compared for bDAVFs incidences and demographic and clinical features. RESULTS: Twenty-one bDAVFs were diagnosed between 2011 and 2021, 7 of which in 2021. The mean age was 57.7 years, and 62 % were males. All cases except one were treated; of them, 85 % exclusively managed with surgery. All treated cases were successfully occluded. The incidence in 2021 was significantly higher than that in the prevaccination period (1.72 vs 0.35/100,000/year;p = 0.036; 95 %Confidence Interval=0.09-2.66). Cohorts were not different in age, sex, hemorrhagic presentation, dural sinus thrombosis or presence of prothrombotic or cardiovascular risk factors. CONCLUSION: The significant increase in the incidence of bDAVF following general vaccination policies against COVID-19 observed in our region suggests a potential correlation between these two facts. Our findings need confirmation from larger cohorts and further pathogenic research.

Advantages and Limitations of Intraoperative Ultrasound Strain Elastography Applied in Brain Tumor Surgery: A Single-Center Experience.

BACKGROUND: Strain elastography is an intraoperative ultrasound (ioUS) modality currently under development with various potential applications in neurosurgery. OBJECTIVE: To describe the main technical aspects, usefulness, and limitations of ioUS strain elastography applied in a large case series of brain tumors. METHODS: We retrospectively analyzed patients who underwent craniotomy for a brain tumor between March 2018 and March 2021. Cases with an ioUS strain elastography study were included. The elastograms were processed semiquantitatively, and the mean tissue elasticity (MTE) values were calculated from the histogram of intensities. An analysis was performed to correlate the histopathological groups and the tumor and peritumoral MTE values using the Kruskal-Wallis test and a decision tree classifier. Furthermore, elastogram quality was assessed to discuss possible artifacts and weaknesses of the ultrasound technique. RESULTS: One hundred two patients with the following histopathological diagnoses were analyzed: 43 high-grade gliomas, 11 low-grade gliomas, 28 meningiomas, and 20 metastases. The tumor MTE values were significantly different between the histopathological groups (P < .001). The decision tree classifier showed an area under the curve of 0.73 and a classification accuracy of 72%. The main technical limitations found in our series were the presence of artifacts after dural opening, the variability of the frequency and amplitude of the mechanical pulsations, and the challenge in evaluating deep lesions. CONCLUSION: Tumor stiffness revealed by ioUS strain elastography has a plausible histopathological correlation. Thus, this fast and versatile technique has enormous potential to be exploited in the coming years.

Relationship between the overall survival in glioblastomas and the radiomic features of intraoperative ultrasound: a feasibility study.

PURPOSE: Predicting the survival of patients diagnosed with glioblastoma (GBM) is essential to guide surgical strategy and subsequent adjuvant therapies. Intraoperative ultrasound (IOUS) can contain biological information that could be correlated with overall survival (OS). We propose a simple extraction method and radiomic feature analysis based on IOUS imaging to estimate OS in GBM patients. METHODS: A retrospective study of surgically treated glioblastomas between March 2018 and November 2019 was performed. Patients with IOUS B-mode and strain elastography were included. After preprocessing, segmentation and extraction of radiomic features were performed with LIFEx software. An evaluation of semantic segmentation was carried out using the Dice similarity coefficient (DSC). Using univariate correlations, radiomic features associated with OS were selected. Subsequently, survival analysis was conducted using Cox univariate regression and Kaplan-Meier curves. RESULTS: Sixteen patients were available for analysis. The DSC revealed excellent agreement for the segmentation of the tumour region. Of the 52 radiomic features, two texture features from B-mode (conventional mean and the grey-level zone length matrix/short-zone low grey-level emphasis [GLZLM_SZLGE]) and one texture feature from strain elastography (grey-level zone length matrix/long-zone high grey-level emphasis [GLZLM_LZHGE]) were significantly associated with OS. After establishing a cut-off point of the statistically significant radiomic features, we allocated patients in high- and low-risk groups. Kaplan-Meier curves revealed significant differences in OS. CONCLUSION: IOUS-based quantitative texture analysis in glioblastomas is feasible. Radiomic tumour region characteristics in B-mode and elastography appear to be significantly associated with OS.

Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI.

Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

Intraoperative magnetic resonance imaging for cerebral cavernous malformations: When is it maybe worth it?

OBJECTIVE: Intraoperative magnetic resonance imaging (iMRI) can be useful for cerebral cavernous malformations (CCM) surgery. However, literature on this topic is scarce. We aim to investigate its clinical utility and propose criteria for the selection of patients who may benefit the most from iMRI. METHODS: From 2017 to 2019, all patients with CCMs who required surgery assisted with iMRI were included in the study. Clinical and radiological features were analyzed. Outcome measures included the need for an immediate second-look resection and clinical course in early post-surgery -Timepoint 1- (Tp1) and at the 6-to-12-month follow-up -Timepoint2- (Tp2). RESULTS: Out of 19 patients with 20 CCMs, 89% had bleeding in the past, and in 75% the CCM affected an eloquent area. According to the iMRI results, an immediate second-look resection was needed in 16% of them. In one patient, a remnant was not seen on iMRI. The mRS worsened in the immediate post-surgical exam (median, 1; IQR, 1) with improvements on the 6-month visit (median, 1; IQR, 2), (p = 0.018). When comparing the outcome of patients with and without symptoms at baseline, the latter fared better at Tp2 (p = 0.005). CONCLUSIONS: iMRI is an intraoperative imaging tool that seems safe for CCM surgery and might reduce the risk of lesion remnants. In our series, it allowed additional revision for further resection in 16% of the patients. In our experience, iMRI may be especially useful for lesions in eloquent areas, those with a significant risk of brain shift and for large CCMs.

Acute changes in diffusion tensor-derived metrics and its correlation with the motor outcome in gliomas adjacent to the corticospinal tract.

BACKGROUND: This study involves analysis of the relationship between variables obtained using diffusion tensor imaging (DTI) and motor outcome in gliomas adjacent to the corticospinal tract (CST). METHODS: Histologically confirmed glioma patients who were to undergo surgery between January 2018 and December 2019 were prospectively enrolled. All patients had a preoperative magnetic resonance imaging (MRI) study that included DTI, a tumor 2 cm or less from the CST, and postsurgical control within 48 h. Patients with MRI that was performed at other center, tumors with primary and premotor cortex invasion, postsurgical complications directly affecting motor outcome and tumor progression <6 months were excluded in the study. In pre- and post-surgical MRI, we measured the following DTI-derived metrics: fractional anisotropy (FA), mean diffusivity, axial diffusivity, and radial diffusivity of the entire CST and peritumoral CST regions and in the contralateral hemisphere. The motor outcome was assessed at 1, 3, and 6 months using the Medical Research Council scale. RESULTS: Eleven patients were analyzed, and six corresponded to high-grade gliomas and five to low-grade gliomas. Four patients had previous motor impairment and seven patients had postsurgical motor deficits (four transient and three permanent). An FA ratio of 0.8 between peritumoral CST regions and the contralateral hemisphere was found to be the cutoff, and lower values were obtained in patients with permanent motor deficits. CONCLUSION: Quantitative analysis of DTI that was performed in the immediate postsurgery period can provide valuable information about the motor prognosis after surgery for gliomas near the CST.

Is There a Relationship between the Elasticity of Brain Tumors, Changes in Diffusion Tensor Imaging, and Histological Findings? A Pilot Study Using Intraoperative Ultrasound Elastography.

Intraoperative ultrasound elastography (IOUS-E) is a novel image modality applied in brain tumor assessment. However, the potential links between elastographic findings and other histological and neuroimaging features are unknown. This study aims to find associations between brain tumor elasticity, diffusion tensor imaging (DTI) metrics, and cell proliferation. A retrospective study was conducted to analyze consecutively admitted patients who underwent craniotomy for supratentorial brain tumors between March 2018 and February 2020. Patients evaluated by IOUS-E and preoperative DTI were included. A semi-quantitative analysis was performed to calculate the mean tissue elasticity (MTE). Diffusion coefficients and the tumor proliferation index by Ki-67 were registered. Relationships between the continuous variables were determined using the Spearman ρ test. A predictive model was developed based on non-linear regression using the MTE as the dependent variable. Forty patients were evaluated. The pathologic diagnoses were as follows: 21 high-grade gliomas (HGG); 9 low-grade gliomas (LGG); and 10 meningiomas. Cases with a proliferation index of less than 10% had significantly higher medians of MTE (110.34 vs. 79.99, p < 0.001) and fractional anisotropy (FA) (0.24 vs. 0.19, p = 0.020). We found a strong positive correlation between MTE and FA (rs (38) = 0.91, p < 0.001). A cubic spline non-linear regression model was obtained to predict tumoral MTE from FA (R2 = 0.78, p < 0.001). According to our results, tumor elasticity is associated with histopathological and DTI-derived metrics. These findings support the usefulness of IOUS-E as a complementary tool in brain tumor surgery.

Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.

BACKGROUND: The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to develop a predictive model of tumor consistency using the radiomic features of preoperative magnetic resonance imaging and the tumor elasticity measured by intraoperative ultrasound elastography (IOUS-E) as a reference parameter. METHODS: A retrospective analysis was performed on supratentorial meningiomas that were operated on between March 2018 and July 2020. Cases with IOUS-E studies were included. A semiquantitative analysis of elastograms was used to define the meningioma consistency. MRIs were preprocessed before extracting radiomic features. Predictive models were built using a combination of feature selection filters and machine learning algorithms: logistic regression, Naive Bayes, k-nearest neighbors, Random Forest, Support Vector Machine, and Neural Network. A stratified 5-fold cross-validation was performed. Then, models were evaluated using the area under the curve and classification accuracy. RESULTS: Eighteen patients were available for analysis. Meningiomas were classified as hard or soft according to a mean tissue elasticity threshold of 120. The best-ranked radiomic features were obtained from T1-weighted post-contrast, apparent diffusion coefficient map, and T2-weighted images. The combination of Information Gain and ReliefF filters with the Naive Bayes algorithm resulted in an area under the curve of 0.961 and classification accuracy of 94%. CONCLUSIONS: We have developed a high-precision classification model that is capable of predicting consistency of meningiomas based on the radiomic features in preoperative magnetic resonance imaging (T2-weighted, T1-weighted post-contrast, and apparent diffusion coefficient map).

Cost-Effectiveness of Low-Field Intraoperative Magnetic Resonance in Glioma Surgery.

OBJECT: Low-field intraoperative magnetic resonance (LF-iMR) has demonstrated a slight increase in the extent of resection of intra-axial tumors while preserving patient`s neurological outcomes. However, whether this improvement is cost-effective or not is still matter of controversy. In this clinical investigation we sought to evaluate the cost-effectiveness of the implementation of a LF-iMR in glioma surgery. METHODS: Patients undergoing LF-iMR guided glioma surgery with gross total resection (GTR) intention were prospectively collected and compared to an historical cohort operated without this technology. Socio-demographic and clinical variables (pre and postoperative KPS; histopathological classification; Extent of resection; postoperative complications; need of re-intervention within the first year and 1-year postoperative survival) were collected and analyzed. Effectiveness variables were assessed in both groups: Postoperative Karnofsky performance status scale (pKPS); overall survival (OS); Progression-free survival (PFS); and a variable accounting for the number of patients with a greater than subtotal resection and same or higher postoperative KPS (R-KPS). All preoperative, procedural and postoperative costs linked to the treatment were considered for the cost-effectiveness analysis (diagnostic procedures, prosthesis, operating time, hospitalization, consumables, LF-iMR device, etc). Deterministic and probabilistic simulations were conducted to evaluate the consistency of our analysis. RESULTS: 50 patients were operated with LF-iMR assistance, while 146 belonged to the control group. GTR rate, pKPS, R-KPS, PFS, and 1-year OS were respectively 13,8% (not significative), 7 points (p < 0.05), 17% (p < 0.05), 38 days (p < 0.05), and 3.7% (not significative) higher in the intervention group. Cost-effectiveness analysis showed a mean incremental cost per patient of 789 € in the intervention group. Incremental cost-effectiveness ratios were 111 € per additional point of pKPS, 21 € per additional day free of progression, and 46 € per additional percentage point of R-KPS. CONCLUSION: Glioma patients operated under LF-iMR guidance experience a better functional outcome, higher resection rates, less complications, better PFS rates but similar life expectancy compared to conventional techniques. In terms of efficiency, LF-iMR is very close to be a dominant technology in terms of R-KPS, PFS and pKPS.