Prognostic utility and characteristics of MIB-1 labeling index as a proliferative activity marker in childhood low-grade glioma: a retrospective observational study.

PURPOSE: The prognostic utility of MIB-1 labeling index (LI) in pediatric low-grade glioma (PLGG) has not yet conclusively been described. We assess the correlation of MIB-1 LI and tumor growth velocity (TGV), aiming to contribute to the understanding of clinical implications and the predictive value of MIB-1 LI as an indicator of proliferative activity and progression-free survival (PFS) in PLGG. METHODS: MIB-1 LI of a cohort of 172 nonependymal PLGGs were comprehensively characterized. Correlation to TGV, assessed by sequential MRI-based three-dimensional volumetry, and PFS was analyzed. RESULTS: Mean MIB-1 LI accounted for 2.7% (range: < 1-10) and showed a significant decrease to 1.5% at secondary surgery (p = .0013). A significant difference of MIB-1 LI in different histopathological types and a correlation to tumor volume at diagnosis could be shown. Linear regression analysis showed a correlation between MIB-1 LI and preoperative TGV (R2 = .55, p < .0001), while correlation to TGV remarkably decreased after incomplete resection (R2 = .08, p = .013). Log-rank test showed no association of MIB-1 LI and 5-year PFS after incomplete (MIB-1 LI > 1 vs ≤ 1%: 48 vs 46%, p = .73) and gross-total resection (MIB-1 LI > 1 vs ≤ 1%: 89 vs 95%, p = .75). CONCLUSION: These data confirm a correlation of MIB-1 LI and radiologically detectable TGV in PLGG for the first time. Compared with preoperative TGV, a crucially decreasing correlation of MIB-1 LI and TGV after surgery may result in limited prognostic capability of MIB-1 LI in PLGG.

The feasibility of half-dose contrast-enhanced scanning of brain tumours at 5.0 T: a preliminary study.

PURPOSE: This study investigated and compared the effects of Gd enhancement on brain tumours with a half-dose of contrast medium at 5.0 T and with a full dose at 3.0 T. METHODS: Twelve subjects diagnosed with brain tumours were included in this study and underwent MRI after contrast agent injection at 3.0 T (full dose) or 5.0 T (half dose) with a 3D T1-weighted gradient echo sequence. The postcontrast images were compared by two independent neuroradiologists in terms of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective image quality score on a ten-point Likert scale. Quantitative indices and subjective quality ratings were compared with paired Student's t tests, and interreader agreement was assessed with the intraclass correlation coefficient (ICC). RESULTS: A total of 16 enhanced tumour lesions were detected. The SNR was significantly greater at 5.0 T than at 3.0 T in grey matter, white matter and enhanced lesions (p < 0.001). The CNR was also significantly greater at 5.0 T than at 3.0 T for grey matter/tumour lesions, white matter/tumour lesions, and grey matter/white matter (p < 0.001). Subjective evaluation revealed that the internal structure and outline of the tumour lesions were more clearly displayed with a half-dose at 5.0 T (Likert scale 8.1 ± 0.3 at 3.0 T, 8.9 ± 0.3 at 5.0 T, p < 0.001), and the effects of enhancement in the lesions were comparable to those with a full dose at 3.0 T (7.8 ± 0.3 at 3.0 T, 8.7 ± 0.4 at 5.0 T, p < 0.001). All subjective scores were good to excellent at both 5.0 T and 3.0 T. CONCLUSION: Both quantitative and subjective evaluation parameters suggested that half-dose enhanced scanning via 5.0 T MRI might be feasible for meeting clinical diagnostic requirements, as the image quality remains optimal. Enhanced scanning at 5.0 T with a half-dose of contrast agents might benefit patients with conditions that require less intravenous contrast agent, such as renal dysfunction.

Microenvironmental reorganization in brain tumors following radiotherapy and recurrence revealed by hyperplexed immunofluorescence imaging.

The tumor microenvironment plays a crucial role in determining response to treatment. This involves a series of interconnected changes in the cellular landscape, spatial organization, and extracellular matrix composition. However, assessing these alterations simultaneously is challenging from a spatial perspective, due to the limitations of current high-dimensional imaging techniques and the extent of intratumoral heterogeneity over large lesion areas. In this study, we introduce a spatial proteomic workflow termed Hyperplexed Immunofluorescence Imaging (HIFI) that overcomes these limitations. HIFI allows for the simultaneous analysis of > 45 markers in fragile tissue sections at high magnification, using a cost-effective high-throughput workflow. We integrate HIFI with machine learning feature detection, graph-based network analysis, and cluster-based neighborhood analysis to analyze the microenvironment response to radiation therapy in a preclinical model of glioblastoma, and compare this response to a mouse model of breast-to-brain metastasis. Here we show that glioblastomas undergo extensive spatial reorganization of immune cell populations and structural architecture in response to treatment, while brain metastases show no comparable reorganization. Our integrated spatial analyses reveal highly divergent responses to radiation therapy between brain tumor models, despite equivalent radiotherapy benefit.

A 78-year-old woman with diffuse white matter infiltration and predominant involvement of bilateral temporo-parieto-occipital regions.

Diffuse paediatric-type high-grade glioma, H3-wildtype and IDH-wildtype (H3/IDH-wt-pHGG) is a newly defined entity amongst brain tumours, primarily reported in children. It is a rare, ill-defined type of tumour and the only method to diagnose it is DNA methylation profiling. The case we report here carries new knowledge about this tumour which may, in fact, occur in elderly patients, be devoid of evocative genomic abnormalities reported in children and harbour a misleading mutation.

Challenges in radiological evaluation of brain metastases, beyond progression.

MRI is the cornerstone in the evaluation of brain metastases. The clinical challenges lie in discriminating metastases from mimickers such as infections or primary tumors and in evaluating the response to treatment. The latter sometimes leads to growth, which must be framed as pseudo-progression or radionecrosis, both inflammatory phenomena attributable to treatment, or be considered as recurrence. To meet these needs, imaging techniques are the subject of constant research. However, an exponential growth after radiotherapy must be interpreted with caution, even in the presence of results suspicious of tumor progression by advanced techniques, because it may be due to inflammatory changes. The aim of this paper is to familiarize the reader with inflammatory phenomena of brain metastases treated with radiotherapy and to describe two related radiological signs: "the inflammatory cloud" and "incomplete ring enhancement", in order to adopt a conservative management with close follow-up.

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung.

OBJECTIVE: To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma. METHODS: Retrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness. RESULTS: In univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group). CONCLUSIONS: Radiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.

Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. / åŒºåˆ†èƒ¶è´¨æ¯ç»†èƒžç˜¤å’Œå•å‘æ€§è„‘è½¬ç§»ç˜¤çš„å¤šæ¨¡æ€èžåˆæ·±åº¦å­¦ä¹ æ¨¡åž‹çš„å¼€å‘å’ŒéªŒè¯.

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS: Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS: The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS: A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

Surgical treatment of small recurrent gliomas based on MR imaging examination.

Microrecurrent glioma is a common neurological tumor, and the key to its surgical treatment is to accurately evaluate the size, location and degree of recurrence of the lesion. The purpose of this study was to explore the surgical treatment of microrecurrent glioma based on MR Imaging, and to provide accurate and reliable basis for clinical decision-making. Before surgery, detailed MR Imaging tests were performed for each patient to accurately locate and evaluate the characteristics of the lesions. Multimodal imaging examination were arranged to accurate the pre-operation diagnosis. Neuro-navigation is necessary for the operation design and tumor confirmation. Function monitor and intraoperation MR were prepared when necessary.Mini was defined by the size, location and symptoms. In all 5 cases requiring reoperation, total resection was achieved. No systemic and local complications occurred. No permeant neurological dysfunction remained. The average stay time after the operation is days. All patients survived in the recent follow-up. Reoperation of mini recurrent glioma is a good treatment choice. We made little injury to patients, which wouldn't affect their conditions and next therapies. Through MR Imaging, the diagnosis and location of microrecurrent glioma, as well as the relationship with surrounding tissues and the degree of infiltration, provide important information for surgeons to evaluate the resectable lesion. By combining MR And functional imaging results, the blood supply and functional area of the lesion can be monitored in real time during surgery, thereby reducing surgical risk and maximizing the protection of surrounding healthy tissue.

Tumefactive Primary Central Nervous System Vasculitis: Dynamic Susceptibility Contrast Perfusion-Weighted Magnetic Resonance Imaging Findings With Histological Correlation.

Purpose: Primary central nervous system vasculitis (PCNSV) is a rare inflammatory disease affecting the central nervous system. In some cases, it presents with large, solitary lesion with extensive mass effect that mimic intracranial neoplasms. This condition results in a diagnostic confusion for neuroradiologists because the differentiation is almost impossible on conventional MRI sequences. The aim of this study is to reveal the significance of dynamic susceptibility contrast (DSC) perfusion-weighted imaging in differentiating of tumefactive PCNSV (t-PCNSV) lesions from intracranial neoplasms such as glio-blastomas and metastasis. Methods: In this retrospective study, DSC of 8 patients with biopsy-proven t-PCNSV has been compared with DSC obtained in 10 patients with glioblastoma, 10 patients with metastasis, who underwent surgery and histopathological confirmation. The ratio of relative cerebral blood volume (rrCBV) was calculated by rCBV (lesion) / rCBV (controlateral normal-appearing white matter) in the gadolinium-enhancing solid areas. Results: The mean rrCBV was 0.86±0.7 (range: 0.76-0.98) in the patients with t-PCNSV, 5,16±0.79 in patients with glioblastoma (range: 3.9-6.3), and 4.27±0.73 (range: 2.8-5.3) in patients with metastases. Conclusion: DSC-PWI seems to be useful in the diagnostic work-up of t-PCSNVs. A low rrCBV, i.e. a rCBV similar or lower to that of the contralateral normal white matter, seems to be consistent with the possibility of t-PCSNV.

Prognostic value of preoperative diffusion restriction in glioblastoma.

INTRODUCTION: Although glioblastoma (GBM) has a very poor prognosis, overall survival (OS) in treated patients shows great difference varying from few days to several months. Identifying factors explaining this difference would improve management of patient treatment. AIM: To determine the relevance of diffusion restriction in newly diagnosed treatment-naïve GBM patients. METHODS: Preoperative magnetic resonance scans of 33 patients with GBM were reviewed. Regions of interest including all the T2 hyperintense lesion were drawn on diffusion weighted B0 images and transferred to the apparent diffusion coefficient (ADC) map. For each patient, a histogram displaying the ADC values within in the regions of interest was generated. Volumetric parameters including tumor regions with restricted diffusion, parameters derived from histogram and mean ADC value of the tumor were calculated. Their relationship with OS was analyzed. RESULTS: Patients with mean ADC value < 1415x10-6 mm2/s had a significantly shorter OS (p=0.021). Among volumetric parameters, the percentage of volume within T2 lesion with a normalized ADC value <1.5 times that in white matter was significantly associated with OS (p=0.0045). Patients with a percentage>23.92% had a shorter OS. Among parameters derived from histogram, the 50th percentile showed a trend towards significance for OS (p=0.055) with patients living longer when having higher values of 50th percentile. A difference in OS was observed between patients according to ADC peak of histogram but this difference did not reach statistical significance (p=0.0959). CONCLUSION: Diffusion magnetic resonance imaging may provide useful information for predicting GBM prognosis.

Relaxation Along a Fictitious Field, continuous wave T1rho, adiabatic T1rho and adiabatic T2rho imaging of human gliomas at 3T: A feasibility study.

In pre-clinical models of brain gliomas, Relaxation Along a Fictitious Field in second rotating frame (TRAFF2), continues wave T1rho (T1ρcw), adiabatic T1rho (T1ρadiab), and adiabatic T2rho (T2ρadiab) relaxation time mappings have demonstrated potential to non-invasively characterize brain gliomas. Our aim was to evaluate the feasibility and potential of 4 different spin lock methods at 3T to characterize primary brain glioma. 22 patients (26-72 years) with suspected primary glioma. T1ρcw was performed using pulse peak amplitude of 500Hz and pulse train durations of 40 and 80 ms while the corresponding values for T1ρadiab, T2ρadiab, TRAFF2 were 500/500/500Hz and 48 and 96, 64 and 112, 45 and 90 ms, respectively. The parametric maps were calculated using a monoexponential model. Molecular profiles were evaluated from tissue specimens obtained during the resection. The lesion regions-of-interest were segmented from high intensity FLAIR using automatic segmentation with manual refinement. Statistical descriptors from the voxel intensity values inside each lesion and radiomic features (Pyrad MRC package) were calculated. From extracted radiomics, mRMRe R package version 2.1.0 was used to select 3 features in each modality for statistical comparisons. Of the 22 patients, 10 were found to have IDH-mutant gliomas and of those 5 patients had 1p/19q codeletion group comparisons. Following correction for effects of age and gender, at least one statistical descriptor was able to differentiate between IDH and 1p/19q codeletion status for all the parametric maps. In the radiomic analysis, corner-edge detector features with Harris-Stephens filtered signal showed significant group differences in IDH and 1p/19q codeletion groups. Spin lock imaging at 3T of human glioma was feasible and various qualitative parameters derived from the parametric maps were found to have potential to differentiate IDH and 1p19q codeletion status. Future larger prospective clinical trials are warranted to evaluate these methods further.

Evolution of RANO in Assessing Brain Tumour Outcomes.

Assessing treatment response is extremely important in management of brain tumours. Response assessment in neuro-oncology (RANO) was introduced in 2008 for the purpose of making recommendations for it by addressing and countering the limitations in previously reported response criteriae. Subsequently, multiple RANO working groups have been formed to cater to different tumour types and to update their previous recommendations to counter the limitations in their criteria. Herein we have a summarized list of RANO criteria for adult brain tumours.

The Value of Intraoperative Ultrasound in Brain Surgery.

Favorable clinical outcomes in adult and pediatric neurosurgical oncology generally depend on the extent of tumor resection (EOR). Maximum safe resection remains the main aim of surgery in most intracranial tumors. Despite the accuracy of intraoperative magnetic resonance imaging (iMRI) in the detection of residual intraoperatively, it is not widely implemented worldwide owing to enormous cost and technical difficulties. Over the past years, intraoperative ultrasound (IOUS) has imposed itself as a valuable and reliable intraoperative tool guiding neurosurgeons to achieve gross total resection (GTR) of intracranial tumors.Being less expensive, feasible, doesn't need a high level of training, doesn't need a special workspace, and being real time with outstanding temporal and spatial resolution; all the aforementioned advantages give a superiority for IOUS in comparison to iMRI during resection of brain tumors.In this chapter, we spot the light on the technical nuances, advanced techniques, outcomes of resection, pearls, and pitfalls of the use of IOUS during the resection of brain tumors.

Multiple Ring-Enhancing Brain Lesions: Fulminant Diffuse Cerebral Toxoplasmosis or Cerebral Metastases?

ABSTRACT: In this case report, we describe a 76-year-old woman, presenting with dizziness for the past 2 months, without other focal neurological signs. A magnetic resonance imaging of the brain was ordered by her GP. The MRI demonstrated multiple ring-enhancing lesions, both supratentorial and infratentorial. Lumbar puncture showed normal findings, in particular a normal cell count and culture. Because of the radiologic appearance, initially thought to be suggestive of cerebral abscesses, antibiotics were started. However, further workup revealed a new diagnosis of a stage IV (metastatic) small cell lung carcinoma, making diffuse brain metastases more likely. The patient was transferred to oncology/pneumology, where she was started on whole-brain radiotherapy, after which systemic therapy would start. However, because of further clinical deterioration, she was admitted at the palliative ward, where she died only 3 months after the initial presentation. In this case report, we emphasize the importance of keeping a broad differential diagnosis and briefly review the various possible pathologies causing ring-enhancing lesions.

Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas.

BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

Los retos en la evaluación radiológica de las metástasis cerebrales, más allá de la progresión / Challenges in radiological evaluation of brain metastases, beyond progression

La resonancia magnética es la piedra angular en la evaluación de las metástasis cerebrales. Los retos clínicos residen en discriminar las metástasis de imitadores como infecciones o tumores primarios y en evaluar la respuesta al tratamiento. Este, en ocasiones, condiciona un crecimiento, que debe encuadrarse como una pseudoprogresión o una radionecrosis, ambos fenómenos inflamatorios atribuibles al mismo, o bien considerarse como una recurrencia. Para responder a estas necesidades, las técnicas de imagen son objeto de constantes investigaciones. No obstante, un crecimiento exponencial tras la radioterapia debe interpretarse con cautela, incluso ante resultados sospechosos de progresión por técnicas avanzadas, ya que puede tratarse de una radionecrosis. El objetivo de este trabajo es familiarizar al lector con los fenómenos inflamatorios de las metástasis cerebrales tratadas con radioterapia y describir dos signos radiológicos relacionados: la «nube inflamatoria» y el «realce en anillo incompleto», con el fin de adoptar un manejo conservador en estos casos.(AU)

MRI is the cornerstone in the evaluation of brain metastases. The clinical challenges lie in discriminating metastases from mimickers such as infections or primary tumors and in evaluating the response to treatment. The latter sometimes leads to growth, which must be framed as pseudo-progression or radionecrosis, both inflammatory phenomena attributable to treatment, or be considered as recurrence. To meet these needs, imaging techniques are the subject of constant research. However, an exponential growth after radiotherapy must be interpreted with caution, even in the presence of results suspicious of tumor progression by advanced techniques, because it may be due to inflammatory changes. The aim of this paper is to familiarize the reader with inflammatory phenomena of brain metastases treated with radiotherapy and to describe two related radiological signs: «the inflammatory cloud» and «incomplete ring enhancement», in order to adopt a conservative management with close follow-up.(AU)

Survival Analysis of Patients Undergoing Intraoperative Contrast-enhanced Ultrasound in the Surgical Treatment of Malignant Glioma.

OBJECTIVE: Complete resection of malignant gliomas is often challenging. Our previous study indicated that intraoperative contrast-enhanced ultrasound (ICEUS) could aid in the detection of residual tumor remnants and the total removal of brain lesions. This study aimed to investigate the survival rates of patients undergoing resection with or without the use of ICEUS and to assess the impact of ICEUS on the prognosis of patients with malignant glioma. METHODS: A total of 64 patients diagnosed with malignant glioma (WHO grade HI and IV) who underwent surgery between 2012 and 2018 were included. Among them, 29 patients received ICEUS. The effects of ICEUS on overall survival (OS) and progression-free survival (PFS) of patients were evaluated. A quantitative analysis was performed to compare ICEUS parameters between gliomas and the surrounding tissues. RESULTS: The ICEUS group showed better survival rates both in OS and PFS than the control group. The univariate analysis revealed that age, pathology and ICEUS were significant prognostic factors for PFS, with only age being a significant prognostic factor for OS. In multivariate analysis, age and ICEUS were significant prognostic factors for both OS and PFS. The quantitative analysis showed that the intensity and transit time of microbubbles reaching the tumors were significantly different from those of microbubbles reaching the surrounding tissue. CONCLUSION: ICEUS facilitates the identification of residual tumors. Age and ICEUS are prognostic factors for malignant glioma surgery, and use of ICEUS offers a better prognosis for patients with malignant glioma.

BCOR::CREBBP fusion in malignant neuroepithelial tumor of CNS expands the spectrum of methylation class CNS tumor with BCOR/BCOR(L1)-fusion.

Methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" was recently defined based on methylation profiling and tSNE analysis of a series of 21 neuroepithelial tumors with predominant presence of a BCOR fusion and/or characteristic CNV breakpoints at chromosome 22q12.31 and chromosome Xp11.4. Clear diagnostic criteria are still missing for this tumor type, specially that BCOR/BCOR(L1)-fusion is not a consistent finding in these tumors despite being frequent and that none of the Heidelberger classifier versions is able to clearly identify these cases, in particular tumors with alternative fusions other than those involving BCOR, BCORL1, EP300 and CREBBP. In this study, we introduce a BCOR::CREBBP fusion in an adult patient with a right temporomediobasal tumor, for the first time in association with methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" in addition to 35 cases of CNS neuroepithelial tumors with molecular and histopathological characteristics compatible with "CNS tumor with BCOR/BCOR(L1)-fusion" based on a comprehensive literature review and data mining in the repository of 23 published studies on neuroepithelial brain Tumors including 7207 samples of 6761 patients. Based on our index case and the 35 cases found in the literature, we suggest the archetypical histological and molecular features of "CNS tumor with BCOR/BCOR(L1)-fusion". We also present four adult diffuse glioma cases including GBM, IDH-Wildtype and Astrocytoma, IDH-Mutant with CREBBP fusions and describe the necessity of complementary molecular analysis in "CNS tumor with BCOR/BCOR(L1)-alterations for securing a final diagnosis.

Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas.

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.

Minimally invasive keyhole approach for supramaximal frontal glioma resections: technical note.

OBJECTIVE: The authors aimed to review the frontal lobe's surgical anatomy, describe their keyhole frontal lobectomy technique, and analyze the surgical results. METHODS: Patients with newly diagnosed frontal gliomas treated using a keyhole approach with supramaximal resection (SMR) from 2016 to 2022 were retrospectively reviewed. Surgeries were performed on patients asleep and awake. A human donor head was dissected to demonstrate the surgical anatomy. Kaplan-Meier curves were used for survival analysis. RESULTS: Of the 790 craniotomies performed during the study period, those in 47 patients met our inclusion criteria. The minimally invasive approach involved four steps: 1) debulking the frontal pole; 2) subpial dissection identifying the sphenoid ridge, olfactory nerve, and optic nerve; 3) medial dissection to expose the falx cerebri and interhemispheric structures; and 4) posterior dissection guided by motor mapping, avoiding crossing the inferior plane defined by the corpus callosum. A fifth step could be added for nondominant lesions by resecting the inferior frontal gyrus. Perioperative complications were recorded in 5 cases (10.6%). The average hospital length of stay was 3.3 days. High-grade gliomas had a median progression-free survival of 14.8 months and overall survival of 23.9 months. CONCLUSIONS: Keyhole approaches enabled successful SMR of frontal gliomas without added risks. Robust anatomical knowledge and meticulous surgical technique are paramount for obtaining successful resections.