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The extent of preoperative peritumoral edema in glioblastoma (GBM) has been negatively correlated with patient outcome. As several ongoing studies are investigating T-cell based immunotherapy in GBM, we conducted this study to assess whether peritumoral edema with potentially increased intracranial pressure, disrupted tissue homeostasis and reduced local blood flow has influence on immune infiltration and affects survival. A volumetric analysis of preoperative imaging (gadolinium enhanced T1 weighted MRI sequences for tumor size and T2 weighted sequences for extent of edema (including the infiltrative zone, gliosis etc.) was conducted in 144 patients using the Brainlab® software. Immunohistochemical staining was analyzed for lymphocytic- (CD 3+) and myelocytic (CD15+) tumor infiltration. A retrospective analysis of patient-, surgical-, and molecular characteristics was performed using medical records. The edema to tumor ratio was neither associated with progression-free nor overall survival (p=0.90, p=0.74). However, GBM patients displaying IDH-1 wildtype had significantly higher edema to tumor ratio than patients displaying an IDH-1 mutation (p=0.01). Immunohistopathological analysis did not show significant differences in lymphocytic or myelocytic tumor infiltration (p=0.78, p=0.74) between these groups. In our cohort, edema to tumor ratio had no significant correlation with immune infiltration and outcome. However, patients with an IDH-1wildtype GBM had a significantly higher edema to tumor ratio compared to their IDH-1 mutated peer group. Further studies are necessary to elucidate the underlying mechanisms.

Purpose To determine whether there is a correlation between the presence of peritumoral edema detected on T2-weighted sequences and pathological prognostic factors and specific biological subtypes of breast cancer. Methods A retrospective study on 120 women with invasive breast cancer, who underwent breast MRI on a 3 T scanner over a period of 12 months, was performed. Peritumoral edema was assessed visually by two breast radiologist in consensus and patients were consequently divided into two groups. For each lesion dimensions, histology and molecular profiling were evaluated. The comparison between the two groups was performed using the χ2 test, subsequently carrying out a univariate and a multivariate logistic regression analysis. Results 41 (34.2%) invasive breast cancers with perilesional edema and 79 (65.8%) without edema were identified. A significant association between the presence of perilesional edema and a larger lesion size ( p  = 0.0001), grade 3 ( p  < 0.001) and a higher Ki-67 index ( p  < 0.001) was found. The multivariate analysis confirmed that a larger tumor size is independently associated with peritumoral edema. A total of 101 (84.2%) luminal-like, 8 (6.7%) HER2-positive, and 11 (9.2%) triple-negative tumors were included in the study. Peritumoral edema turned out to be less frequently associated with luminal-like lesions ( p  < 0.001), while the luminal A status was found to have a significant association with the absence of peritumoral edema in the univariate analysis. Conclusion The detection of peritumoral edema on T2-weighted sequences should be considered as a valid additional prognostic tool in the evaluation of breast cancer, since it is associated with biologically aggressive non-luminal breast cancers, characterized by large dimension, high tumor grade, and high Ki-67 values.

This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. A retrospective analysis was performed on 300 patients who received BEV treatment from September 2013 to January 2024. The dataset incorporated 13 predictive features: 8 clinical variables and 5 radiological variables. The dataset was divided into a training set (70%) and a test set (30%) using stratified sampling. Data preprocessing was carried out through methods such as handling missing values with the MICE method, detecting and adjusting outliers, and feature scaling. Four algorithms, namely Random Forest (RF), Logistic Regression, Gradient Boosting Tree, and Naive Bayes, were selected to construct binary classification models. A tenfold cross-validation strategy was implemented during training, and techniques like regularization, hyperparameter optimization, and oversampling were used to mitigate overfitting. The RF model demonstrated superior performance, achieving an accuracy of 0.89, a precision of 0.94, F1-score of 0.92, with both AUC-ROC and AUC-PR values reaching 0.91. Feature importance analysis consistently identified edema volume as the most significant predictor, followed by edema index, patient age, and tumor volume. Traditional multivariate logistic regression corroborated these findings, confirming that edema volume and edema index were independent predictors ( p  < 0.01). Our results highlight the potential of ML—driven predictive models in optimizing BEV treatment selection, reducing unnecessary treatment risks, and improving clinical decision—making in neuro—oncology.

Background The optimal treatment volume for Glioblastoma multiforme (GBM) is still a subject of debate worldwide. The current study was aimed to determine the distances between recurring tumors and the edge of primary lesions, and thereby provide evidence for accurate target area delineation. Methods Between October 2007 and March 2019, 68 recurrent patients with GBM were included in our study. We measured the distance from the initial tumor to the recurrent lesion of GBM patients by expanding the initial gross tumor volume (GTV) to overlap the center of recurrent lesion, with the help of the Pinnacle Treatment Planning System. Results Recurrences were local in 47(69.1%) patients, distant in 12(17.7%) patients, and both in 9(13.2%) patients. Factors significantly influencing local recurrence were age ( P  = 0.049), sex ( P  = 0.049), and the size of peritumoral edema ( P  = 0.00). A total number of 91 recurrent tumors were analyzed. All local recurrences occurred within 2 cm and 94.8% (55/58) occurred within 1 cm of the original GTV based on T1 enhanced imaging. All local recurrences occurred within 1.5 cm and 98.3%(57/58) occurred within 0.5 cm of the original GTV based on T2-FLAIR imaging. 90.9% (30/33) and 81.8% (27/33) distant recurrences occurred >3 cm of T1 enhanced and T2-Flair primary tumor margins, respectively. Conclusions The 1 cm margin from T1 enhanced lesions and 0.5 cm margin from T2-Flair abnormal lesions could cover 94.8 and 98.3% local recurrences respectively, which deserves further prospective study as a limited but effective target area.

Purpose Glioblastoma (GBM) is an aggressive brain tumor often accompanied by a vasogenic peritumoral edema, which contributes to symptoms both at diagnosis and during later stages of the disease. Previous studies have suggested effectiveness of the endogenous protein, Antisecretory Factor (AF), in reducing the intracranial pressure in cytotoxic brain edema after trauma. Interestingly, AF also seems to carry antineoplastic effects in experimental GBM models. This study investigated whether AF reduces peritumoral edema in GBM patients. As a secondary aim, we assessed potential effects on tumor progression by AF. Methods Fifteen newly diagnosed GBM patients were treated for 7 days preoperatively with AF in addition to standard of care (SOC) treatment with corticosteroids. The change in edema volume was assessed volumetrically using T2/FLAIR weighted MRI and compared to a control group of 10 GBM patients receiving SOC only. Results At baseline the mean tumor volume for the entire cohort was 35.7 cm3 with a mean edema of 62.2 cm3. There was no significant difference in edema volume change between the AF treated patients, who demonstrated a mean edema reduction of 7.1cm3 (95%CI -5.4–19.6), and the controls, 11.3cm3 (95%CI -0.8–23.5), p  = 0.61. No difference was observed in tumor volume change between the two groups, p  = 0.79. No adverse treatment effects were noted. Conclusion Treatment with AF in addition to SOC does not seem to reduce the peritumoral edema in GBM patients. The treatment was well tolerated. The lack of edema-reducing effect may be related to the different pathophysiological properties of vasogenic and cytotoxic edema.

Resection of large anterior midline skull base meningiomas with extensive peritumoral edema poses high risks due to postoperative edema decompensation leading to increased intracranial pressure. Initial craniectomy prevents intracranial pressure decompensation but requires secondary cranioplasty. This study compares single-stage osteoplastic craniotomy with tumor resection to a two-stage approach using bifrontal craniectomy, tumor resection and subsequent cranioplasty after edema recovery in a second surgical step. Patients with large anterior midline skull base meningiomas (> 50 mm) and extensive peritumoral edema were included. Group 1 underwent single-stage resection (2002–2016), while Group 2 had a two-stage approach (2012–2022). The primary outcome was the Karnofsky Performance Scale (KPS) at three months post-surgery. Secondary outcomes included preoperative KPS, KPS at discharge and last follow-up, ICU stay, hospital stay length and complication rates. A total of 25 patients were analyzed (Group 1: n  = 9; Group 2: n  = 16). Group 2 demonstrated significantly improved KPS at three months postoperatively (median KPS 70% vs. 50%; p  = 0.0204) with a non-significant reduction in ICU stay (10 vs. 6.5 days; p  = 0.3284). Although no significant differences were observed in KPS at discharge (Group 1: KPS 30% vs. Group 2: KPS 50%; p  = 0.1829) or last follow-up (Group 1: KPS 60% vs. Group 2: KPS 80%; p  = 0.1630), Group 2 patients required fewer postoperative interventions for complications unrelated to cranioplasty. Overall complication rates were comparable in both groups (Group 1: 67% vs. Group 2: 56%; p  = 0.6274). Two-stage resection of large anterior midline skull base meningiomas with extensive edema provides superior clinical outcomes at three months postoperatively without increasing overall complication rates. These findings support the use of a two-stage surgical strategy for highly selected patients. However, further multicenter studies are warranted to validate these results in larger cohorts.

Abstract BACKGROUND Gamma knife radiosurgery (GKRS) is recognized as an important treatment modality for meningioma. OBJECTIVE To analyze the long-term outcomes in meningioma patients treated with GKRS to determine the risk factors related to treatment failure and peritumoral edema (PTE) development. METHODS Between 1998 and 2010, 770 consecutive patients were treated with GKRS for intracranial meningioma. After the exclusion of patients with follow-up periods of less than 5 yr and those with neurofibromatosis, multiple meningiomas, nonbenign meningioma, or radiotherapy, a total of 424 patients were enrolled in this study. The median follow-up duration was 92 mo. The median tumor volume was 4.35 cm3, and the median marginal dose was 14 Gy. RESULTS The overall local tumor control rate was 84%. The actuarial tumor control rates were 91.7% and 78.9% at 5 and 10 yr, respectively. The tumor control rate of a radiologically diagnosed tumor was higher than that of a grade I tumor (82% vs 70.1% at 10 yr, P = .001). In multivariate analysis, factors associated with tumor progression were female sex (hazard ratio: 0.5, P = .025) and a previous history of craniotomy (hazard ratio: 1.9, P = .009). Symptomatic PTE was identified in 36 (8.5%) patients, and the factor associated with poor PTE was the presence of PTE before GKRS (odds ratio: 4.6, P < .001). Permanent complication rate was 4%. CONCLUSION GKRS appears to be an effective treatment modality for meningioma with long-term follow-up. However, the identification of delayed tumor progression in our study suggests that extended follow-up data should be collected after GKRS.

Although the free water content within the perilesional T2 hyperintense region should differ between glioblastomas (GBM) and brain metastases based on histological differences, the application of classical MR diffusion models has led to inconsistent results regarding the differentiation between these two entities. Whereas diffusion tensor imaging (DTI) considers the voxel as a single compartment, multicompartment approaches such as neurite orientation dispersion and density imaging (NODDI) or the recently introduced diffusion microstructure imaging (DMI) allow for the calculation of the relative proportions of intra- and extra-axonal and also free water compartments in brain tissue. We investigate the potential of water-sensitive DTI, NODDI and DMI metrics to detect differences in free water content of the perilesional T2 hyperintense area between histopathologically confirmed GBM and brain metastases. Respective diffusion metrics most susceptible to alterations in the free water content (MD, V-ISO, V-CSF) were extracted from T2 hyperintense perilesional areas, normalized and compared in 24 patients with GBM and 25 with brain metastases. DTI MD was significantly increased in metastases (p = 0.006) compared to GBM, which was corroborated by an increased DMI V-CSF (p = 0.001), while the NODDI-derived ISO-VF showed only trend level increase in metastases not reaching significance (p = 0.060). In conclusion, diffusion MRI metrics are able to detect subtle differences in the free water content of perilesional T2 hyperintense areas in GBM and metastases, whereas DMI seems to be superior to DTI and NODDI.

Background and purpose Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics. Methods Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis. Results ADC min , ADC mean , rADC min , rADC mean and Ki-67 LI showed a negative correlation ( r  = − 0.478, r  = − 0.369, r  = − 0.488, r  = − 0.388, all P  < 0.001). The Ki-67 LI of low-grade gliomas (LGGs) was different from that of high-grade gliomas (HGGs), and the cut-off point of Ki-67 LI for distinguishing LGGs from HGGs was 9.5%, with an area under the ROC curve (AUROC) of 0.962 (95%CI 0.933–0.990). The ADC parameters in the high Ki-67 group were significantly lower than those in the low Ki-67 group (all P  < 0.05). The peritumoral edema (PTE) of gliomas in the high Ki-67 LI group was higher than that in the low Ki-67 LI group ( P  < 0.05). The AUROC of Ki-67 LI level assessed by the multivariate logistic regression model was 0.800 (95%CI 0.721–0.879). Conclusions There was a negative correlation between ADC parameters and Ki-67 LI, and the multivariate logistic regression model combined with peritumoral edema and ADC parameters could improve the prediction ability of Ki-67 LI.

Meningiomas is a tumor of the meninges and is among the most common intracranial neoplasms in adults, accounting for over a third of all primary brain tumors in the United States. Meningiomas can be associated with peritumoral brain edema (PTBE) which if not managed appropriately can lead to poor clinical outcomes. In this review, we summarize the relevant pathophysiology, predictors, and principles for treatment of PTBE. The results of various case-reports and case-series have found that meningioma-associated PTBE have patterns in age, tumor size, and hormone receptor positivity. Our study describes how increased age, increased tumor size, tumor location in the middle fossa, and positive expression of hormone receptors, VEGF, and MMP-9 can all be predictors for worse clinical outcomes. We also characterize treatment options for PTBE such as glucocorticoids and VEGF inhibitors along with the ongoing clinical trials attempting to alleviate PTBE in meningioma cases. The trends summarized in this review can be used to better predict the behavior of meningioma-associated PTBE and establish prognosis models to identify at risk patients.