Solitary Brain Tumor Research Articles

Differentiation of glioblastoma, brain metastases and central nervous system lymphomas using amount of vasogenic edema and diffusion MR imaging of tumor core and peritumoral zone- Searching for a practical approach

Background/Aim: The differential diagnosis of solitary brain tumors poses challenges for clinicians and radiologists, often leading to invasive biopsy procedures. Therefore, this study aimed to evaluate the variations in edema volume and diffusion characteristics between the tumor core and peritumoral zone in cases of glioblastoma, brain metastasis, and central nervous system lymphoma. The aim was to identify additional parameters for conventional magnetic resonance imaging (MRI) that could aid in the differential diagnosis. Methods: A total of 39 patients (13 with central nervous system lymphoma, 13 with glioblastoma, and 13 with brain metastases) were included in this retrospective cohort study. Apparent diffusion coefficient (ADC) values were calculated from the ADC maps obtained from Brain MRI for both the lesion and peritumoral region. Additionally, the largest diameter of the vasogenic edema-mass complex was measured using T2 sequences. In the contrast-enhanced series, the largest diameter of the metastatic lesion was measured. The edema-mass ratio was determined by dividing the diameter of the edema-mass complex by the diameter of the mass. Results: There was a statistically significant difference in the edema-mass ratio among the tumor types (P=0.008). Further analysis using Bonferroni correction revealed that this difference was primarily due to glioblastoma. Compared to patients with lymphoma and brain metastases, lesions diagnosed as glioblastoma exhibited a lower edema-mass ratio. Additionally, a statistically significant difference was observed in the ADC value measured from the lesion according to the tumor type (P=0.017). It was determined that lesions associated with central nervous system lymphoma had lower ADC values than those with glioblastoma. Conclusion: Including lesional and perilesional ADC values obtained through diffusion-weighted examination and edema mass ratio measurements may enhance the accuracy of differential diagnosis. Utilizing these imaging characteristics in a multiparametric approach, as suggested by this research, can improve the accuracy of diagnosing malignant cancers, thereby enabling better patient management and treatment decisions.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features.

Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors.

A volume matched comparison of survival after radiosurgery in non-small cell lung cancer patients with one versus more than twenty brain metastases.

Treatment of patients with a large number of brain metastases using radiosurgery remains controversial. In this study we sought to conduct a volume matched comparison to evaluate the clinical outcome of patients with > 20 brain metastases and compared it with patents with solitary brain tumor form non-small cell lung cancer (NSCLC). Between 2014 and 2020, 26 NSCLC patients (925 tumors) underwent stereotactic radiosurgery (SRS) for > 20 metastases in a single procedure (median margin dose = 16Gy, median cumulative tumor volume = 4.52cc); 56 patients underwent SRS for a single metastasis (median margin dose = 18Gy, median volume = 4.74cc). The overall survival (OS), local tumor control (LC), adverse radiation effect (ARE) risk, and incidence of new tumor development were compared. No difference in OS was found between patients with > 20 brain metastases (median OS = 15months) and patients with solitary metastasis (median OS = 12months; p = 0.3). In the solitary tumor cohort, two of 56 (3.5%) tumors progressed whereas in the > 20 cohort only 3 of 925 (0.3%) tumors showed progression (*p = 0.0013). The rate of new tumor development was significantly higher in patients with > 20 tumors (***p = 0.0001). No significant difference of ARE rate was found (7.5% for > 20 tumors vs. 8.7% for single metastasis). Patients with > 20 tumors showed significantly better LCwithsimilar OS compared to patients with solitary tumors. Current guidelines that restrict the role of SRS to patients with 1-4 tumors should be revised.

CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone.

Due to their similar imaging features, high-grade gliomas (HGGs) and solitary brain metastases (BMs) can be easily misclassified. The peritumoral zone (PZ) of HGGs develops neoplastic cell infiltration, while in BMs the PZ contains pure vasogenic edema. As the two PZs cannot be differentiated macroscopically, this study investigated whether computed tomography (CT)-based texture analysis (TA) of the PZ can reflect the histological difference between the two entities. Thirty-six patients with solitary brain tumors (HGGs, n = 17; BMs, n = 19) that underwent CT examinations were retrospectively included in this pilot study. TA of the PZ was analyzed using dedicated software (MaZda version 5). Univariate, multivariate, and receiver operating characteristics analyses were used to identify the best-suited parameters for distinguishing between the two groups. Seven texture parameters were able to differentiate between HGGs and BMs with variable sensitivity (56.67–96.67%) and specificity (69.23–100%) rates. Their combined ability successfully identified HGGs with 77.9–99.2% sensitivity and 75.3–100% specificity. In conclusion, the CT-based TA can be a useful tool for differentiating between primary and secondary malignancies. The TA features indicate a more heterogenous content of the HGGs’ PZ, possibly due to the local infiltration of neoplastic cells.

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases.

BackgroundThe effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.MethodsOne hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC).ResultsThrough the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57–0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness.ConclusionsWe developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.

SURG-01. Management of solitary brain metastasis less than 4 cm in diameter. Surgical resection versus stereotactic radiotherapy: a meta-analysis

IntroductionTo treat a solitary metastasis in the brain, surgical resection and/or radiotherapy are the standard treatments of care. However, the clinical scenarios in which to use these techniques alone or in combination are controversial. While a course of stereotactic radiotherapy is often administered to a patient who presents with multiple metastases, surgical resection is often directed against a larger solitary brain metastasis before irradiating the resection bed. The management of a smaller solitary tumor (diameter less than 4 cm) is less clear. Accordingly, our meta-analysis assembled studies that focused on patients with a solitary tumor less than 4 cm in diameter.MethodsFollowing PRISMA guidelines (PROSPERO ID: CRD42021242434), we searched PubMed, Web of Knowledge, and Cochrane Library databases for randomized controlled trials (RCT) and observational studies comparing surgery to radiotherapy for solitary metastatic brain tumors less than 4 cm in diameter. From 498 total records, we included 9 studies for meta-analysis. Analysis was performed on R.Results2 RCTs and 7 observational studies were identified. 431 patients underwent surgical intervention, and 349 patients exclusively underwent radiotherapy. The surgical treatment cohort did not exhibit a difference in 1-year (OR [95% CI] = 0.866 [0.609–1.289]), 2-year (1.7 [0.843–3.428]), or overall survival (1.18 [0.598–2.327]). However, the surgical treatment group demonstrated greater local tumor recurrence after 1-year (3.975 [1.979–7.987]) and overall local recurrence (3.045 [1.276 - 7.268]). There was no difference between the overall rates of distant recurrence (0.565 [0.218 - 1.466]).ConclusionsOur analysis opens more discussion about the management of solitary brain metastasis. Patient selection is paramount in achieving better local control. Stereotactic radiotherapy should be considered for treatment of solitary brain metastasis less than 4 cm in diameter in selected patients. Future randomized control trials for small solitary masses are recommended.

A radiomics-based model to differentiate glioblastoma from solitary brain metastases

To differentiate glioblastoma (GBM) from solitary brain metastases (MET) using radiomic analysis. Two hundred and fifty-three patients with solitary brain tumours (157 GBM and 98 solitary brain MET) were split into a training cohort (n=178) and a validation cohort (n=77) by stratified sampling using computer-generated random numbers at a ratio of 7:3. After feature extraction, minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to build the radiomics signature on the training cohort and validation cohort. Performance was assessed by radiomics score (Rad-score), receiver operating characteristic (ROC) curve, calibration, and clinical usefulness. Eleven radiomic features were selected as significant features in the training cohort. The Rad-score was significantly associated with the differentiation between GBM and solitary brain MET (p<0.001) both in the training and validation cohorts. The radiomics signature yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and validation cohorts to distinguish between GBM and solitary brain MET. The radiomics model might be a useful supporting tool for the preoperative differentiation of GBM from solitary brain MET, which could aid pretreatment decision-making.

Prediktivna dijagnostika i/ili prognostički biomarkeri dobijeni iz rutinskih biohemijskih analiza krvi u pacijenata sa solitarnim intrakranijalnim tumorom

BackgroundRadiological and/or laboratory tests may be sometimes inadequate distinguishing glioblastoma from metastatic brain tumors. The aim of this study was to find possible predictive biomarkers produced from routine blood biochemistry analysis results evaluated preoperatively in each patient with solitary brain tumor in distinguishing glioblastoma from metastatic brain tumors as well as revealing short-term prognosis.MethodsPatients admitted to neurosurgery clinic between January 2015 and September 2018 were included in this study and they were divided into GLIOMA (n=12) and METASTASIS (n=17) groups. Patients' data consisted of age, gender, Glasgow Coma Scale scores, duration of stay in hospital, Glasgow Outcome Scale (GOS) scores and histopathological examination reports, hemoglobin level, leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, basophil and platelet count results, neutrophil-lymphocyte ratio and platelet-lymphocyte ratio values, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) levels were evaluated preoperatively.ResultsThe CRP levels of METASTASIS group (143.10 mg/L) were higher than those of GLIOMA group (23.90 mg/L); and it was 82% sensitive and 75% specific in distinguishing metastatic brain tumor from glioblastoma if CRP value was >55.00 mg/L. A positive correlation was determined between GOS score and hemoglobin level and between ESR and CRP values. However, GOS scores were negatively correlated with the ESR level and duration of stay in hospital.ConclusionsStudy results demonstrated that CRP values could be predictive biomarker in distinguishing metastatic brain tumor from glioblastoma. In addition, ESR, CRP, hemoglobin levels and duration of stay in hospital could be prognostic biomarkers in predicting short-term prognosis of patients with solitary brain tumor.

Anesthesia for Awake Craniotomy in Patients with Metastatic Brain Tumor in Ibadan, Nigeria

Introduction: Craniotomies can be performed under general anaesthesia or with the patients awake known as awake craniotomy. Awake craniotomy requires that the patients be conscious and cooperative during intraoperative neurological testing. Methods: This was a retrospective study of the anaesthetic protocol and the complications encountered during the perioperative management of patients who had awake craniotomy for metastatic brain resection in a developing country over a 3- year period. The information retrieved were demographic data, tumor location, anaesthetic technique and perioperative complications. Results: There were ten patients comprising of 9 (90%) females and 1 (10%) male. The age ranged from 33 to 66 years with a mean age of 44.00 ± 12.02 years. Eight patients had metastatic brain tumor from the breast carcinoma, one from the lung and the last patient had metastasis from the colon. Four patients had tumor excision from the frontal lobe while 6 patients had excision from the parietal lobe. They all had solitary brain tumor. The anaesthetic technique used was conscious sedation and scalp block. Propofol and fentanyl were used for the conscious sedation while 0.25% Plain Bupivacaine and 1% Lidocaine with adrenaline (1: 200,00) were used for the scalp block. Patients were allowed to breathe spontaneously 100% oxygen. Two patients had respiratory depression with oxygen saturation less than 95% and laryngeal mask airway was inserted. Hypertension and tachycardia were seen in 4 patients, focal seizure in 2 patients and aphasia in 1 patient. Intensive care unit admission was for 24 hours and the today length of hospital stay was 2-7 days. No mortality in this series. Conclusion: Conscious sedation with scalp block is a safe and a tolerable technique for awake craniotomy. The complications observed were minimal and can be treated to obtain a good outcome.

Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone.

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

MET-04 EVALUATION OF PERITUMORAL BRAIN PARENCHYMA USING CONTRAST-ENHANCED FIESTA IMAGING FOR DIFFERENTIATING METASTATIC BRAIN TUMORS AND GLIOBLASTOMAS

Introduction: Metastatic brain tumors and glioblastomas commonly revealed heterogenous enhancement lesions with peritumoral brain edema on magnetic resonance imaging (MRI). In particular, distinguishing solitary metastatic brain tumors from glioblastomas is difficult on conventional MRI. Fast imaging employing steady-state acquisition (FIESTA) can emphasize the water content signal with a high spatial resolution. In this study, we evaluate a role of contrast-enhanced FIESTA(CE-FIESTA) by focusing on the peritumoral brain parenchyma between metastatic brain tumors and glioblastomas. Materials and Methods: We included patients who underwent initial surgery and were histologically diagnosed with metastatic brain tumor (43 cases) or glioblastoma (14 cases) between November 2008 and May 2016. We evaluated CE-FIESTA findings of peritumoral brain parenchyma. Next, we performed an observer performance study with neuroradiologists based on the findings of peritumoral brain parenchyma using conventional MRI and CE-FIESTA. Results: CE-FIESTA revealed hyperintense rim in peritumoral brain parenchyma. We classified hyperintense rim in three groups, as follow: type A, no hyperintense rim; type B, partial hyperintense rim; and type C, extended hyperintense rim. Regarding the diagnosis of metastatic brain tumors, the observer performance demonstrated high sensitivity (95.3%), specificity (85.7%) and accuracy (93.0%) of type C on CE-FIESTA, and thus, CE-FIESTA could distinguish metastatic brain tumors from glioblastomas with high accuracy. Conclusions: CE-FIESTA may provide useful information for distinguish metastatic brain tumors from glioblastomas, focusing on the differences in the peritumoral brain parenchyma.

The Utility of Whole Body Imaging in the Evaluation of Solitary Brain Tumors

Solitary brain tumors can propose a diagnostic dilemma owing to the difficulty in differentiating between primary brain tumors and metastatic disease. The similar radiologic appearance on routine magnetic resonance imaging will necessitate the need for additional noninvasive testing. We sought to determine the clinical utility of preoperative whole body screening with computed tomography (CT) to detect metastatic disease in patients with solitary brain tumors. A prospectively maintained surgical database for a large quaternary care academic institution was retrospectively reviewed for all patients undergoing craniotomy for a new diagnosis of enhancing solitary brain lesion from January 2011 to January 2016. Patients were excluded if the imaging findings had demonstrated multiple brain tumors, they had a known diagnosis of malignancy, or they had undergone previous craniotomy. The demographic and radiographic information and clinical and histopathologic data were collected and tallied. A total of 218 patients with solitary brain tumors met the inclusion criteria and were included in the present study. Histopathologic analysis confirmed primary central nervous system tumors in 152 patients (74.4%) and metastatic disease in 66 (25.6%). Preoperative screening with whole body CT had a sensitivity of 0.92 and specificity of 0.95 for detecting systemic metastases in the patients. Preoperative whole body CT correctly identified systemic malignancy in 88% of the patients ultimately diagnosed with metastasis (positive predictive value, 88%). Of those with negative whole body imaging findings, 97% had a diagnosis of a primary central nervous system neoplasm (negative predictive value, 97%). Preoperative whole body CT had a positive predictive value of 88% and negative predictive value of 97% in the present study and was both sensitive (92%) and specific (95%) for the detection of extracranial tumors. The identification of extracranial tumors on whole body CT screening might alter management.

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 solitary brain MET) were divided into training (n = 227) and test (n = 185) cohorts. Radiomic features extraction was performed with PyRadiomics software. In the training cohort, twelve feature selection methods and seven classification methods were evaluated to construct favorable radiomic machine-learning classifiers. The performance of the classifiers was evaluated using the mean area under the curve (AUC) and relative standard deviation in percentile (RSD). In the training cohort, thirteen classifiers had favorable predictive performances (AUC≥0.95 and RSD ≤6). In the test cohort, receiver operating characteristic (ROC) curve analysis revealed that support vector machines (SVM)+least absolute shrinkage and selection operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy. Furthermore, the clinical performance of the best classifier was superior to neuroradiologists in accuracy, sensitivity, and specificity. In conclusion, employing radiomic machine-learning technology could help neuroradiologist in differentiating GBM from solitary brain MET preoperatively.

Differentiation of Glioblastoma and Solitary Brain Metastasis by Gradient of Relative Cerebral Blood Volume in the Peritumoral Brain Zone Derived from Dynamic Susceptibility Contrast Perfusion Magnetic Resonance Imaging.

The purpose of our study was to evaluate the efficacy of the relative cerebral blood volume (rCBV) gradient in the peritumoral brain zone (PBZ)-the difference in the rCBV values from the area closest to the enhancing lesion to the area closest to the healthy white matter-in differentiating glioblastoma (GB) from solitary brain metastasis (MET). A 3.0-T magnetic resonance imaging (MRI) machine was used to perform dynamic susceptibility contrast perfusion MRI (DSC-MRI) on 43 patients with a solitary brain tumor (24 GB, 19 MET). The rCBV ratios were acquired by DSC-MRI data in 3 regions of the PBZ (near the enhancing tumor, G1; intermediate distance from the enhancing tumor, G2; far from the enhancing tumor, G3). The maximum rCBV ratios in the PBZ (rCBVp) and the enhancing tumor were also calculated, respectively. The perfusion parameters were evaluated using the nonparametric Mann-Whitney test. The sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve were identified. The rCBVp ratios and rCBV gradient in the PBZ were significantly higher in GB compared with MET (P < 0.05 for both rCBVp ratios and rCBV gradient). The threshold values of 0.50 or greater for rCBVp ratios provide sensitivity and specificity of 57.69% and 79.17%, respectively, for differentiation of GB from MET. Compared with rCBVp ratios, rCBV gradient had higher sensitivity (94.44%) and specificity (91.67%) using the threshold value of greater than 0.06. The parameter of rCBV gradient derived from DSC-MRI in the PBZ seems to be the most efficient parameter to differentiate GB from METs.

Conventional MRI texture analysis of peritumoral edema in the differential diagnosis of glioblastoma and solitary metastatic brain tumor

Objective To investigate the value of conventional MR texture analysis of peritumoral edema in differentiating between glioblastoma and solitary metastatic brain tumor. Methods Forty patients with brain glioma and forty patients with brain metastases confirmed by pathology or clinic were enrolled. All patients underwent conventional MRI scan and enhanced examination, including axial T1WI, T2WI, fluid attenuated inversion recovery(FLAIR) and enhanced T1WI. The ROI was manually outlined by MaZda software in the most obvious level of 4 sequences of peritumoral edema and the texture features were extracted, including mean, variance, skewness, kurtosis, 1 percentile, 10 percentile, 50 percentile, 90 percentile, 99 percentile. Independent sample t-test (in case of normality and variance homogeneity test) or rank sum test (in case of not satisfying normality and homogeneity of variance) was used to compare the differences of the histogram parameters and to make meaningful parameters based on logistic regression modeling and drawing the receiver operating characteristic curve (ROC curve) to evaluate the differential diagnosis efficacy. Results Through the preliminary feature screening, skew of FLAIR (SkewFLAIR) for glioblastomas was -0.358±0.432, respectively,while for solitary metastatic brain tumor, the statistics was -0.648±0.503. The results showed statistically significant difference(t=7.574, P=0.006). The differences between the kurtosis (KurtFLAIR) were also proven to be statistically significant (Z=4.360, P=0.037),with -0.384 (-0.798, 0.049) for glioblastomas and -0.089 (-0.456, 0.613) for solitary metastasis brain tumor. The sensitivity of the corresponding logistic regression differential diagnosis model was 77.5%, specificity was 94.3%, and area under the curve (AUC) was 0.85. Conclusion Conventional MRI texture analysis of peritumoral edema provides reliable and quantified objective basis for the differential diagnosis of glioblastoma and solitary metastasis. Key words: Glioblastoma; Solitary metastasis; Peritumor edema; Magnetic resonance imaging; Texture analysis

Evaluation of Peritumoral Brain Parenchyma Using Contrast-Enhanced 3D Fast Imaging Employing Steady-State Acquisition at 3T for Differentiating Metastatic Brain Tumors and Glioblastomas

Metastatic brain tumors and glioblastomas are the 2 of the most common brain neoplasms in adults. However, distinguishing solitary metastatic brain tumors from glioblastomas on conventional magnetic resonance imaging remains particularly challenging. Thus, we aimed to retrospectively assess the role of contrast-enhanced fast imaging employing steady-state acquisition (CE-FIESTA) imaging in distinguishing between metastatic brain tumors and glioblastomas. Forty-three patients with metastatic brain tumors and 14 patients with glioblastomas underwent conventional magnetic resonance imaging and CE-FIESTA before surgery. First, 1 neuroradiologist and 1 neurosurgeon classified the CE-FIESTA findings for the peritumoral brain parenchyma by consensus. Next, the 2 neuroradiologists performed an observer performance study comparing tumor shape classification (smooth or irregular margins), a classic imaging finding, with the CE-FIESTA classification of the peritumoral brain parenchyma. The CE-FIESTA findings for the peritumoral brain parenchyma were classified as follows: type A, no hyperintense rim; type B, partial hyperintense rim; and type C, extended hyperintense rim. With regard to the diagnosis of metastatic brain tumors, the observer performance study demonstrated that the mean sensitivity, specificity, and accuracy of an extended hyperintense rim classification (type C) on CE-FIESTA images were 95.3%, 85.7%, and 93.0%, respectively. The accuracy of the CE-FIESTA classification was significantly higher than that of the tumor shape classification. CE-FIESTA images may provide useful information for distinguishing metastatic brain tumors from glioblastomas, especially when focusing on differences in the peritumoral brain parenchyma.

Differentiation of Glioblastomas From Solitary Brain Metastases Using Radiomic Machine-Learning Classifiers

Background: This study identifies optimal radiomic machine-learning classifiers to differentiate glioblastomas (GBM) from solitary brain metastases (MET), the most common neoplasms in adults, preoperatively. Methods: Four hundred and twelve patients with solitary brain tumors (242 with GBM and 170 with solitary brain MET) were divided into training (n =227) and validation (n =185) sets. Extraction of radiomic features from preoperative magnetic resonance images of each patient was accomplished with PyRadiomics software. Twelve feature selection methods and seven classification methods were evaluated to construct optimal radiomic machine-learning classifiers in the training set that were subsequently evaluated in a validation set. The role of the classifiers in differential diagnosis was evaluated using the mean area under the curve (AUC) and relative standard deviation in percentile (RSD). Findings:In the training set, thirteen classifiers had favorable predictive performances (AUC≥0.95 and RSD ≤6). In the validation set, receiver operating characteristic (ROC) curve analysis revealed that support vector machines (SVM) least absolute shrinkage and selection operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy, followed by Adaboost (ADa) LASSO (AUC, 0.89), Multi-Layer Perceptron (MLP) LASSO (AUC, 0.87), and random forest (RF) LASSO (AUC, 0.87). Furthermore, the clinical performance of these radiomic machine-learning classifiers were superior to neuroradiologists in accuracy, sensitivity, and specificity. Interpretation: By employing radiomic machine-learning technology, optimal machine-learning classifiers were identified for differentiating GBM from solitary brain MET preoperatively, which could significantly augment therapeutic strategies. Funding Statement: We acknowledge financial support from the National Natural Science Foundation of China (No. 81601452), and Key laboratory of functional and clinical translational medicine, Fujian province university(JNYLC1808) Declaration of Interests: The author declares no competing interest. Ethics Approval Statement: This study was approved by the ethics committee of Beijing Tiantan Hospital.

Remote effects in the ipsilateral thalamus and/or contralateral cerebellar hemisphere using FDG PET in patients with brain tumors.

To evaluate reduced metabolism in the ipsilateral thalamus (TH) and/or contralateral cerebellum (CE) according to tumor localization and cortical metabolism around the tumor in patients with brain tumors based on FDG uptake. This study investigated 48 consecutive patients with solitary cerebral hemisphere parenchymal brain tumors who underwent PET/CT and MRI. Patients were divided into 4 groups (A: reduced uptake in ipsilateral TH and contralateral CE, B: reduced uptake in ipsilateral TH only, C: reduced uptake in contralateral CE only, and D: no reduced uptake in ipsilateral TH or contralateral CE). FDG uptake and MRI findings were compared among these groups. Of 48 patients, group A included 24 (50%), group B included 10 (21%), group C included 0, and group D included 14 (29%). No significant tendencies were observed between the groups regarding tumor localization. However, reduced cortical metabolism around the tumor was observed in 22 patients in group A, 7 patients in group B, and 1 patient in group D. All patients in group B showed reduced metabolism from around the tumor up to the ipsilateral TH. Reduced FDG uptake in ipsilateral TH and contralateral CE usually occur simultaneously in patients with solitary brain tumors.

Role of mass effect, tumor volume and peritumoral edema volume in the differential diagnosis of primary brain tumor and metastasis

IntroductionThe differentiation of metastatic and primary brain tumors with certainty is important since clinical management and treatment of these two types of tumors are radically different.The purpose of the present study was to evaluate the effect of peritumoral edema volume, tumor volume and mass effect of tumor on differential diagnosis of metastatic and primary brain tumors. Also we have planned to investigate if the relationship between edema volume and mass affect can contribute to the differential diagnosis. Material and methodsWe retrospectively reviewed MR images of patients with primary (n=40) and metastatic (n=40) intra-axial supratentorial brain tumor. Supratentorial primary solitary brain tumor group was also subdivided as GBM subgroup (n=24) and other than GBM subgroup (n=16) for statistical analysis. Metastasis at suitable localization which can lead to midline shift (due to mass effect) were selected. Tumor volume, peritumoral edema volume and mass-edema index (peritumoral edema volume/tumor volume) were calculated. Displacement of the midline structures (subfalcian herniation) was measured.Metastasis, GBM and other than GBM groups were evaluated for subfalcian shift, shift grade, tumor volume, peritumoral edema volume and mass-edema index by using Kruskal-Wallis test after Bonferroni correction. Mann-Whitney U test was used to analise subfalcian shift, tumor volume, peritumoral edema volume and mass-edema index of primary tumor and methastasis groups since the data was not normally distributed. Shift grade of the two groups was analised with chi-square test. ResultsMidline shift, tumor volume and mass-edema index were significantly different between metastasis and primary tumor groups (p=0.001, p<0.001, p=0.001 respectively). Midline shift and tumor volume of the primary tumor group were greater than metastasis group while mass-edema index was less. Shift grade of metastasis and primary tumor groups was also significant (p=0.041). A midline shift more than 5mm (grade 2) was more common in primary tumors. There was no significant difference between GBM and other than GBM groups. ConclusionMeasurement of midline shift, tumor volume and mass-edema index may contribute to the differential diagnosis of brain metastasis from primary brain tumors. Also mass-edema index can be a useful tool for differential diagnosis in the future. But further studies with larger series are needed.