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

Bi-exponential diffusion-weighted imaging for differentiating high-grade gliomas from solitary brain metastases: a VOI-based histogram analysis

This study investigated the use of bi-exponential diffusion-weighted imaging (DWI) combined with structural features to differentiate high-grade glioma (HGG) from solitary brain metastasis (SBM). A total of 57 patients (31 HGG, 26 SBM) who underwent pre-surgical multi-b DWI and structural MRI (T1W, T2W, T1W + C) were included. Volumes of interest (VOI) in the peritumoral edema area (PTEA) and enhanced tumor area (ETA) were selected for analysis. Histogram features of slow diffusion coefficient (Dslow), fast diffusion coefficient (Dfast), and perfusion fraction (frac) were extracted. Results showed that HGG patients had higher skewness of Dfast (P = 0.022) and frac (P = 0.077), higher kurtosis of Dslow (P = 0.019) and frac (P = 0.025), and lower entropy of Dslow (P = 0.005) and frac (P = 0.001) within the ETA. Additionally, HGG exhibited lower mean frac in both ETA (P = 0.007) and PTEA (P = 0.017). Combining skewness of frac in ETA with clear tumor margin enhanced diagnostic performance, achieving an optimal AUC of 0.79. These findings suggest that histogram analysis of diffusion and perfusion characteristics in ETA and structural features can effectively differentiate HGG from SBM.

Solid stress estimations via intraoperative 3D navigation in patients with brain tumors

Background

Physical forces exerted by expanding brain tumors - specifically the compressive stresses propagated through solid tissue structures - reduces brain perfusion and neurological function, but heretofore has not been directly measured in patients in vivo . Solid stress levels estimated from tumor growth patterns are negatively correlated with neurological performance in patients. We hypothesize that measurements of solid stress can be used to inform clinical management of brain tumors.

Methods

We developed an intraoperative technique to quantitatively estimate solid stress and brain replacement by the tumor. In 30 patients we made topographic measurements of brain deformation through the craniotomy site with a neuronavigation system during surgical workflows immediately preceding tumor resection (< 5 minutes in the OR). Utilizing these measurements in conjunction with finite element modeling, we calculated solid stress within the tumor and the brain, and estimated the amount of brain tissue replaced, i.e., lost, by the tumor growth.

Results

Mean solid stresses were in the range of 10 to 600 Pa, and the amount of tissue replacement was up to 10% of the brain. Brain tissue loss in patients delineated glioblastoma from brain metastatic tumors, and in mice solid stress was a sensitive biomarker of chemotherapy response.

Conclusions

We present here a quantitative approach to intraoperatively measure solid stress in patients that can be readily adopted into standard clinical workflows. Brain tissue loss due to tumor growth is a novel mechanical-based biomarker that, in addition to solid stress, may inform personalized management in future clinical studies in brain cancer.

Key Points

Intraoperative and computational technique quantified solid stress and tissue loss in 30 patients Solid stress and tissue loss distinguished tumor types, showing potential as clinical biomarkers.

Importance of the Study

This study addresses a critical gap, as solid stress has been implicated in tumor progression and treatment resistance but not directly measured in patients with brain cancers before. Here, we present a novel intraoperative technique to quantitatively measure solid stress and brain tissue replacement in brain tumor patients. By combining intraoperative neuro-navigation with finite element modeling, we estimate solid stress and quantify the loss of brain tissue replaced by tumor growth. Importantly, higher tissue replacement was associated with glioblastoma compared to metastatic tumors. In mice, solid stress is a sensitive biomarker of treatment response. These findings establish solid stress and tissue replacement as potential physical biomarkers to inform personalized management of brain tumors. Quantifying these mechanical forces during surgery could help predict patient outcomes and guide clinical decision-making.

Dose and Efficacy of Bevacizumab in Recurrent High-Grade Gliomas: A Retrospective Study

Purpose

We retrospectively analyzed the effect of Bevacizumab (BEV) on recurrent high-grade glioma (rHGG) and examined the relationship between dose and efficacy.

Methods

A total of 182 patients with rHGG were included in this study. Patients were divided into a non-BEV group and a BEV group according to the treatment they received, and the BEV group was further divided into a low-dose group and a high-dose group based on the dose. Depending on the number of groups and the characteristics of numerical variables, t-test, ANOVA, or rank-sum test were selected. Categorical variables were compared using the chi-squared test.

Results

Progression-free survival (PFS) was lower in the non-BEV group compared to the BEV group, while overall survival (OS) was not different between the two groups. There was no difference in PFS and OS between low-dose group and high-dose group. Notably, we found that patients with longer PFS and OS were more likely to be from the BEV group. In addition, differences in Karnofsky Performance Score (KPS), steroid dose, and brain edema were observed in the non-BEV, low-dose, and high-dose groups from 3 to 12 months after treatment.

Conclusion

BEV can improve PFS in patients with rHGG, although its impact on OS is limited. There was no difference in the efficacy of different doses of BEV on rHGG. Interestingly, patients with longer PFS and OS were more likely to be from the BEV group. Based on these findings, long-term low-dose BEV appears to be an effective treatment option for rHGG.

Differentiation of glioma and solitary brain metastasis: a multi-parameter magnetic resonance imaging study using histogram analysis

BACKGROUND

Differentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn't been fully investigated for the differentiation and may have the potential to improve it.

METHODS

A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance.

RESULTS

Higher ADCkurtosis (P = 0.022), frackurtosis (P<0.001),and fracskewness (P<0.001) were found for glioma, while higher (MTRasym@3.5ppm)10 (P = 0.045), frac10 (P<0.001),frac90 (P = 0.001), fracmean (P<0.001), and fracentropy (P<0.001) were observed for SBM. frackurtosis (OR = 0.431, 95%CI 0.256-0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)10, frac10, and frackurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac10 and frackurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25).

CONCLUSIONS

The frac10 and frackurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)10 helps improving the differentiation specificity.

Effect of low-dose bevacizumab on health-related quality of life in patients with recurrent high-grade glioma: A retrospective clinical study

BACKGROUND

We retrospectively analyzed the effects of low-dose bevacizumab (BEV) combined with temozolomide (TMZ) on health-related quality of life (HRQL) in patients with recurrent high-grade glioma (rHGG).

METHODS

A total of 129 patients with rHGG were included in this study. Patients were divided into a combination group and TMZ group based on the treatment they received. The Quality of Life Questionnaire Core 30 (QLQ-C30) and EORTC Brain Cancer Module (QLQ-BN20) were used to evaluate HRQL in all patients before and after treatment. Categorical variables were compared using the chi-squared test. The data for all continuous variables were first tested for a normal distribution. If the data conformed to a normal distribution, a T test was used for comparison. If the data did not conform to a normal distribution, the rank-sum test was used.

RESULTS

There were differences in PFS and PFS-6 between the BEV + TMZ and TMZ groups (P＜0.05). However, there was no difference in the OS between the two groups (P＞0.05). The BEV + TMZ group performed better than the TMZ group in both the QLQ-C30 and QLQ-BN20. In addition, the KPS score was higher in the BEV + TMZ group than in the TMZ group. Steroid doses given were lower in the BEV + TMZ group than in the TMZ group (P < 0.05).

CONCLUSIONS

Low-dose BEV + TMZ can relieve the clinical symptoms of rHGG patients, reduce their steroid dose, improve HRQL, and prolong PFS, but does not bear any benefit on OS.

Differentiation of multiple brain metastases and glioblastoma with multiple foci using MRI criteria

OBJECTIVE

Glioblastoma with multiple foci (mGBM) and multiple brain metastases share several common features on magnetic resonance imaging (MRI). A reliable preoperative diagnosis would be of clinical relevance. The aim of this study was to explore the differences and similarities between mGBM and multiple brain metastases on MRI.

METHODS

We performed a retrospective analysis of 50 patients with mGBM and compared them with a cohort of 50 patients with multiple brain metastases (2-10 lesions) histologically confirmed and treated at our department between 2015 and 2020. The following imaging characteristics were analyzed: lesion location, distribution, morphology, (T2-/FLAIR-weighted) connections between the lesions, patterns of contrast agent uptake, apparent diffusion coefficient (ADC)-values within the lesion, the surrounding T2-hyperintensity, and edema distribution.

RESULTS

A total of 210 brain metastases and 181 mGBM lesions were analyzed. An infratentorial localization was found significantly more often in patients with multiple brain metastases compared to mGBM patients (28 vs. 1.5%, p < 0.001). A T2-connection between the lesions was detected in 63% of mGBM lesions compared to 1% of brain metastases. Cortical edema was only present in mGBM. Perifocal edema with larger areas of diffusion restriction was detected in 31% of mGBM patients, but not in patients with metastases.

CONCLUSION

We identified a set of imaging features which improve preoperative diagnosis. The presence of T2-weighted imaging hyperintensity connection between the lesions and cortical edema with varying ADC-values was typical for mGBM.

Brain-Tumor Interface-Based MRI Radiomics Models to Determine EGFR Mutation, Response to EGFR-TKI and T790M Resistance Mutation in Non-Small Cell Lung Carcinoma Brain Metastasis

BACKGROUND

Preoperative assessment of epidermal growth factor receptor (EGFR) status, response to EGFR-tyrosine kinase inhibitors (TKI) and development of T790M mutation in non-small cell lung carcinoma (NSCLC) patients with brain metastases (BM) is important for clinical decision-making, while previous studies were only based on the whole BM.

PURPOSE

To investigate values of brain-to-tumor interface (BTI) for determining the EGFR mutation, response to EGFR-TKI and T790M mutation.

STUDY TYPE

Retrospective.

POPULATION

Two hundred thirty patients from Hospital 1 (primary cohort) and 80 patients from Hospital 2 (external validation cohort) with BM and histological diagnosis of primary NSCLC, and with known EGFR status (biopsy) and T790M mutation status (gene sequencing).

FIELD STRENGTH/SEQUENCE

Contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) fast spin echo sequences at 3.0T MRI.

ASSESSMENT

Treatment response to EGFR-TKI therapy was determined by the Response Evaluation Criteria in Solid Tumors. Radiomics features were extracted from the 4 mm thickness BTI and selected by least shrinkage and selection operator regression. The selected BTI features and volume of peritumoral edema (VPE) were combined to construct models using logistic regression.

STATISTICAL TESTS

The performance of each radiomics model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

A total of 7, 3, and 3 features were strongly associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status, respectively. The developed models combining BTI features and VPE can improve the performance than those based on BTI features alone, generating AUCs of 0.814, 0.730, and 0.774 for determining the EGFR mutation, response to EGFR-TKI and T790M mutation, respectively, in the external validation cohort.

DATA CONCLUSION

BTI features and VPE were associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status in NSCLC patients with BM.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 2.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.

Ensemble Inversion for Brain Tumor Growth Models With Mass Effect

We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single scalar parameter, tumor proliferation, migration, while localizing the tumor initiation site. The single-scan calibration problem is severely ill-posed because the precancerous, healthy, brain anatomy is unknown. To address the ill-posedness, we introduce an ensemble inversion scheme that uses a number of normal subject brain templates as proxies for the healthy precancer subject anatomy. We verify our solver on a synthetic dataset and perform a retrospective analysis on a clinical dataset of 216 glioblastoma (GBM) patients. We analyze the reconstructions using our calibrated biophysical model and demonstrate that our solver provides both global and local quantitative measures of tumor biophysics and mass effect. We further highlight the improved performance in model calibration through the inclusion of mass effect in tumor growth models-including mass effect in the model leads to 10% increase in average dice coefficients for patients with significant mass effect. We further evaluate our model by introducing novel biophysics-based features and using them for survival analysis. Our preliminary analysis suggests that including such features can improve patient stratification and survival prediction.

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis, and Primary Central Nervous System Lymphoma: An External Validation Study

(1) Background: Neuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) represents a diagnostic and therapeutic challenge in neurosurgical practice, expanding the burden of care and exposing patients to additional risks related to further invasive procedures and treatment delays. In addition, atypical cases and overlapping features have not been entirely addressed by modern diagnostic research. The aim of this study was to validate a previously designed and internally validated ResNet101 deep learning model to differentiate glioblastomas, PCNSLs and BMs. (2) Methods: We enrolled 126 patients (glioblastoma: n = 64; PCNSL: n = 27; BM: n = 35) with preoperative T1Gd-MRI scans and histopathological confirmation. Each lesion was segmented, and all regions of interest were exported in a DICOM dataset. A pre-trained ResNet101 deep neural network model implemented in a previous work on 121 patients was externally validated on the current cohort to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans. (3) Results: The model achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.73; 95%CI: 0.62–0.85), glioblastomas (AUC: 0.78; 95%CI: 0.71–0.87) and moderate to low ability in differentiating BMs (AUC: 0.63; 95%CI: 0.52–0.76). The performance of expert neuro-radiologists on conventional plus advanced MR imaging, assessed by retrospectively reviewing the diagnostic reports of the selected cohort of patients, was found superior in accuracy for BMs (89.69%) and not inferior for PCNSL (82.90%) and glioblastomas (84.09%). (4) Conclusions: We investigated whether the previously published deep learning model was generalizable to an external population recruited at a different institution—this validation confirmed the consistency of the model and laid the groundwork for future clinical applications in brain tumour classification. This artificial intelligence-based model might represent a valuable educational resource and, if largely replicated on prospective data, help physicians differentiate glioblastomas, PCNSL and solitary BMs, especially in settings with limited resources.

Cranial defect and pneumocephalus are associated with significant postneurosurgical positional brain shift: evaluation using upright computed tomography

Only few studies have assessed brain shift caused by positional change. This study aimed to identify factors correlated with a large postneurosurgical positional brain shift (PBS). Sixty-seven patients who underwent neurosurgical procedures had upright computed tomography (CT) scan using settings similar to those of conventional supine CT. The presence of a clinically significant PBS, defined as a brain shift of ≥ 5 mm caused by positional change, was evaluated. The clinical and radiological findings were investigated to identify factors associated with a larger PBS. As a result, twenty-one patients had a clinically significant PBS. The univariate analysis showed that supratentorial lesion location, intra-axial lesion type, craniectomy procedure, and residual intracranial air were the predictors of PBS. Based on the multivariate analysis, craniectomy procedure (p < 0.001) and residual intracranial air volume (p = 0.004) were the predictors of PBS. In a sub-analysis of post-craniectomy patients, PBS was larger in patients with supratentorial craniectomy site and parenchymal brain injury. A large craniectomy area and long interval from craniectomy were correlated with the extent of PBS. In conclusion, patients who undergo craniectomy and those with residual intracranial air can present with a large PBS. In post-craniectomy patients, the predisposing factors of a large PBS are supratentorial craniectomy, presence of parenchymal injury, large skull defect area, and long interval from craniectomy. These findings can contribute to safe mobilization among postneurosurgical patients and the risk assessment of sinking skin flap syndrome.

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

Background

Neuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) remains challenging in specific cases showing similar appearances or atypical features. Overall, advanced MRI protocols have high diagnostic reliability, but their limited worldwide availability, coupled with the overlapping of specific neuroimaging features among tumor subgroups, represent significant drawbacks and entail disparities in the planning and management of these oncological patients.

Objective

To evaluate the classification performance metrics of a deep learning algorithm trained on T1-weighted gadolinium-enhanced (T1Gd) MRI scans of glioblastomas, atypical PCNSLs and BMs.

Materials and Methods

We enrolled 121 patients (glioblastoma: n=47; PCNSL: n=37; BM: n=37) who had undergone preoperative T1Gd-MRI and histopathological confirmation. Each lesion was segmented, and all ROIs were exported in a DICOM dataset. The patient cohort was then split in a training and hold-out test sets following a 70/30 ratio. A Resnet101 model, a deep neural network (DNN), was trained on the training set and validated on the hold-out test set to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans.

Results

The DNN achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.98; 95%CI: 0.95 - 1.00) and glioblastomas (AUC: 0.90; 95%CI: 0.81 - 0.97) and moderate ability in differentiating BMs (AUC: 0.81; 95%CI: 0.70 - 0.95). This performance may allow clinicians to correctly identify patients eligible for lesion biopsy or surgical resection.

Conclusion

We trained and internally validated a deep learning model able to reliably differentiate ambiguous cases of PCNSLs, glioblastoma and BMs by means of T1Gd-MRI. The proposed predictive model may provide a low-cost, easily-accessible and high-speed decision-making support for eligibility to diagnostic brain biopsy or maximal tumor resection in atypical cases.

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics

Background

Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.

Purpose

To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses.

Methods and Materials

A total of 350 patients were collected from two institutions, including 182 patients with GBM and 168 patients with MET, which were all proven by pathology. The ROI of the tumor was obtained on axial postcontrast MRI which was performed before operation. Seven radiomic feature selection methods and four classification algorithms constituted 28 classifiers in two classification strategies, with the best classifier serving as the final radiomics model. The clinical and combination models were constructed using the nomograms developed. The performance of the nomograms was evaluated in terms of calibration, discrimination, and clinical usefulness. Student’s t-test or the chi-square test was used to assess the differences in the clinical and radiological characteristics between the training and internal validation cohorts. Receiver operating characteristic curve analysis was performed to assess the performance of developed models with the area under the curve (AUC).

Results

The classifier fisher_decision tree (fisher_DT) showed the best performance (AUC: 0.696, 95% CI:0.608-0.783) for distinguishing between GBM and MET in internal validation cohorts; the classifier reliefF_random forest (reliefF_RF) showed the best performance (AUC: 0.759, 95% CI: 0.613-0.904) for distinguishing between MET-lung and MET-other in internal validation cohorts. The combination models incorporating the radiomics signature and clinical-radiological characteristics were superior to the clinical-radiological models in the two classification strategies (AUC: 0.764 for differentiation between GBM in internal validation cohorts and MET and 0.759 or differentiation between MET-lung and MET-other in internal validation cohorts). The nomograms showed satisfactory performance and calibration and were considered clinically useful, as revealed in the decision curve analysis.

Data Conclusion

The combination of radiomic and non-radiomic features is helpful for the differentiation among GBM, MET-lung, and MET-other.

Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis

BACKGROUND

Brain metastasis and glioblastoma multiforme are two of the most common malignant brain neoplasms. There are many difficulties in distinguishing these diseases from each other.

PURPOSE

The purpose of this study was to determine whether the mean apparent diffusion coefficient and absolute standard deviation derived from apparent diffusion coefficient measurements can be used to differentiate glioblastoma multiforme from brain metastasis based on cellularity levels.

MATERIAL AND METHODS

Magnetic resonance images of 34 patients with histologically verified brain tumors were evaluated retrospectively. Apparent diffusion coefficient and standard deviation values were measured in the enhancing tumor, peritumoral region, and contralateral healthy white matter. Then, to determine whether there was a statistical difference between brain metastasis and glioblastoma multiforme, we analyzed different variables between the two groups.

RESULTS

Neither mean apparent diffusion coefficient values and ratios nor standard deviation values and ratios were significantly different between glioblastoma multiforme and brain metastasis. Receiver operating characteristic curve analysis of the logistic model with backward stepwise feature selection yielded an area under the curve of 0.77, a specificity of 84%, a sensitivity of 67%, a positive predictive value of 83.33%, and a negative predictive value of 78.26% for distinguishing between glioblastoma multiforme and brain metastasis. The absolute standard deviation and standard deviation ratios were significantly higher in the peritumoral edema compared to the tumor region in each case.

CONCLUSION

Apparent diffusion coefficient values and ratios, as well as standard deviation values and ratios in peritumoral edema, cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema. However, standard deviation values could successfully characterize areas of peritumoral edema from the tumoral region in each case.

Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images

BACKGROUND AND PURPOSE

Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of a deep learning-based model in discriminating glioblastoma from solitary brain metastasis using preoperative conventional MR images was evaluated.

MATERIALS AND METHODS

Records of 598 patients with histologically confirmed glioblastoma or solitary brain metastasis at our institution between February 2006 and December 2017 were retrospectively reviewed. Preoperative contrast-enhanced T1WI and T2WI were preprocessed and roughly segmented with rectangular regions of interest. A deep neural network was trained and validated using MR images from 498 patients. The MR images of the remaining 100 were used as an internal test set. An additional 143 patients from another tertiary hospital were used as an external test set. The classifications of ResNet-50 and 2 neuroradiologists were compared for their accuracy, precision, recall, F1 score, and area under the curve.

RESULTS

The areas under the curve of ResNet-50 were 0.889 and 0.835 in the internal and external test sets, respectively. The area under the curve of neuroradiologists 1 and 2 were 0.889 and 0.768 in the internal test set and 0.857 and 0.708 in the external test set, respectively.

CONCLUSIONS

A deep learning-based model may be a supportive tool for preoperative discrimination between glioblastoma and solitary brain metastasis using conventional MR images.

Edema-mass Ratio Based On Magnetic Resonance Imaging As A Preoperative Diagnostic Factor For Posterior Fossa Metastasis

BACKGROUND

Peritumoral edema of primary brain tumors is an important cause of morbidity and mortality. The number of studies currently available on the prognostic role of peritumoral brain edema in the posterior fossa is extremely limited.

OBJECTIVE

Based on the known importance of magnetic resonance imaging in diagnosing supratentorial metastases, this study aimed to investigate the effects of peritumoral edema on survival of patients with posterior fossa metastases and the preoperative diagnostic value of MRI.

METHODS

Edema and mass volumes of 49 patients with posterior fossa metastasis, who underwent surgery during 2012-2016, were measured using magnetic resonance imaging. The edema/mass indices were retrospectively calculated and interpreted by evaluating the demographic, clinical, and survival data.

RESULTS

The study consisted of 32 (65.3%) male and 17 (34.7%) female participants, with the mean age ± standard deviation of 47.25±29.25 (17-81) years. Among the 49 patients with posterior fossa metastases, 34 (69.4%) had carcinoma, while 15 (30.6%) had non-carcinoma metastases. The edema/mass indices of patients with carcinoma and non-carcinoma metastases were found to be 14.55±9.64 and 1.34±1.08, respectively, and the difference was statistically significant (p<0.001). The mean survival of patients with carcinoma and non-carcinoma metastases was found to be 642±11.52 days and 726±9.32 days, respectively; however, this difference was not statistically significant (p=0.787).

CONCLUSION

The edema/mass ratio was found to be a significant diagnostic factor for the prediction of posterior fossa metastases. Further detailed studies are warranted to investigate the effect of edema/mass ratio on survival rate.

Concurrent Apatinib and Brain Radiotherapy in Patients With Brain Metastases From Driver Mutation-negative Non-small-cell Lung Cancer: Study Protocol for an Open-label Randomized Controlled Trial

Brain radiotherapy (BR) is a well-recognized approach for multiple brain metastases (BMs) from non-small-cell lung cancer (NSCLC). However, the prognosis for these patients remains poor. Apatinib, an antiangiogenic agent targeting vascular endothelial growth factor receptor-2, has shown excellent efficacy in multiple solid tumors. This phase II (WWW. ClinicalTrials.gov Identifier: VEGFR-2 NCT03801200) randomized trial aims to evaluate the efficacy and safety of this combined modality paradigm in patients with BMs from driver mutation-negative NSCLC. This is a multicenter, open-label, randomized controlled clinical trial. A total of 90 eligible patients will be allocated in a 1:1 ratio, to either the experimental group (concurrent apatinib and BR) or the control group (BR alone). The primary endpoint is intracranial progression-free survival. The secondary endpoints include intracranial objective response rate, intracranial disease control rate, intracranial time to progression, overall survival, and occurrence of peritumoral brain edema using standardized measurement. Quality of life and adverse events will also be evaluated. Assessments will be carried out before enrollment (baseline) along with 4 and 12 weeks after radiotherapy, followed by every 12 weeks thereafter and up to 24 months. In summary, the aim of this trial is to demonstrate the clinical efficacy and safety of concurrent BR and apatinib in patients with driver mutation-negative NSCLC with multiple BMs, in efforts to expand management options for this population with poor prognosis.

The Infratentorial Localization of Brain Metastases May Correlate with Specific Clinical Characteristics and Portend Worse Outcomes Based on Voxel-Wise Mapping

The infratentorial regions are vulnerable to develop brain metastases (BMs). However, the associations between the infratentorial localization of BMs and clinical characteristics remained unclear. We retrospectively studied 1102 patients with 4365 BM lesions. Voxel-wise mapping of MRI was applied to construct the tumor frequency heatmaps after normalization and segmentation. The analysis of differential involvement (ADIFFI) was further used to obtain statistically significant clusters. Kaplan-Meier method and Cox regression were used to analyze the prognosis. The parietal, insular and left occipital lobes, and cerebellum were vulnerable to BMs with high relative metastatic risks. Infratentorial areas were site-specifically affected by the lung, breast, and colorectal cancer BMs, but inversely avoided by melanoma BMs. Significant infratentorial clusters were associated with young age, male sex, lung neuroendocrine and squamous cell carcinomas, high expression of Ki-67 of primaries and BMs, and patients with poorer prognosis. Inferior OS was observed in patients with ≥3 BMs and those who received whole-brain radiotherapy alone. Infratentorial involvement of BMs was an independent risk factor of poor prognosis for patients who received surgery (p = 0.023, hazard ratio = 1.473, 95% confidence interval = 1.055-2.058). The current study may add valuable clinical recognition of BMs and provide references for BMs diagnosis, treatment evaluation, and prognostic prediction.

Evaluation of Perienhancing Area in Differentiation between Glioblastoma and Solitary Brain Metastasis

Purpose:

Accurate differential diagnosis between glioblastoma and brain metastasis is important. We aimed to differentiate these tumors by evaluation of the perienhancing area.

Materials and Methods:

Thirty patients with glioblastoma and solitary brain metastasis were included. The diameters of perienhancing and enhancing areas were measured, and the percentage of enhancing area was calculated. We measured Apparent diffusion coefficient (ADC) of perienhancing and enhancing areas. Intratumoral necrotic areas were measured.

Results:

The enhancing area of glioblastoma was 56.61% and metastasis was 42.55% (p = 0.08). The ADC values of the perienhancing part of GBM was 0.7 and metastasis was 0.79 (p = 0.052). The ADC value of the enhancing part of the GBM was 0.82 and metastasis was 0.8 (p-value = 0.72). The intratumoral necrotic area of glioblastoma (152.25 mm³) was higher than in metastasis (0 mm³) (p-value = 0.003) with a cutoff area of 11.8 mm².

Conclusion:

The ADC values of the perienhancing area were lower in glioblastoma with a near-significant p-value. Other perienhancing parameters demonstrated no significant difference between both tumors. The intratumoral necrotic area of glioblastoma is larger than metastasis.

Next-Generation Sequencing Analysis of ctDNA for the Detection of Glioma and Metastatic Brain Tumors in Adults

Background and aims: The next-generation sequencing technologies and their related assessments of circulating tumor DNA in both glioma and metastatic brain tumors remain largely limited.

Methods: Based largely on a protocol approved by the institutional review board at Peking University International Hospital, the current retrospective, single-center study was conducted. Genomic DNA was extracted from blood samples or tumor tissues. With the application of NextSeq 500 instrument (Illumina), Sequencing was performed with an average coverage of 550-fold. Paired-end sequencing was employed utilized with an attempt to achieve improved sensitivity of duplicate detection and therefore to increase the detection reliability of possible fusions.

Results: A total of 28 patients (21 men and 7 women) with brain tumors in the present study were involved in the study. The patients enrolled were assigned into two groups, including glioma group (n = 21) and metastatic brain tumor group (n = 7). The mean age of metastatic brain tumor group (59.86 ± 8.85 y), (43.65 ± 13.05 y) reported significantly higher results in comparison to that of glioma group (45.3 ± 12.3 years) (P < 0.05). The mutant genes in metastatic brain tumor group included ALK, MDM2, ATM, BRCA1, FGFR1, MDM4 and KRAS; however, there were no glioma-related mutant genes including MGMT, IDH1, IDH2, 1p/19q, and BRAF et al. Interesteringly, only two patient (28.3%) was detected blood ctDNA in metastatic brain tumor group; In contrast, blood ctDNA was found in ten glioma patients (47.6%) including 1p/19q, MDM2, ERBB2, IDH1, CDKN2A, CDK4, PDGFRA, CCNE1, MET. The characterizations of IDH mutations in the glioma included IDH1 mutation (p.R132H) and IDH2 mutation (p.R172K). The mutation rate of IDH in tumor tissues was 37.06 ± 8.32%, which was significantly higher than blood samples (P < 0.05).

Conclusion: The present study demonstrated that the mutant genes among glioma and metastatic brain tumors are shown to be different. Moreover, the ctDNAs in the metastatic brain tumors included ALK and MDM2, and glioma-related ctDNAs included 1p/19q and MDM2 followed by frequencies of ERBB2, IDH1, CDKN2A, CDK4, PDGFRA, CCNE1, MET. These ctDNAs might be biomarkers and therapeutic responders in brain tumor.

Effect of Piezo1 Overexpression on Peritumoral Brain Edema in Glioblastomas

BACKGROUND AND PURPOSE

Previous studies have suggested that increased mortality and disability in patients with brain tumor are associated with peritumoral brain edema. However, the mechanism of peritumoral brain edema in brain tumors is unknown. This study aimed to investigate the effect of Piezo1 overexpression on peritumoral brain edema in glioblastomas.

MATERIALS AND METHODS

The Piezo1 expression in cell lines and paired samples was detected by quantitative reverse transcription polymerase chain reaction, Western blot, and immunohistochemistry. Sixty-four patients with glioblastomas were analyzed retrospectively. The Piezo1 expression of tumor tissue was detected by immunohistochemistry. The diameters of tumor and edema were measured by preoperative MR imaging, and the edema index value was calculated.

RESULTS

Western blot and quantitative reverse transcription polymerase chain reaction showed that Piezo1 expression was higher in 6 glioma cell lines than in the normal astrocyte cell line. Compared with peritumoral tissues, Piezo1 was up-regulated in tumor tissues. Sixty-four patients with glioblastomas were enrolled in further study. Piezo1 was higher in the moderate edema group than in the mild edema group (P < .001), higher in the severe edema group than in the moderate edema group (P < .001), and correlated with the edema index (r = 0.73; P < .001). Receiver operating characteristic curve analysis showed that the edema index yielded an area under the curve of 0.867 (95% CI, 0.76-0.97; P < .001), with a sensitivity of 100% and a specificity of 70%.

CONCLUSIONS

Piezo1 overexpression is positively correlated with the degree of peritumoral brain edema in glioblastomas. Predicting high Piezo1 expression in tumor tissues based on the edema extent shows good sensitivity and specificity.

Evaluating angioarchitectural characteristics of glial and metastatic brain tumors with conventional magnetic resonance imaging

Primary and metastatic brain tumors can overlap in traditional imaging features detected on preoperative conventional magnetic resonance imaging (MRI). The research objective was to determine whether morphological vascular characteristics present in routine preoperative imaging using traditional MRI sequences are predictive of primary versus metastatic brain tumors; secondarily to determine association of conventional and vascular-related imaging parameters with intraoperative blood loss, pathological invasion, and World Health Organization (WHO) tumor grade. A retrospective review analyzed 100 consecutive intracranial tumor surgeries, 50 WHO grade II-IV gliomas and 50 intracranial metastases. Two blinded expert readers independently evaluated preoperative MRIs, obtained via standard morphological imaging sequences, for adjacent or intra-tumoral arterial aneurysm, peritumoral venous ectasia, prominence, or engorgement ("aberrant peritumoral vessels"), and prominent intra-tumoral flow voids. Multivariate analysis was performed to develop models predictive of glioma and glioblastoma (GBM). Aberrant peritumoral vessels and prominent intra-tumoral flow voids were statistically significant predictors of glioma in univariate analyses (p = 0.048, p = 0.001, respectively) and when combined in multivariate analysis (OR = 5.23, p = 0.001), particularly for GBM (OR = 9.08, p < 0.001). Multivariate modeling identified prominent intra-tumoral flow voids and FLAIR invasion as the strongest combined predictors of gliomas and GBM. Aberrant peritumoral vessels and larger tumor volume predicted higher intraoperative blood loss in all analyses. No vascular-related parameters predicted pathological invasion on multivariate analysis. Aberrant peritumoral vessels and prominent intra-tumoral flow voids were predictive of gliomas, specifically GBM. These vascular characteristics, evaluated on routine clinical preoperative MRI imaging, may aid in distinguishinggliomafrom brainmetastases andmay predict intraoperative blood loss.

Alterations of functional and structural connectivity in patients with brain metastases

Metastases are the most prevalent tumors in the brain and are commonly associated with high morbidity and mortality. Previous studies have suggested that brain tumors can induce a loss of functional connectivity and alter the brain network architecture. Little is known about the effect of brain metastases on whole-brain functional and structural connectivity networks. In this study, 14 patients with brain metastases and 16 healthy controls underwent resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). We constructed functional connectivity network using rs-fMRI signal correlations and structural connectivity network using DTI tractography. Graph theoretical analysis was employed to calculate network properties. We further evaluated the performance of brain networks after metastases resection by a simulated method. Compared to healthy controls, patients with brain metastases showed an altered “small-world” architecture in both functional and structural connectivity networks, shifting to a more randomness organization. Besides, the coupling strength of functional-structural connectivity was decreased in patients. After removing nodes infiltrated by metastases, aggravated disruptions were found in both functional and structural connectivity networks, and the alterations of network properties correlated with the removed hubs number. Our findings suggest that brain metastases interfere with the optimal network organization and relationship of functional and structural connectivity networks, and tumor resection involving hubs could cause a worse performance of brain networks. This study provides neuroimaging guidance for neurosurgical planning and postoperative assessment of brain metastases from the aspect of brain networks.

Functional Connectivity in Adult Brain Tumor Patients: A Systematic Review

Brain tumor (BT) patients often experience reduced cognitive abilities and disrupted adaptive functioning before and after treatment. An innovative approach to understanding the underlying brain networks associated with these outcomes has been to study the brain's functional connectivity (FC), the spatially distributed and temporally correlated activity throughout the brain, and how it can be affected by a tumor. The present review synthesized the extant BT FC literature that utilizes functional magnetic resonance imaging to study FC strength of commonly observed networks during rest and task. A systematic review of English articles using PubMed was conducted. Search terms included brain tumor OR glioma AND functional connectivity, independent component analysis, ICA, psychophysiological interaction, OR PPI. Studies in which participants were diagnosed with BTs as adults that evaluated specific networks of interest using independent component analysis or seed-based component analysis were included. Twenty-five studies met inclusion criteria. BT patients often presented with decreases in FC strength within well-established networks and increases in atypical FC patterns. Network differences were tumor adjacent and distal, and left hemisphere tumors generally had a greater impact on FC. FC alterations often correlated with behavioral or cognitive outcomes when assessed. Overall, BTs appear to lead to various alterations in FC across different functional networks, and the most common change is a decrease in expected FC strength. More longitudinal studies are needed to determine the time course of network alterations across treatment and recovery, the role of medical treatments in BT survivors' FC, and the potential of FC patterns as biomarkers of cognitive outcomes.

Computerized identification of the vasculature surrounding a pulmonary nodule

OBJECTIVES

The idea of inferring the prognosis of lung tumor via its surrounding vasculature is novel, but not supported by available technology. In this study, we described and validated a computerized method to identify the vasculature surrounding a pulmonary nodule depicted on low-dose computed tomography (LDCT).

MATERIALS AND METHODS

The proposed computerized scheme identified the vessels surrounding a lung nodule by using novel computational geometric solutions and quantified them by decomposing the vessels into independent vessel branches. We validated this scheme by testing it on a dataset consisting of 100 chest CT examinations, with 50-paired benign and malignant nodules. Two experienced pulmonologists were asked to measure the vessel branches associated with a nodule under the aid of a visualization tool. We used the Bland-Altman plots and the concordance correlation coefficient (CCC) to assess the agreement between the results of the computer algorithm and two experienced pulmonologists.

RESULTS

Bland-Altman different analysis demonstrated a mean bias of 0.61 ± 4.17 in terms of vessel branches between the computer results and the human experts, while the inter-rater mean bias was -0.61 ± 1.60. The CCC-based agreements between the computer and the two raters were 0.90 / 0.86, 0.79 / 0.83 for benign and malignant nodules, respectively.

CONCLUSION

The small width of the limits of agreement between the computer algorithm and the human experts suggests that the results from the computer and the pulmonologist experts were relatively consistent, namely the computerized scheme is capable of reliably identifying the vasculature surrounding a nodule.

Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis

BACKGROUND

Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI.

PURPOSE

To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post-contrast T₁ -weighted (T₁ W) MRI.

STUDY TYPE

Retrospective.

SUBJECTS

Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others).

FIELD STRENGTH/SEQUENCE

Post-contrast 3D T₁ W gradient echo images, acquired with 1.5 and 3.0 T MR systems.

ASSESSMENT

Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first- and second-order statistical, morphological, wavelet features, and bag-of-features. Following dimension reduction, classification was performed using various machine-learning algorithms including support-vector machine (SVM), k-nearest neighbor, decision trees, and ensemble classifiers.

STATISTICAL TESTS

For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.

RESULTS

For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively.

DATA CONCLUSION

Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T₁ W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519-528.

Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms

Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided.

The relationship between MRI quantitative parameters and the expression of hypoxia inducible factor-1 alpha in cerebral astrocytoma

OBJECTIVE

Astrocytoma is the common type of glioma. But the MRI scanning for astrocytoma preoperation pathological diagnosis is not exact. The purpose of this study was to use the MRI multi quantitative parameters to improve the diagnosis of astrocytoma and exploit their molecular mechanism related to the expression of HIF-1α.

METHODS

Superconducting MR scanner and its work station were used to calculate the MRI multi quantitative parameters of the selected patients in this experiment. Scion Image Beta4.03 software was used to get the cellular density of tumor tissue. The expression of HIF-1α in astrocytoma specimens was detected by immunohistochemistry method. The correlation of MRI multi quantitative parameters and the expression of HIF-1α was analyzed by statistical software.

RESULT

The values of ADC, RSIGd, EP, EI, cellular density and the expression of HIF-1α were changed with the malignant degree of astrocytoma to some extent, but not every quantitative parameter was related to the expression of HIF-1α.

CONCLUSION

The MRI multi quantitative parameters binding with conventional MRI imaging can significantly raise the diagnostic accuracy of astrocytoma preoperatively. MRI features could indirectly reflect the biological behavior of astrocytoma. The peritumoral edema can't be explained by only one theory.