Regions Of Peritumoral Edema Research Articles

Free water modeling of peritumoral edema using multi-fiber tractography: Application to tracking the arcuate fasciculus for neurosurgical planning.

PurposePeritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema.Materials and methodsWe retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts.ResultsThe AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route.ConclusionOur results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.

Value of Minimum Apparent Diffusion Coefficient in Peritumoral Edema in the Differential Diagnosis between Primary Central Nervous System Lymphoma and Glioblastoma

Objective To evaluate the role of minimum apparent diffusion coefficient(MinADC) values in peritumoral edema based on magnetic resonance diffusion weighted imaging in the differential diagnosis between primary central nervous system lymphoma(PCNSL) and glioblastoma(GBM).Methods ADC values in peritumoral edema were measured in 16 patients with PCNSL(diffuse large B cell lymphoma) and 31 patients with GBM(WHO grade 4) confirmed by pathology.Regions of interests were manually drawn on ADC maps on peritumoral edema regions to obtain the MinADC value.Independent samples t-test and receiver operating characteristic analysis were performed for statistical analysis.Results The MinADC value [(1.20-1.45)×10-3 mm2/s,mean(1.35±0.68)×10-3 mm2/s] in PCNSL was significantly higher than that in GBM [(0.95-1.31)×10-3 mm2/s,mean(1.12±0.09)×10-3 mm2/s](t=9.977,P=0.000).The area under the receiver operating characteristic curve was 0.986,and the cutoff value of MinADC was 1.245×10-3 mm2/s for the differentiation between PCNSL and GBM,with the best combination of sensitivity(94.1%) and specificity(94.1%).Conclusion MinADC value can be a simple and effective measure for the differential diagnosis between PCNSL and GBM.

USING DEEP NEURAL NETWORKS FOR RADIOGENOMIC ANALYSIS.

Radiogenomic studies have suggested that biological heterogeneity of tumors is reflected radiographically through visible features on magnetic resonance (MR) images. We apply deep learning techniques to map between tumor gene expression profiles and tumor morphology in pre-operative MR studies of glioblastoma patients. A deep autoencoder was trained on 528 patients, each with 12,042 gene expressions. Then, the autoencoder's weights were used to initialize a supervised deep neural network. The supervised model was trained using a subset of 109 patients with both gene and MR data. For each patient, 20 morphological image features were extracted from contrast-enhancing and peritumoral edema regions. We found that neural network pre-trained with an autoencoder and dropout had lower errors than linear regression in predicting tumor morphology features by an average of 16.98% mean absolute percent error and 0.0114 mean absolute error, where several features were significantly different (adjusted p-value < 0.05). These results indicate neural networks, which can incorporate nonlinear, hierarchical relationships between gene expressions, may have the representational power to find more predictive radiogenomic associations than pairwise or linear methods.

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival. This paper presents a method for estimating peritumoral edema infiltration using radiomic signatures determined via machine learning methods, and tests it on 90 patients with de novo glioblastoma. The generalizability of the proposed predictive model was evaluated via cross-validation in a discovery cohort ([Formula: see text]) and was subsequently evaluated in a replication cohort ([Formula: see text]). Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. The cross-validated accuracy of our predictive infiltration model on the discovery and replication cohorts was 87.51% (odds ratio = 10.22, sensitivity = 80.65, and specificity = 87.63) and 89.54% (odds ratio = 13.66, sensitivity = 97.06, and specificity = 76.73), respectively. The radiomic signature of the recurrent tumor region revealed higher vascularity and cellularity when compared with the nonrecurrent region. The proposed model shows evidence that multiparametric pattern analysis from clinical MRI sequences can assist in in vivo estimation of the spatial extent and pattern of tumor recurrence in peritumoral edema, which may guide supratotal resection and/or intensification of postoperative radiation therapy.

Effect of photodynamic therapy combined with torasemide on the expression of matrix metalloproteinase 2 and sodium-potassium-chloride cotransporter 1 in rat peritumoral edema and glioma.

Peritumoral edema is a key stage in the infiltration and recurrence of glioma. Photodynamic therapy (PDT) increases the extent of peritumoral edema, which leads to a decrease in the effectiveness of PDT in treating glioma. The present study evaluated the effects of PDT combined with torasemide on the levels of matrix metalloproteinase (MMP) 2 and sodium-potassium-chloride cotransporter (NKCC) 1 in peritumoral edema regions of rat glioma. Adult male Wistar rats were inoculated with rat glioma C6 cells, and the presence of glioma was confirmed using magnetic resonance imaging 7 days subsequent to injection. The rats were randomly assigned to 4 groups (n=15): Control group, the rats received no treatment; PDT group, the rats received PDT at 80 J/cm2 for 10 min; torasemide group, the rats received 5 mg/kg torasemide intraperitoneally; and PDT + torasemide group, the rats received 5 mg/kg torasemide intraperitoneally for 3 days following PDT at 80 J/cm2 for 10 min. A total of 5 rats from each group were sacrificed 21 days following injection and the peritumoral edema tissues were harvested. MMP2 and NKCC1 expression levels were detected in the tissues using immunohistochemistry and western blot analysis. The mRNA expression levels of MMP2 and NKCC1 were observed using reverse transcription-quantitative polymerase chain reaction. Peritumoral edema was measured using a wet-to-dry weight (W/D) ratio, and survival times of the remaining 10 rats in each group were evaluated. Compared with the control group, tumor growth was significantly suppressed in the PDT group and the survival time was prolonged through a reduction in the expression of MMP2 (P<0.05), and an increased W/D ratio resulted in significantly increased expression of NKCC1 (P<0.05). Compared with the PDT group, the expression of NKCC1 and the W/D ratio in the PDT + torasemide group were significantly decreased (P<0.05), while no significant difference was observed in the expression levels of MMP2. In conclusion, PDT combined with torasemide prolonged the survival time of rats by inhibiting the growth of glioma through a reduction in the expression of MMP2, and by reducing peritumoral edema through a reduction in the expression levels of NKCC1.

Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography.

The aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers. Ten patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-T magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-region-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts. Single-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. Based on visual inspection, the two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was apparently more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography ([Formula: see text]). Two-tensor UKF tractography tracks a larger volume CST than single-tensor streamline tractography in the setting of peritumoral edema and crossing fibers in brain tumor patients.

Non-model segmentation of brain glioma tissues with the combination of DWI and fMRI signals.

For quantitative analysis of glioma, multimodal Magnetic Resonance Imaging (MRI) signals are required in combination to perform a complementary analysis of morphological, metabolic, and functional changes. Most of the morphological analyses are based on T1-weighted and T2-weighted signals, called traditional MRI. But more detailed information about tumorous tissues could not be explained. An information combination scheme of Diffusion-Weighted Imaging (DWI) and Blood-Oxygen-Level Dependent (BOLD) contrast Imaging is proposed in this paper. This is a non-model segmentation scheme of brain glioma tissues in a particular perspective of combining multi-parameters of DWI and BOLD contrast functional Magnetic Resonance Imaging (fMRI). Compared with traditional MRI, a promising advantage of our work is to provide an effective and adequate subdivision of the related pathological regions with glioma, by incorporating both knowledge of image graylevel and spatial structure. Furthermore, it is an automatic segmentation method without needs of parameter selection and model fitting for the extracted tissues. By the experiments in patients with glioma, the proposed method has achieved the average overlap ratios of 83.6% in the whole tumor region and 82.5% in the peritumoral edema region with the manual segmentation as "ground truth".

Diffusion-weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging.

Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000s. Before its application in clinical oncology, this powerful technique had already achieved widespread recognition due to its utility in the diagnosis of cerebral infarction. Following this initial success, the ability of DWI to detect inherent tissue contrast began to be exploited in the field of oncology. Although the initial oncologic applications for tumor detection and characterization, assessing treatment response, and predicting survival were primarily in the field of neurooncology, the scope of DWI has since broadened to include oncologic imaging of the prostate gland, breast, and liver. Despite its growing success and application, misconceptions about the underlying physical basis of the DWI signal exist among researchers and clinicians alike. In this review, we provide a detailed explanation of the biophysical basis of diffusion contrast, emphasizing the difference between hindered and restricted diffusion, and elucidating how diffusion parameters in tissue are derived from the measurements via the diffusion model. We describe one advanced DWI modeling technique, called restriction spectrum imaging (RSI). This technique offers a more direct in vivo measure of tumor cells, due to its ability to distinguish separable pools of water within tissue based on their intrinsic diffusion characteristics. Using RSI as an example, we then highlight the ability of advanced DWI techniques to address key clinical challenges in neurooncology, including improved tumor conspicuity, distinguishing actual response to therapy from pseudoresponse, and delineation of white matter tracts in regions of peritumoral edema. We also discuss how RSI, combined with new methods for correction of spatial distortions inherent in diffusion MRI scans, may enable more precise spatial targeting of lesions, with implications for radiation oncology and surgical planning. See all articles in this Cancer Research section, "Physics in Cancer Research."

Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema

Conventional magnetic resonance (MR) imaging has a number of limitations in the diagnosis of the most common intracranial brain tumors, including tumor specification and the detection of tumoral infiltration in regions of peritumoral edema. To prospectively assess if diffusion-weighted MR imaging (DWI) could be used to differentiate between different types of brain tumors and to distinguish between peritumoral infiltration in high-grade gliomas, lymphomas, and pure vasogenic edema in metastases and meningiomas. MR imaging and DWI was performed on 93 patients with newly diagnosed brain tumors: 59 patients had histologically verified high-grade gliomas (37 glioblastomas multiforme, 22 anaplastic astrocytomas), 23 patients had metastatic brain tumors, five patients had primary cerebral lymphomas, and six patients had meningiomas. Apparent diffusion coefficient (ADC) values of tumor (enhancing regions or the solid portion of tumor) and peritumoral edema, and ADC ratios (ADC of tumor or peritumoral edema to ADC of contralateral white matter, ADC of tumor to ADC of peritumoral edema) were compared with the histologic diagnosis. ADC values and ratios of high-grade gliomas, primary cerebral lymphomas, metastases, and meningiomas were compared by using ANOVA and multiple comparisons. Optimal thresholds of ADC values and ADC ratios for distinguishing high-grade gliomas from metastases were determined by receiver operating characteristic (ROC) curve analysis. Statistically significant differences were found for minimum and mean of ADC tumor and ADC tumor ratio values between metastases and high-grade gliomas when including only one factor at a time. Including a combination of in total four parameters (mean ADC tumor, and minimum, maximum and mean ADC tumor ratio) resulted in sensitivity, specificity, positive (PPV), and negative predictive values (NPV) of 72.9, 82.6, 91.5, and 54.3% respectively. In the ROC curve analysis, the area under the curve of the combined four parameters was the largest (0.84), indicating a good test. Our results suggest that ADC values and ADC ratios (minimum and mean of ADC tumor and ADC tumor ratio) may be helpful in the differentiation of metastases from high-grade gliomas. It cannot distinguish high-grade gliomas from lymphomas, and lymphomas from metastases. ADC values and ADC ratios in peritumoral edema cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema when measurements for high-grade gliomas, lymphomas, metastases, and meningiomas were compared.

Application of apparent diffusion coefficient and fractional anisotropy in identification of tumor component and grading of brain astrocytoma

To evaluate the value of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in identification of tumor element and grading of brain astrocytoma. Thirty-three patients with histologically confirmed astrocytoma underwent diffusion weighted imaging (DWI), diffusion tensor imaging (DTI), and conventional MRI before operation. The values of ADC and FA of different regions in the same tumor and of astrocytoma of different grades were measured and compared. The ADC values of the tumor parenchyma, necrotic region, peritumoral edema region were (1.28 +/- 0.44), (1.97 +/- 0.53), and (1.74 +/- 0.47) respectively, all significantly higher than that of the corresponding normal brain tissues [(0.80 +/- 0.18), P = 0.009, P = 0.000, P = 0.000] with significantly differences between the tumor parenchyma and necrotic region and peritumoral edema region (both P < 0.05), however, there was not significant difference between the necrotic region and peritumoral edema region. The FA values of the tumor parenchyma, necrotic region, and peritumoral edema region were (0.18 +/- 0.07), (0.14 +/- 0.05), and (0.16 +/- 0.05) respectively, all significantly higher than that of the corresponding normal brain tissues [(0.58 +/- 0.10), all P = 0.000], without significant differences among the former 3 groups. There were no significant differences in the ADC and FA values among the tumors at different grades, however, there was a tendency of ADC to decrease and of FA to increase along the increase of grade of tumor, although not significantly. ADC value plays an important part in distinguishing tumor components and determining tumor boundary, and plays a certain role in judging the grade of astrocytomas. FA value is vital to determine the tumor boundary, and has certain value in differentiating high-grade from low-grade astrocytomas.

Implications of contrast-enhanced CT-based and MRI-based target volume delineations in radiotherapy treatment planning for brain tumors

Delineation of various target volumes using contrast-enhanced magnetic resonance imaging (MRI) and/or computed tomography (CT) constitutes the primary step for radiation therapy planning (RTP) in brain tumors. This study presents a quantification and comparative evaluation of the various clinical target volumes (CTV) and gross target volumes (GTV) as outlined by contrast-enhanced CT and MRI, along with its implications for postoperative radiotherapy of brain tumors. Twenty-one patients of gliomas were considered for this prospective study. Peritumoral edema as CTV and residual tumor as GTV were delineated separately in postoperative contrast-enhanced CT and MRI. These volumes were estimated separately and their congruence studied for contrast-enhanced CT and MRI. Compared to MRI, CT underestimated the volumes, with significant differences seen in the mean CTV (mean +/- SD: -62.92 +/- 93.99 cc; P = 0.006) and GTV (mean +/- SD: -21.08 +/- 36.04 cc; P = 0.014). These differences were found to be significant for high-grade gliomas (CTV: P = 0.045; GTV: P = 0.044), while they were statistically insignificant for low-grade gliomas (CTV: P = 0.080; GTV: P = 0.117). The mean differences in the volumes for CTV and GTV were estimated to be -106.7% and -62.6%, respectively, taking the CT volumes as the baseline. Thus, even though, electron density information from CT is essential for RTP, target delineation during postoperative radiotherapy of brain tumors, especially for high-grade tumors, should be based on MRI so as to avoid inadvertent geographical misses, especially in the regions of peritumoral edema.

Quantitative apparent diffusion coefficients and T2 relaxation times in characterizing contrast enhancing brain tumors and regions of peritumoral edema

To investigate the potential value and relationship of in vivo quantification of apparent diffusion coefficients (ADCs) and T2 relaxation times for characterizing brain tumor cellularity and tumor-related edema. A total of 26 patients with newly diagnosed gliomas, meningiomas, or metastases underwent diffusion-weighted and six-echo multisection T2-preparation imaging. Regions of interest (ROIs) were drawn on conventional MR images to include tumor (as defined by contrast agent enhancement) and immediate and peripheral edema. Areas of necrosis were excluded. Median values of ADCs and T2 in the ROIs were calculated. ADCs for gliomas were similar to those for meningiomas or metastases in all regions. Tumor T2 values for gliomas (159.5+/-30.6 msec) were significantly higher than those for meningiomas or metastases (125.0+/-31.1 msec; P=0.005). Immediate-edema T2 values for meningiomas or metastases (226.0+/-44.1 msec) were significantly higher than those for gliomas (203.5+/-32.8 msec; P=0.033). Peripheral-edema T2 values for gliomas (219.5+/-41.9 msec) were similar to those for meningiomas or metastases (202.5+/-26.5 msec; P=0.377). Both immediate- and peritumoral-edema ADCs and T2 values were significantly higher than those in tumor for both tumor types. ADCs and T2 values from all regions correlated significantly for gliomas (r=0.95; P<0.0001) and for meningiomas or metastases (r=0.81; P<0.0001). The higher immediate-edema T2 values for nonglial tumors than for gliomas suggest tumor-related edema (vasogenic vs. infiltrated) can be further characterized by using T2 values. There were significant correlations between ADC and T2 values.

The characteristics of cerebral meningiomas and surrounding tissues on dynamic CT

Dynamic CT was utilized to evaluate 11 patients with histologically benign meningiomas. While it was found that all demonstrated macroscopic neovascularity, subtle differences in the dynamic perfusion curves were identified both between different meningiomas and from region to region within the same tumor. Other than basic anatomic differences, these changes may reflect intratumoral ischemia and hypothetically herald cystic/necrotic alteration within the neoplasm. The dynamic calculations over the surrounding brain showed areas of gross hyper- and hypoperfused cerebral cortex, and hypoperfused white matter in regions of peritumoral edema. These latter findings are of uncertain clinical importance. The dynamic examination also confirmed cases of dural venous sinus invasion and calvarial permeation by tumor. In addition, the dynamic series showed macroscopic neovascularity in one case with a completely negative selective cerebral arteriogram. It is felt that certain cases which have previously been evaluated by static CT may benefit from further study utilizing the dynamic method.