Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (n = 248), a performance -on par with the interrater agreement between expert annotators as reported in literature. We obtained an EOR classification precision/recall of 0.72/0.78 on the internal test dataset (n = 462) and 0.90/0.87 on the external dataset. Furthermore, Kaplan-Meier curves were used to compare the overall survival between patients with maximal and submaximal resection in the internal test dataset, as determined by either clinicians or the model. There was no significant difference between the survival predictions using the model's and clinical EOR classification. We find that the proposed segmentation model is capable of reliably classifying the EOR of glioblastoma tumors on early post-operative MRI scans. Moreover, we show that stratification of patients based on the model's predictions offers at least the same prognostic value as when done by clinicians.

Quantification of Tissue Compression Identifies High-Grade Glioma Patients with Reduced Survival.

The compression of peritumoral healthy tissue in brain tumor patients is considered a major cause of the life-threatening neurologic symptoms. Although significant deformations caused by the tumor growth can be observed radiologically, the quantification of minor tissue deformations have not been widely investigated. In this study, we propose a method to quantify subtle peritumoral deformations. A total of 127 MRI longitudinal studies from 23 patients with high-grade glioma were included. We estimate longitudinal displacement fields based on a symmetric normalization algorithm and we propose four biomarkers. We assess the interpatient and intrapatient association between proposed biomarkers and the survival based on Cox analyses, and the potential of the biomarkers to stratify patients according to their survival based on Kaplan−Meier analysis. Biomarkers show a significant intrapatient association with survival (p < 0.05); however, only compression biomarkers show the ability to stratify patients between those with higher and lower overall survival (AUC = 0.83, HR = 6.30, p < 0.05 for CompCH). The compression biomarkers present three times higher Hazard Ratios than those representing only displacement. Our study provides a robust and automated method for quantifying and delineating compression in the peritumoral area. Based on the proposed methodology, we found an association between lower compression in the peritumoral area and good prognosis in high-grade glial tumors.

Operator dependency of arterial input function in dynamic contrast-enhanced MRI.

OBJECTIVE: To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas. METHODS: The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in Ktrans was assessed by Fleiss' kappa (κ). RESULTS: There was a moderate to substantial agreement (ICC 0.51-0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77-0.95 for Ktrans, kep and ve and ICC 0.70 for vp. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in Ktrans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement. DISCUSSION: AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy.

BACKGROUND: The aim of this study was to investigate changes in structural magnetic resonance imaging (MRI) according to the RANO criteria and perfusion- and permeability related metrics derived from dynamic contrast-enhanced MRI (DCE) and dynamic susceptibility contrast MRI (DSC) during radiochemotherapy for prediction of progression and survival in glioblastoma. METHODS: Twenty-three glioblastoma patients underwent biweekly structural and perfusion MRI before, during, and two weeks after a six weeks course of radiochemotherapy. Temporal trends of tumor volume and the perfusion-derived parameters cerebral blood volume (CBV) and blood flow (CBF) from DSC and DCE, in addition to contrast agent capillary transfer constant (Ktrans) from DCE, were assessed. The patients were separated in two groups by median survival and differences between the two groups explored. Clinical- and MRI metrics were investigated using univariate and multivariate survival analysis and a predictive survival index was generated. RESULTS: Median survival was 19.2 months. A significant decrease in contrast-enhancing tumor size and CBV and CBF in both DCE- and DSC-derived parameters was seen during and two weeks past radiochemotherapy (p < 0.05). A 10%/30% increase in Ktrans/CBF two weeks after finishing radiochemotherapy resulted in significant shorter survival (13.9/16.8 vs. 31.5/33.1 months; p < 0.05). Multivariate analysis revealed an index using change in Ktrans and relative CBV from DSC significantly corresponding with survival time in months (r2 = 0.843; p < 0.001). CONCLUSIONS: Significant temporal changes are evident during radiochemotherapy in tumor size (after two weeks) and perfusion-weighted MRI-derived parameters (after four weeks) in glioblastoma patients. While DCE-based metrics showed most promise for early survival prediction, a multiparametric combination of both DCE- and DSC-derived metrics gave additional information.

T1 in high-grade glioma and the influence of different measurement strategies on parameter estimations in DCE-MRI.

BACKGROUND: To investigate the evolution of T1 in tumor and the necessity of baseline T1 (T1 (0)) mapping for the accurate estimation of kinetic parameters during standard therapy using dynamic contrast enhanced (DCE) MRI in patients with high-grade glioma (HGG). METHODS: Longitudinal DCE-MRI was performed in 23 patients (196 scans) with confirmed HGG. Kinetic parameters were derived from the extended Tofts model and analyzed both using fixed and pixel-wise T1 (0) values estimated from a Look-Locker sequence. Median tumor T1 value from all scans was used for fixed T1 (0) analysis. Dependence of accurate T1 (0) mapping for the estimation of the kinetic parameters was further investigated through computer simulations and histogram analysis. RESULTS: T1 in tumor increased significantly during and after treatment (P < 0.001). There was a linear correlation between the error in 1/T1 (0) and the resulting error in estimated parameters in both simulations and clinical data (r(2) >0.98 for all parameters). A strong correlation between the estimated longitudinal change in all kinetic parameters obtained using pixel-wise and fixed T1 (0) was observed (r(2) > 0.84 for all parameters). Histogram analysis revealed a linear change (r(2) > 0.62) in K(trans) normalized histogram peak height ratio as function of percentage deviation from the nominal T1 (0) value. No effect of T1 (0) histogram distribution on K(trans) was observed (P = 0.52). CONCLUSION: Temporal changes in the median kinetic parameters in tumor were equally well described using pixel-wise and fixed T1 (0) despite an increase in T1 (0) over time, suggesting that T1 mapping is not generally required in DCE-MRI based monitoring of glioma patients.

Serial diffusion tensor imaging for early detection of radiation-induced injuries to normal-appearing white matter in high-grade glioma patients.

PURPOSE: To study the potential of diffusion tensor imaging (DTI) to serve as a biomarker for radiation-induced brain injury during chemo-radiotherapy (RT) treatment. MATERIALS AND METHODS: Serial DTI data were collected from 18 high-grade glioma (HGG) patients undergoing RT and 7 healthy controls. Changes across time in mean, standard deviation (SD), skewness, and kurtosis of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λa ), and transversal diffusivity (λt ) within the normal-appearing white matter (NAWM) were modeled using a linear mixed-effects model to assess dose dependent changes of five dose bins (0-60 Gy), and global changes compared with a control group. RESULTS: Mean MD, λa and λt were all significantly increasing in >41 Gy dose regions (0.14%, 0.10%, and 0.18% per week) compared with <12 Gy regions. SD λt had significant dose dependent time evolution of 0.019*dose per week. Mean and SD MD, λa and λt in the global NAWM of the patient group significantly increased (mean; 0.06%, 0.03%, 0.09%, and SD; 0.57%, 0.34%, 0.51 per week) compared with the control group. The changes were significant at week 6 of, or immediately after RT. CONCLUSION: DTI is not sensitive to acute global NAWM changes during the treatment of HGG, but sensitive to early posttreatment changes.

Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth.

BACKGROUND: Volumetric magnetic resonance imaging (MRI) is now widely available and routinely used in the evaluation of high-grade gliomas (HGGs). Ideally, volumetric measurements should be included in this evaluation. However, manual tumor segmentation is time-consuming and suffers from inter-observer variability. Thus, tools for semi-automatic tumor segmentation are needed. PURPOSE: To present a semi-automatic method (SAM) for segmentation of HGGs and to compare this method with manual segmentation performed by experts. The inter-observer variability among experts manually segmenting HGGs using volumetric MRIs was also examined. MATERIAL AND METHODS: Twenty patients with HGGs were included. All patients underwent surgical resection prior to inclusion. Each patient underwent several MRI examinations during and after adjuvant chemoradiation therapy. Three experts performed manual segmentation. The results of tumor segmentation by the experts and by the SAM were compared using Dice coefficients and kappa statistics. RESULTS: A relatively close agreement was seen among two of the experts and the SAM, while the third expert disagreed considerably with the other experts and the SAM. An important reason for this disagreement was a different interpretation of contrast enhancement as either surgically-induced or glioma-induced. The time required for manual tumor segmentation was an average of 16 min per scan. Editing of the tumor masks produced by the SAM required an average of less than 2 min per sample. CONCLUSION: Manual segmentation of HGG is very time-consuming and using the SAM could increase the efficiency of this process. However, the accuracy of the SAM ultimately depends on the expert doing the editing. Our study confirmed a considerable inter-observer variability among experts defining tumor volume from volumetric MRIs.

T2*-correction in dynamic contrast-enhanced MRI from double-echo acquisitions.

PURPOSE: To evaluate the importance of T2*-effects on the arterial input function (AIF) and on the resulting dynamic parameter estimation in dynamic contrast-enhanced (DCE) MRI of high-grade gliomas. MATERIALS AND METHODS: Seven patients with high-grade gliomas were imaged in total 50 times using a double-echo DCE sequence. Kinetic analysis using the extended Tofts model was performed using AIFs with and without correction for T2*-effects, and the resulting estimates of the transfer constant (K(trans) ), blood plasma volume (vp ), and the rate constant (kep ) were compared. Numerical simulations were done for comparison with clinical results as well as to further investigate the dependency of parameter values on the magnitude of T2*-induced errors. RESULTS: All kinetic parameters were found to be overestimated if T2*-effects in the AIF were not accounted for; with vp being most severely affected. The relative error in each parameter was dependent on the absolute parameter magnitude, resulting in incorrect parametric tumor distributions in the presence of uncorrected AIF T2*-effects. CONCLUSION: In DCE, a sufficiently short echo time should be used or corrections for T2*-effects based on double-echo acquisition should be made for correct quantification of kinetic parameters.

Correction of B0-distortions in echo-planar-imaging-based perfusion-weighted MRI.

PURPOSE: To evaluate and quantify a scheme for correcting susceptibility artifacts in spin-echo echo-planar-imaging-based dynamic susceptibility contrast (DSC) perfusion MRI of high-grade gliomas at 3 Tesla. MATERIALS AND METHODS: Sixteen patients with a total of 78 scans were studied. DSC-MRI images were corrected using a displacement map generated from opposite phase-encoding polarity images. Two methods were used for quantification in the correction: (i) linear regression of pixel-by-pixel comparisons, performed both globally and relative to the anterior and posterior commissure plane (AC-PC plane), of T2-weighted images with both corrected and uncorrected raw DSC images; and (ii) counting significant (>2.0) normalized cerebral blood volume (nCBV) pixels from perfusion maps in the tumor region of interest. RESULTS: Sixty-four of 78 datasets showed significant differences in the coefficient of correlation (r2) values. The difference between corrected and uncorrected r2 values was positive in all but one patient. Correction of B0- distortion significantly improved r2 in slices around the AC-PC plane. In 62% of the datasets, we observed an increased number of significant pixels in the corrected nCBV maps; 36% showed more significant pixels in uncorrected nCBV maps; 1% showed no difference. CONCLUSION: Distortion correction of DSC-MRI may provide improved accuracy compared with uncorrected data, especially for tumors located below the corpus callosum and near the frontal sinuses.

Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results.

PURPOSE: To investigate the effect of variations in temporal resolution and total measurement times on the estimations of kinetic parameters derived from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas (HGGs). MATERIALS AND METHODS: DCE-MRI with high temporal resolution (dynamic sampling time (T(s)) = 2.1 s and 3.4 s) and total sampling time (T(acq)) of 5.2 min was acquired in 101 examinations from 15 patients. Using the modified Tofts model K(trans), k(ep) v(e) and v(p) were estimated. The effects of increasing T(s) and reducing T(acq) on the estimated kinetic parameters were estimated through down-sampling and data truncation, and the results were compared with numerical simulations. RESULTS: There was an overall dependence of all four kinetic parameters on T(s) and T(acq). Increasing T(s) resulted in under-estimation of K(trans) and over-estimation of V(p), whereas k(ep) and V(e) varied in a less predictable manner. Reducing T(acq) resulted in over-estimation of K(trans) and k(ep) and under-estimation of v(p) and v(e). Increasing T(s) and reducing T(acq) resulted in increased relative error for all four parameters. CONCLUSION: Estimated K(trans), K(ep), and V(e) in HGGs were within 15% of the high sampling rate reference values for T(s) <20 s. Increasing T(s) and reducing T(acq) leads to reduced precision of the estimated values.