Absolute CBV for the differentiation of recurrence and radionecrosis of brain metastases after gamma knife radiotherapy: a comparison with relative CBV

Perfusion MRI in the evaluation of brain metastases: current practice review and rationale for study of baseline MR perfusion imaging prior to stereotactic radiosurgery (STARBEAM-X)

Stereotactic radiosurgery is an established focal treatment for brain metastases with high local control rates. An important side-effect of stereotactic radiosurgery is the development of radionecrosis. On conventional MR imaging, radionecrosis and tumour progression often have similar appearances, but have contrasting management approaches. Perfusion MR imaging is often used in the post-treatment setting in order to help distinguish between the two, but image interpretation can be fraught with challenges.Perfusion MR plays an established role in the baseline and post-treatment evaluation of primary brain tumours and a number of studies have concentrated on the value of perfusion imaging in brain metastases. Of the parameters generated, relative cerebral blood volume is the most widely used variable in terms of its clinical value in differentiating between radionecrosis and tumour progression. Although it has been suggested that the relative cerebral blood volume tends to be elevated in active metastatic disease following treatment with radiosurgery, but not with treatment-related changes, the literature available on interpretation of the ratios provided in the context of defining tumour progression is not consistent.This article aims to provide an overview of the role perfusion MRI plays in the assessment of brain metastases and introduces the rationale for the STARBEAM-X study (Study of assessment of radionecrosis in brain metastases using MR perfusion extra imaging), which will prospectively evaluate baseline perfusion imaging in brain metastases. We hope this will allow insight into the vascular appearance of metastases from different primary sites, and aid in the interpretation of post-treatment perfusion imaging.

Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases

BACKGROUND

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) could be helpful to separate true disease progression from pseudo-progression in brain metastases when assessing the need for retreatment. However, the selection of arterial input functions (AIFs) is not standardized for analysis, limiting its use for this application.

PURPOSE

To compare population-based AIFs, AIFs specific to each patient, and AIFs specific to every visit in the longitudinal follow-up of brain metastases.

MATERIAL AND METHODS

Longitudinal data were collected from eight patients before treatment (6 of 8 patients) and after treatment (6-17 visits). Imaging was performed using a 1.5-T MRI system. Lesions were segmented by subtracting precontrast images from postcontrast images. Cerebral blood volume (rCBV) and cerebral blood flow (rCBF) were computed, and Pearson's product moment correlation coefficients were calculated to evaluate similarity of DSC parameters dependent on various AIF choices across time. AIF shape characteristics were compared. Parameter differences between white matter (WM) and gray matter (GM) were obtained to determine which AIF choice maximizes tissue differentiation.

RESULTS

Although DSC parameters follow similar patterns in time, the various AIF selections cause large parameter variations with relative standard deviations of up to ±60%. AIFs sampled in one patient across sessions more similar in shape than AIFs sampled across patients. Estimates of rCBV based on scan-specific AIFs differentiated better between perfusion in WM and GM than patient-specific or population-based AIFs (P ≤ 0.02).

CONCLUSION

Results indicate that scan-specific AIFs are the best choice for DSC-MRI parameter estimations in the longitudinal follow-up of brain metastases.

Diagnostic accuracy of MRI techniques for treatment response evaluation in patients with brain metastasis: A systematic review and meta-analysis

BACKGROUND

Treatment response assessment in patients with brain metastasis uses contrast enhanced T1-weighted MRI. Advanced MRI techniques have been studied, but the diagnostic accuracy is not well known. Therefore, we performed a metaanalysis to assess the diagnostic accuracy of the currently available MRI techniques for treatment response.

METHODS

A systematic literature search was done. Study selection and data extraction were done by two authors independently. Meta-analysis was performed using a bivariate random effects model. An independent cohort was used for DSC perfusion external validation of diagnostic accuracy.

RESULTS

Anatomical MRI (16 studies, 726 lesions) showed a pooled sensitivity of 79% and a specificity of 76%. DCE perfusion (4 studies, 114 lesions) showed a pooled sensitivity of 74% and a specificity of 92%. DSC perfusion (12 studies, 418 lesions) showed a pooled sensitivity was 83% with a specificity of 78%. Diffusion weighted imaging (7 studies, 288 lesions) showed a pooled sensitivity of 67% and a specificity of 79%. MRS (4 studies, 54 lesions) showed a pooled sensitivity of 80% and a specificity of 78%. Combined techniques (6 studies, 375 lesions) showed a pooled sensitivity of 84% and a specificity of 88%. External validation of DSC showed a lower sensitivity and a higher specificity for the reported cut-off values included in this metaanalysis.

CONCLUSION

A combination of techniques shows the highest diagnostic accuracy differentiating tumor progression from treatment induced abnormalities. External validation of imaging results is important to better define the reliability of imaging results with the different techniques.

Reproducibility of volume analysis of dynamic susceptibility contrast perfusion-weighted imaging in untreated glioblastomas

PURPOSE

Despite a high variability, the hotspot method is widely used to calculate the cerebral blood volume (CBV) of glioblastomas on DSC-MRI. Our aim was to investigate inter- and intra-observer reproducibility of parameters calculated with the hotspot or a volume method and that of an original parameter assessing the fraction of pixels in the tumour volume displaying rCBV > 2: %rCBV > 2.

METHODS

Twenty-seven consecutive patients with untreated glioblastoma (age: 63, women: 11) were retrospectively included. Three observers calculated the maximum tumour CBV value (rCBVmax) normalized with a reference ROI in the contralateral white matter (CBVWM) with (i) the hotspot method and (ii) with a volume method following tumour segmentation on 3D contrast-enhanced T1-WI. From this volume method, %rCBV > 2 was also assessed. After 8-12 weeks, one observer repeated all delineations. Intraclass (ICC) and Lin's (LCC) correlation coefficients were used to determine reproducibility.

RESULTS

Inter-observer reproducibility of rCBVmax was fair with the hotspot and good with the volume method (ICC = 0.46 vs 0.65, p > 0.05). For CBVWM, it was fair with the hotspot and excellent with the volume method (0.53 vs 0.84, p < 0.05). Reproducibility of one pairwise combination of observers was significantly better for both rCBVmax and CBVWM (LCC = 0.33 vs 0.75; 0.52 vs 0.89, p < 0.05). %rCBV > 2 showed excellent inter- and intra-observer reproducibility (ICC = 0.94 and 0.91).

CONCLUSION

Calculated in glioblastomas with a volume method, rCBVmax and CBVWM yielded good to excellent reproducibility but only fair with the hotspot method. Overall, the volume analysis offers a highly reproducible parameter, %rCBV > 2, that could be promising during the follow-up of such heterogeneous tumours.

Quantitative MR Perfusion for the Differentiation of Recurrence and Radionecrosis in Hypoperfusion and Hyperperfusion Brain Metastases After Gamma Knife Radiosurgery

Objectives

Dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) plays an important role in the differential diagnosis between radionecrosis and recurrence of brain metastases (BMs) after gamma knife radiosurgery (GKRS). While the perfusion condition of preliminary hyperperfusion and hypoperfusion BMs when recur has not been studied, as well the separating performance of quantitative DSC-PWI in both kinds of BMs.

Methods

From February 2017 to October 2019, quantitative DSC-PWI was performed in patients with untreated BMs in this observational study. Patients were assigned to hyperperfusion and hypoperfusion group according the quantitative cerebral blood volume (qCBV). During follow-up after GKRS, patients with a diagnostic pitfall of radionecrosis and recurrence accepted second quantitative DSC-PWI. Final diagnosis was based on the histological results or follow-up results. Receiver operating curve analysis was used to explore the performance of qCBV.

Results

Twenty-nine patients (mean age: 61.3 ± 9.4 years old; male/female: 13/16) were assigned to the group of hypoperfusion group, and 26 patients (mean age: 58 ± 10.4 years old; male/female: 14/12) to hyperperfusion group. The mean qCBV values between hypoperfusion and hyperperfusion groups when recurred were not significantly different (3.17 ± 0.53 ml/100 g vs. 3.27 ± 0.47 ml/100 g, p = 0.63). qCBV was feasible to separate radionecrosis and recurrence in both groups (AUC=0.94 and AUC=0.93, separately).

Conclusion

Both premilitary hyperperfusion and hypoperfusion BMs would transform to a high microvascular density when recurs. qCBV is feasible to distinguish radionecrosis and recurrence among both kinds of BMs after GKRS.

Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

Radiomics Method for the Differential Diagnosis of Radionecrosis Versus Progression after Fractionated Stereotactic Body Radiotherapy for Brain Oligometastasis

Stereotactic radiotherapy (SRT) is recommended for treatment of brain oligometastasis (BoM) in patients with controlled primary disease. Where contrast enhancement enlargement occurs during follow-up, distinguishing between radionecrosis and progression presents a critical challenge. Without pathological confirmation, decision-making may be inappropriate and delayed. Quantitative imaging features extracted from routinely performed examinations are of interest in potentially addressing this problem. We explored the added value of the radiomics method for the differential diagnosis of these two entities. Twenty patients who received SRT for BoM, from any primary location, were included (8 radionecrosis, 12 progressions, pathologically confirmed). We assessed the clinical relevance of 1,766 radiomics features, extracted using IBEX software, from the first T1-weighted postcontrast magnetic resonance imaging (MRI) after SRT showing a lesion modification. We evaluated seven feature-selection methods and 12 classification methods in terms of respective predictive performance. The classification accuracy was measured using Cohen's kappa after leave-one-out cross-validation. In this work, the best predictive power reached was a Cohen's kappa of 0.68 (overall accuracy of 85%), expressing a strong agreement between the algorithm prediction and the histological gold standard. Prediction accuracy was 75% for radionecrosis, and 91% for progression. The area under a curve reached 0.83 using a bagging algorithm trained with the chi-square score features set. These findings indicated that the radiomics method is able to discriminate radionecrosis from progression in an accurate, early and noninvasive way. This promising study is a proof of concept, preceding a larger prospective study for defining a robust model to support decision-making in BoM. In summary, distinguishing between radionecrosis and progression is challenging without pathology. We built a classification model based on imaging data and machine learning. Using this model, we were able predict progression and radionecrosis in, respectively, 91% and 75% of cases.

Measurements of cerebral blood volume using quantitative susceptibility mapping, R2 * relaxometry, and ferumoxytol-enhanced MRI

Ferumoxytol-enhanced MRI holds potential for the non-invasive assessment of vascular architecture using estimates of cerebral blood volume (CBV). Ferumoxytol specifically enables steady-state imaging with extended acquisition times, for substantial improvements in resolution and contrast-to-noise ratio. With such data, quantitative susceptibility mapping (QSM) can be used to obtain images of local tissue magnetic susceptibility and hence estimate the increase in blood susceptibility after administration of a contrast agent, which in turn can be correlated to tissue CBV. Here, we explore the use of QSM for CBV estimation and compare it with R₂ * (1/T₂ *)-based results. Institutional review board approval was obtained, and all subjects provided written informed consent. For this prospective study, MR images were acquired on a 3.0 T scanner in 19 healthy subjects using a multiple-echo T₂ *-weighted sequence. Scanning was performed before and after the administration of two doses of ferumoxytol (1 mg FE/kg and 4 mg FE/kg). Different QSM approaches were tested on numerical phantom simulations. Results showed that the accuracy of magnetic susceptibility measurements improved with increasing image resolution and decreasing vascular density. In vivo changes in magnetic susceptibility were measured after the administration of ferumoxytol utilizing QSM, and significantly higher QSM-based CBV was measured in gray matter compared with white matter. QSM- and R₂ *-based CBV estimates correlated well, with similar average values, but a larger variance was found in QSM-based estimates.

Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis

Glioblastoma multiforme (GBM) is difficult to be separated from solitary brain metastasis (sBM) in clinical practice. This study aimed to distinguish two entities by the histogram analysis of absolute cerebral blood volume (CBV) map.From March 2016 to June 2018, 24 patients with GBM and 18 patients with sBM were included in this retrospective study. The enhancing area was first segmented on the post-contrast T1WI, then the segmentation was copied to the absolute CBV map and histogram analysis was finally performed. Unpaired t test was used to select the features that could separate two entities and receiving operating curve was used to test the diagnostic performance. Finally, a machine learning method was used to test the diagnostic performance combing all the selected features.Six of 19 features were feasible to distinguish GBM from sBM (all P < .001), among which energy had the highest diagnostic performance (area under curve, 0.84; accuracy, 88%), while a machine learning method could improve the diagnostic performance (area under curve, 0.94; accuracy, 95%).Histogram analysis of the absolute CBV in the enhancing area could help us distinguish GBM from sBM, in addition, a machine learning method with combined features is preferable. It is quite helpful in the condition that the biological nature of peritumoral edema could not separate these two entities.

Dynamic susceptibility MR perfusion in diagnosing recurrent brain metastases after radiotherapy: A systematic review and meta-analysis

Background

The diagnostic performance of dynamic susceptibility contrast (DSC) MR perfusion in discriminating treatment‐related changes from recurrence in irradiated brain metastases is currently not completely clear.

Purpose

To systematically review the accuracy of DSC MR perfusion in diagnosing recurrent brain metastases after radiotherapy.

Study Type

Systematic review and meta‐analysis.

Subjects

MEDLINE and Embase were searched for original studies investigating the accuracy of DSC MR perfusion in diagnosing recurrent brain metastases after radiotherapy. Ten studies, comprising a total of more than 271 metastases, were included.

Field Strength/Sequence

1.5T or 3.0T, DSC MR perfusion.

Assessment

Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies‐2 tool.

Statistical Tests

Sensitivity and specificity were pooled with a bivariate random‐effects model. Heterogeneity was assessed by a chi‐squared test. Potential sources for heterogeneity were explored by subgroup analyses.

Results

In seven studies the diagnostic criterion was not prespecified. In eight studies it was unclear whether the reference standard was interpreted blindly. In seven studies it was unclear whether DSC MR perfusion results influenced which reference standard was used. Pooled sensitivity and specificity were 81.6% (95% confidence interval [CI]: 70.6%, 89.1%) and 80.6% (95% CI: 64.2%, 90.6%), respectively. There was significant heterogeneity in both sensitivity (P = 0.005) and specificity (P < 0.001). There were no significant differences in relative diagnostic odds ratio according to publication year, country of origin, study size, and DSC MR perfusion interpretation method (visual analysis of cerebral blood volume [CBV] map vs. relative CBV measurement) (P > 0.2). Due to insufficiently detailed reporting, it was not possible to investigate the influence of primary tumor origin on accuracy.

Data Conclusion

Our results suggest that the accuracy of DSC MR perfusion in diagnosing recurrent brain metastases after radiotherapy is fairly high. However, these findings should be interpreted with caution because of methodological quality concerns and heterogeneity between studies.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2020;51:524–534.