Differentiation between glioblastomas and brain metastases and regarding their primary site of malignancy using dynamic susceptibility contrast MRI at 3T

Patterns of abnormal magnetic susceptibility in the brain: an image-based review

PURPOSE

This article is designed to facilitate a systematic approach to formulating a radiologic differential diagnosis based on the pattern of abnormal magnetic susceptibility on MRI. Susceptibility-weighted imaging (SWI) is a three-dimensional sequence with excellent spatial resolution and superior contrast resolution. It originated from and has largely replaced two-dimensional T2* weighted sequences. Currently, SWI refers to any high-spatial resolution susceptibility-enhanced sequence from different MR vendors.

METHODS

There are many entities that have specific patterns unique on SWI. We chose both entities that are commonly encountered in the clinical practice and unusual entities that may present as challenges in making the diagnosis. Each entity is discussed in detail, focusing on salient imaging features seen with SWI and key differences highlighted from other entities.

RESULTS

In the first category, lesions with randomly distributed susceptibility abnormalities are described. Further classification is made based on the presence of foci of susceptibility located diffusely through the brain (e.g. diffuse axonal injury) noting the subtleties of the shape, size, and preferential distribution of these foci. Special attention is also directed toward entities that, while random in location, are associated with a specific lesion (e.g. abscess). Finally, clues to correctly diagnose the various pathologies are provided. In the second category, the focus is on lesions that can be classified based upon anatomic locations whether peripheral (e.g. sulcal hemosiderin) versus central distribution.

CONCLUSION

Knowledge of the patterns of susceptibility on SWI and the physics behind this technique are essential for facilitating MR interpretation.

Multisite Benchmark Study for Standardized Relative CBV in Untreated Brain Metastases Using the DSC-MRI Consensus Acquisition Protocol

BACKGROUND AND PURPOSE

A national consensus recommendation for the collection of DSC-MRI perfusion data, used to create maps of relative CBV (rCBV), has been recently established for primary and metastatic brain tumors. The goal was to reduce intersite variability and improve ease of comparison across time and sites, fostering widespread use of this informative measure. To translate this goal into practice, the prospective collection of consensus DSC-MRI data and characterization of derived rCBV maps in brain metastases is needed. The purpose of this multisite study was to determine rCBV in untreated brain metastases in comparison to glioblastoma (GBM) and normal-appearing brain by using the national consensus protocol.

MATERIALS AND METHODS

Subjects from 3 sites with untreated enhancing brain metastases underwent DSC-MRI according to a recommended option that uses a midrange flip angle, GRE-EPI acquisition, and the administration of both a preload and second DSC-MRI dose of 0.1 mmol/kg gadolinium-based contrast agent. Quantitative maps of standardized relative CBV (srCBV) were generated and enhancing lesion ROIs determined from postcontrast T1-weighted images alone or calibrated difference maps, termed Δ T1 (dT1) maps. Mean srCBV for metastases were compared with normal-appearing white matter (NAWM) and GBM from a previous study. Comparisons were performed by using either the Wilcoxon signed-rank test for paired comparisons or the Mann-Whitney U nonparametric test for unpaired comparisons.

RESULTS

Forty-nine patients with a primary histology of lung (n = 25), breast (n = 6), squamous cell carcinoma (n = 1), melanoma (n = 5), gastrointestinal (GI) (n = 3), and genitourinary (GU) (n = 9) were included in comparison to GBM (n = 31). The mean srCBV of all metastases (1.83±1.05) were significantly lower (P = .0009) than mean srCBV for GBM (2.67 ± 1.34) with both statistically greater (P < .0001) than NAWM (0.68 ± 0.18). Histologically distinct metastases are each statistically greater than NAWM (P < .0001) with lung (P = .0002) and GU (P = .02) srCBV being significantly different from GBM srCBV.

CONCLUSIONS

Using the consensus DSC-MRI protocol, mean srCBV values were determined for treatment-naïve brain metastases in comparison to normal-appearing white matter and GBM thus setting the benchmark for all subsequent studies adherent to the national consensus recommendation.

Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques

BACKGROUND

Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear.

PURPOSE

To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation.

STUDY TYPE

Systematic review and meta-analysis.

POPULATION

Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488).

FIELD STRENGTH/SEQUENCE

Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T.

ASSESSMENT

Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs.

STATISTICAL TESTS

Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant.

RESULTS

HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08).

DATA CONCLUSION

ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible.

LEVEL OF EVIDENCE: 1

TECHNICAL EFFICACY

Stage 2.

Advances in Glioblastoma Diagnosis: Integrating Genetics, Noninvasive Sampling, and Advanced Imaging

Glioblastoma is the most common primary brain tumor in adult patients, and despite standard-of-care treatment, median survival has remained less than two years. Advances in our understanding of molecular mutations have led to changes in the diagnostic criteria of glioblastoma, with the WHO classification integrating important mutations into the grading system in 2021. We sought to review the basics of the important genetic mutations associated with glioblastoma, including known mechanisms and roles in disease pathogenesis/treatment. We also examined new advances in image processing as well as less invasive and noninvasive diagnostic tools that can aid in the diagnosis and surveillance of those undergoing treatment for glioblastoma. Our review is intended to serve as an overview of the current state-of-the-art in the diagnosis and management of glioblastoma.

Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases

Background: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window of opportunity for reframing treatment strategies enabling optimal and timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived from diffusion tensor imaging (DTI) and dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI) along with machine learning-based methods to distinguish GBMs from single BMs. Methods: Patients with histopathology-confirmed GBMs (n = 62) and BMs (n = 26) and exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI and DSC-PWI prior to treatment. Median values of mean diffusivity (MD), fractional anisotropy, linear, planar and spheric anisotropic coefficients, and relative cerebral blood volume (rCBV) and maximum rCBV values were measured from CERs and immediate peritumor regions. Data normalization and scaling were performed. In the next step, most relevant features were extracted (non-interacting features), which were subsequently used to generate a set of new, innovative, high-order features (interacting features) using a feature engineering method. Finally, 10 machine learning classifiers were employed in distinguishing GBMs and BMs. Cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance. Results: A random forest classifier with ANOVA F-value feature selection algorithm using both interacting and non-interacting features provided the best diagnostic performance in distinguishing GBMs from BMs with an area under the ROC curve of 92.67%, a classification accuracy of 87.8%, a sensitivity of 73.64% and a specificity of 97.5%. Conclusions: A machine learning based approach involving the combined use of interacting and non-interacting physiological MRI parameters shows promise to differentiate between GBMs and BMs with high accuracy.

Radiosurgery-induced early changes in peritumoral tissue sodium concentration of brain metastases

BACKGROUND

Stereotactic radiosurgery (SRS) is an effective therapy for brain metastases. Response is assessed with serial 1H magnetic resonance imaging (MRI). Early markers for response are desirable to allow for individualized treatment adaption. Previous studies indicated that radiotherapy might have impact on tissue sodium concentration. Thus, 23Na MRI could provide early quantification of response to SRS.

PURPOSE

We investigated whether longitudinal detection of tissue sodium concentration alteration within brain metastases and their peritumoral tissue after SRS with 23Na MRI was feasible.

STUDY TYPE

Prospective.

POPULATION

Twelve patients with a total of 14 brain metastases from various primary tumors.

ASSESSMENT

23Na MRI scans were acquired from patients 2 days before, 5 days after, and 40 days after SRS. Gross tumor volume (GTV) and healthy-appearing regions were manually segmented on the MPRAGE obtained 2 days before SRS, onto which all 23Na MR images were coregistered. Radiation isodose areas within the peritumoral tissue were calculated with the radiation planning system. Tissue sodium concentration before and after SRS within GTV, peritumoral tissue, and healthy-appearing regions as well as the routine follow-up with serial MRI were evaluated.

STATISTICAL TESTS

Results were compared using Student's t-test and correlation was evaluated with Pearson's correlation coefficient.

RESULTS

We found a positive correlation between the tissue sodium concentration within the peritumoral tissue and radiation dosage. Two patients showed local progression and a differing tissue sodium concentration evolution within GTV and the peritumoral tissue compared to mean tissue sodium concentration of the other patients. No significant tissue sodium concentration changes were observed within healthy-appearing regions.

CONCLUSION

Tissue sodium concentration assessment within brain metastases and peritumoral tissue after SRS with 23Na MRI is feasible and might be able to quantify tissue response to radiation.

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.

Combining amide proton transfer-weighted and arterial spin labeling imaging to differentiate solitary brain metastases from glioblastomas

PURPOSE

To evaluate the clinical utility of amide proton transfer-weighted imaging (APTw) and arterial spin labeling (ASL) in differentiating solitary brain metastases (SBMs) from glioblastomas (GBMs).

METHODS

Forty-eight patients diagnosed with brain tumors were enrolled. All patients underwent conventional MRI, APTw, and ASL scans on a 3.0 T MRI system. The mean APTw value and mean cerebral blood flow (CBF) value were measured. The differences in various parameters between GBMs and SBMs were assessed using the independent-samples t-test. The quantitative performance of these MRI parameters in distinguishing between GBMs and SBMs was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

GBMs exhibited significantly higher APTw and CBF values in peritumoral regions compared with SBMs (P < 0.05). There was no significant difference between SBMs and GBMs in tumor cores. APTw MRI had a higher diagnostic efficiency in differentiating SBMs from GBMs (area under the curve [AUC]: 0.864; 75.0% sensitivity and 81.8% specificity). Combined use of APTw and CBF value increased the AUC to 0.927.

CONCLUSION

APTw may be superior to ASL for distinguishing between SBMs and GBMs. Combination of APTw and ASL showed better discrimination and a superior diagnostic performance.

Differentiation of high grade glioma and solitary brain metastases by measuring relative cerebral blood volume and fractional anisotropy: a systematic review and meta-analysis of MRI diagnostic test accuracy studies

OBJECTIVE

This study aims to research the efficacy of MRI (I) for differentiating high-grade glioma (HGG) (P) with solitary brain metastasis (SBM) (C) by creating a combination of relative cerebral blood volume (rCBV) (O) and fractional anisotropy (FA) (O) in patients with intracerebral tumors.

METHODS

Searches were conducted on September 2021 with no publication date restriction, using an electronic search for related articles published in English, from PubMed (1994 to September 2021), Scopus (1977 to September 2021), Web of Science (1985 to September 2021), and Cochrane (1997 to September 2021). A total of 1056 studies were found, with 23 used for qualitative and quantitative data synthesis. Inclusion criteria were: patients diagnosed with HGG and SBM without age, sex, or race restriction; MRI examination of rCBV and FA; reliable histopathological diagnostic method as the gold-standard for all conditions of interest; observational and clinical studies. Newcastle-Ottawa quality assessment Scale (NOS) and Cochrane risk of bias tool (ROB) for observational and clinical trial studies were managed to appraise the quality of individual studies included. Data extraction results were managed using Mendeley and Excel, pooling data synthesis was completed using the Review Manager 5.4 software with random effect model to discriminate HGG and SBM, and divided into four subgroups.

RESULTS

There were 23 studies included with a total sample size of 597 HGG patients and 373 control groups/SBM. The analysis was categorized into four subgroups: (1) the subgroup with rCBV values in the central area of the tumor/intratumoral (399 HGG and 232 SBM) shows that HGG patients are not significantly different from SBM/controls group (SMD [95% CI] = -0.27 [-0.66, 0.13]), 2) the subgroup with rCBV values in the peritumoral area (452 HGG and 274 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] =  -1.23 [-1.45 to -1.01]), (3) the subgroup with FA values in the central area of the tumor (249 HGG and 156 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = - 0.44 [-0.84,-0.04]), furthermore (4) the subgroup with FA values in the peritumoral area (261 HGG and 168 SBM) shows that the HGG patients are significantly higher than the SBM (SMD [95% CI] = -0.59 [-1.02,-0.16]).

CONCLUSION

Combining rCBV and FA measurements in the peritumoral region and FA in the intratumoral region increase the accuracy of MRI examination to differentiate between HGG and SBM patients effectively. Confidence in the accuracy of our results may be influenced by major interstudy heterogeneity. Whereas the I2 for the rCBV in the intratumoral subgroup was 80%, I2 for the rCBV in the peritumoral subgroup was 39%, and I2 for the FA in the intratumoral subgroup was 69%, and I2 for the FA in the peritumoral subgroup was 74%. The predefined accurate search criteria, and precise selection and evaluation of methodological quality for included studies, strengthen this studyOur study has no funder, no conflict of interest, and followed an established PROSPERO protocol (ID: CRD42021279106).

ADVANCES IN KNOWLEDGE

The combination of rCBV and FA measurements' results is promising in differentiating HGG and SBM.

Diffusion-Weighted Imaging Combined with Perfusion-Weighted Imaging under Segmentation Algorithm in the Diagnosis of Melanoma

This study is aimed at exploring the value of magnetic resonance diffusion-weighted imaging (DWI) combined with perfusion-weighted imaging (PWI) for diagnosing melanoma under a three-dimensional (3D) hybrid segmentation algorithm. 40 patients with melanoma were collected as research objects and subjected to magnetic resonance imaging (MRI) examination. A segmentation model was constructed and the original images were input. The noise contained in the images was preprocessed and normalized, and the mixed level set segmentation was performed after linear fusion of the images. Imaging findings were analyzed to find that the combined diagnosis of DWI and PWI with a 3D hybrid segmentation algorithm had the advantage of being clear and accurate. 10 primary cases were detected, which occurred in the cerebral meninges; 30 cases of metastases occurred inside the skull, mostly adjacent to the surface of the brain. The typical T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) of melanoma showed high signal and low signal, respectively, and the enhanced scan showed obvious enhancement. Atypical melanoma was manifested variously in MRI; a few had cystic necrosis, and an enhanced scan of the solid area revealed significant enhancement. Patients with multiple metastatic melanomas mainly showed low signal on DWI, and patients with primary or single metastatic melanoma mainly showed high signal or mixed high signal. Patients with perfusion imaging showed high perfusion on PWI. The 3D hybrid segmentation algorithm helped to improve the accuracy of DWI combined with PWI in the diagnosis of melanoma. This work provided a certain reference for the clinical diagnosis of melanoma.

Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning

Purpose

The majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning.

Materials and Methods

Radiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brain metastases and 103 patients with GBMs from the cancer genome atlas.

Results

Three groups of models were developed. The average areas under the receiver operating characteristic curve (AUCs) were 0.856 for CE T1-weighted images, 0.976 for T2-weighted images, and 0.988 for a combination in the testing groups, and the AUCs of the groups of models in the independent validation were 0.687, 0.831, and 0.867, respectively. A total of 149 radiomics features were considered as the most valuable features for the differential diagnosis of GBMs and metastases.

Conclusion

The models established by TPOT can distinguish glioblastoma from solitary brain metastases well, and its non-invasiveness, convenience, and robustness make it potentially useful for clinical applications.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.

Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review

Simple Summary

We present a systematic review of published reports on machine learning (ML) applications for the differentiation of gliomas from brain metastases by summarizing study characteristics, strengths, and pitfalls. Based on these findings, we present recommendations for future research in this field.

Abstract

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.

Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis

OBJECTIVE

Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.

METHODS

In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.

RESULTS

A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%.

CONCLUSION

This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.

KEY POINTS

• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.

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.

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.

Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis

Objective

The purpose of this study was to investigate the feasibility of applying handcrafted radiomics (HCR) and deep learning-based radiomics (DLR) for the accurate preoperative classification of glioblastoma (GBM) and solitary brain metastasis (BM).

Methods

A retrospective analysis of the magnetic resonance imaging (MRI) data of 140 patients (110 in the training dataset and 30 in the test dataset) with GBM and 128 patients (98 in the training dataset and 30 in the test dataset) with BM confirmed by surgical pathology was performed. The regions of interest (ROIs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI (T1CE) were drawn manually, and then, HCR and DLR analyses were performed. On this basis, different machine learning algorithms were implemented and compared to find the optimal modeling method. The final classifiers were identified and validated for different MRI modalities using HCR features and HCR + DLR features. By analyzing the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the predictive efficacy of different methods.

Results

In multiclassifier modeling, random forest modeling showed the best distinguishing performance among all MRI modalities. HCR models already showed good results for distinguishing between the two types of brain tumors in the test dataset (T1WI, AUC = 0.86; T2WI, AUC = 0.76; T1CE, AUC = 0.93). By adding DLR features, all AUCs showed significant improvement (T1WI, AUC = 0.87; T2WI, AUC = 0.80; T1CE, AUC = 0.97; p < 0.05). The T1CE-based radiomic model showed the best classification performance (AUC = 0.99 in the training dataset and AUC = 0.97 in the test dataset), surpassing the other MRI modalities (p < 0.05). The multimodality radiomic model also showed robust performance (AUC = 1 in the training dataset and AUC = 0.84 in the test dataset).

Conclusion

Machine learning models using MRI radiomic features can help distinguish GBM from BM effectively, especially the combination of HCR and DLR features.

The Prognostic Role of Peritumoral Edema in Patients with Newly Diagnosed Glioblastoma: A Retrospective Analysis

OBJECTIVE

Previous studies on glioblastomas (GBMs) have not reached a consensus on peritumoral edema (PTE)'s influence on survival. This study evaluated the PTE index's prognostic role in newly diagnosed GBMs using a well-designed method.

METHODS

Selected patients were reviewed after a rigorous screening process. Their general information was obtained from electronic medical records. The imaging metrics (MTD, TTM, TTE) representing tumor diameter, laterality, and PTE extent were obtained by manual measurement in Syngo FastView software. The PTE index was a ratio of TTE to MTD. Multiple variables were evaluated using analysis of variance and Cox regression model.

RESULTS

Of 143 patients, 62 were included in this study. MGMT promoter methylation and tumor laterality were both independent prognostic factors (p = 0.020, 0.042; HR = 0.272, 2.630). The lateral tumors' index was higher than that of the medial tumors (57.7% vs. 42.6%, p = 0.027). Low-index tumors were located in relatively medial positions compared with high-index tumors (TTM, 4.9 vs. 12.8, p = 0.032). This finding indicated that the PTE index tended to increase with tumor laterality. Moreover, the patients with low-index tumors had a significant survival disadvantage in the univariate analysis but not in the multivariate analysis (p = 0.023, 0.220). However, further analysis found that the combination of tumor laterality and PTE statistically stratified the survival outcome. The patients with lateral high-index tumors survived significantly longer (p = 0.022, HR = 1.927).

CONCLUSIONS

In contrast with the previous studies, this study recommends combining PTE and tumor laterality for survival stratification in newly diagnosed GBMs.

Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis

Background

The differential diagnosis of glioblastomas (GBM) from solitary brain metastases (SBM) is essential because the surgical strategy varies according to the histopathological diagnosis. Intraoperative ultrasound elastography (IOUS-E) is a relatively novel technique implemented in the surgical management of brain tumors that provides additional information about the elasticity of tissues. This study compares the discriminative capacity of intraoperative ultrasound B-mode and strain elastography to differentiate GBM from SBM.

Methods

We performed a retrospective analysis of patients who underwent craniotomy between March 2018 to June 2020 with glioblastoma (GBM) and solitary brain metastases (SBM) diagnoses. Cases with an intraoperative ultrasound study were included. Images were acquired before dural opening, first in B-mode, and then using the strain elastography module. After image pre-processing, an analysis based on deep learning was conducted using the open-source software Orange. We have trained an existing neural network to classify tumors into GBM and SBM via the transfer learning method using Inception V3. Then, logistic regression (LR) with LASSO (least absolute shrinkage and selection operator) regularization, support vector machine (SVM), random forest (RF), neural network (NN), and k-nearest neighbor (kNN) were used as classification algorithms. After the models’ training, ten-fold stratified cross-validation was performed. The models were evaluated using the area under the curve (AUC), classification accuracy, and precision.

Results

A total of 36 patients were included in the analysis, 26 GBM and 10 SBM. Models were built using a total of 812 ultrasound images, 435 of B-mode, 265 (60.92%) corresponded to GBM and 170 (39.8%) to metastases. In addition, 377 elastograms, 232 (61.54%) GBM and 145 (38.46%) metastases were analyzed. For B-mode, AUC and accuracy values of the classification algorithms ranged from 0.790 to 0.943 and from 72 to 89%, respectively. For elastography, AUC and accuracy values ranged from 0.847 to 0.985 and from 79% to 95%, respectively.

Conclusion

Automated processing of ultrasound images through deep learning can generate high-precision classification algorithms that differentiate glioblastomas from metastases using intraoperative ultrasound. The best performance regarding AUC was achieved by the elastography-based model supporting the additional diagnostic value that this technique provides.

Validation of combined use of DWI and percentage signal recovery-optimized protocol of DSC-MRI in differentiation of high-grade glioma, metastasis, and lymphoma

PURPOSE

With conventional MRI, it is often difficult to effectively differentiate between contrast-enhancing brain tumors, including primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and metastasis. This study aimed to assess the discrimination ability of the parameters obtained from DWI and the percentage signal recovery- (PSR-) optimized protocol of DSC-MRI between these three tumor types at an initial step.

METHODS

DSC-MRI using a PSR-optimized protocol (TR/TE = 1500/30 ms, flip angle = 90°, no preload) and DWI of 99 solitary enhancing tumors (60 HGGs, 24 metastases, 15 PCNSLs) were retrospectively assessed before treatment. rCBV, PSR, ADC in the tumor core and rCBV, and ADC in peritumoral edema were measured. The differences were evaluated using one-way ANOVA, and the diagnostic performance was evaluated using ROC curve analysis.

RESULTS

PSR in the tumor core showed the best discriminating performance in differentiating these three tumor types with AUC values of 0.979 for PCNSL vs. others and 0.947 for HGG vs. metastasis. The ADC was only helpful in the tumor core and distinguishing PCNSLs from others (AUC = 0.897).

CONCLUSION

Different from CBV-optimized protocols (preload, intermediate FA), PSR derived from the PSR-optimized protocol seems to be the most important parameter in the differentiation of HGGs, metastases, and PCNSLs at initial diagnosis. This property makes PSR remarkable and carries the need for comprehensive DSC-MRI protocols, which provides PSR sensitivity and CBV accuracy together, such as the preload use of the PSR-optimized protocol before the CBV-optimized protocol.