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

The T1/T2 Ratio is Associated With Resectability in Patients With Isocitrate Dehydrogenase-Mutant Astrocytomas Central Nervous System World Health Organization Grades 2 and 3

BACKGROUND AND OBJECTIVES

Isocitrate dehydrogenase (IDH)-mutant astrocytomas central nervous system World Health Organization grade 2 and 3 show heterogeneous appearance on MRI. In the premolecular era, the discrepancy between T1 hypointense and T2 hyperintense tumor volume in absolute values has been proposed as a marker for diffuse tumor growth. We set out to investigate if a ratio of T1 to T2 tumor volume (T1/T2 ratio) is associated with resectability and overall survival (OS) in patients with IDH-mutant astrocytomas.

METHODS

Patient data from 2 centers (Sahlgrenska University Hospital, Center A; LMU University Hospital, Center B) were collected retrospectively. Inclusion criteria were as follows: pre and postoperative MRI scans available for volumetric analysis (I), diagnosis of an IDH-mutant astrocytoma between 2003 and 2021 (II), and tumor resection at initial diagnosis (III). Tumor volumes were manually segmented. The T1/T2 ratio was calculated and correlated with extent of resection, residual T2 tumor volume, and OS.

RESULTS

The study comprised 134 patients with 65 patients included from Center A and 69 patients from Center B. The median OS was 134 months and did not differ between the cohorts (P = .29). Overall, the median T1/T2 ratio was 0.79 (range 0.15-1.0). Tumors displaying a T1/T2 ratio of 0.33 or lower showed significantly larger residual tumor volumes postoperatively (median 17.9 cm3 vs 4.6 cm3, P = .03). The median extent of resection in these patients was 65% vs 90% (P = .03). The ratio itself did not correlate with OS. In multivariable analyses, larger postoperative tumor volumes were associated with shorter survival times (hazard ratio 1.02, 95% CI 1.01-1.03, P < .01).

CONCLUSION

The T1/T2 ratio might be a good indicator for diffuse tumor growth on MRI and is associated with resectability in patients with IDH-mutant astrocytoma. This ratio might aid to identify patients in which an oncologically relevant tumor volume reduction cannot be safely achieved.

Diagnostic Algorithm for Intracranial Lesions in the Emergency Department: Effectiveness of the Relative Brain Volume and Hounsfield Unit Value Measured by Perfusion Tomography

Background Early treatment of intracranial lesions in the emergency department is crucial, but it can be challenging to differentiate between them. This differentiation is essential because the treatment of each type of lesion is different. Cerebral computed tomography perfusion (CTP) imaging can help visualize the vascularity of brain lesions and provide absolute quantification of physiological parameters. Compared to magnetic resonance imaging, CTP has several advantages, such as simplicity, wide availability, and reproducibility. Purpose This study aimed to assess the effectiveness of Hounsfield units (HU) in measuring the density of hypercellular lesions and the ability of CTP to quantify hemodynamics in distinguishing intracranial space-occupying lesions. Methods A retrospective study was conducted from March 2016 to March 2022. All patients underwent CTP and CT scans, and relative cerebral blood volume (rCBV) and HU were obtained for intracranial lesions. Results We included a total of 244 patients in our study. This group consisted of 87 (35.7%) individuals with glioblastomas (GBs), 48 (19.7%) with primary central nervous system lymphoma (PCNSL), 45 (18.4%) with metastases (METs), and 64 (26.2) with abscesses. Our study showed that the HUs for METs were higher than those for GB (S 57.4% and E 88.5%). In addition, rCBV values for PCNSL and abscesses were lower than those for GB and METs. The HU in PCNSL was higher than those in abscesses (S 94.1% and E 96.6%). Conclusion PCT parameters provide valuable information for diagnosing brain lesions. A comprehensive assessment improves accuracy. Combining rCBV and HU enhances diagnostic accuracy, making it a valuable tool for distinguishing between lesions. PCT's widespread availability allows for the use of both anatomical and functional information with high spatial resolution for diagnosing and managing brain tumor patients.

A glutamatergic biomarker panel enables differentiating Grade 4 gliomas/astrocytomas from brain metastases

Background

The differentiation of high-grade glioma and brain tumors of an extracranial origin is eminent for the decision on subsequent treatment regimens. While in high-grade glioma, a surgical resection of the tumor mass is a fundamental part of current standard regimens, in brain metastasis, the burden of the primary tumor must be considered. However, without a cancer history, the differentiation remains challenging in the imaging. Hence, biopsies are common that may help to identify the tumor origin. An additional tool to support the differentiation may be of great help. For this purpose, we aimed to identify a biomarker panel based on the expression analysis of a small sample of tissue to support the pathological analysis of surgery resection specimens. Given that an aberrant glutamate signaling was identified to drive glioblastoma progression, we focused on glutamate receptors and key players of glutamate homeostasis.

Methods

Based on surgically resected samples from 55 brain tumors, the expression of ionotropic and metabotropic glutamate receptors and key players of glutamate homeostasis were analyzed by RT-PCR. Subsequently, a receiver operating characteristic (ROC) analysis was performed to identify genes whose expression levels may be associated with either glioblastoma or brain metastasis.

Results

Out of a total of 29 glutamatergic genes analyzed, nine genes presented a significantly different expression level between high-grade gliomas and brain metastases. Of those, seven were identified as potential biomarker candidates including genes encoding for AMPA receptors GRIA1, GRIA2, kainate receptors GRIK1 and GRIK4, metabotropic receptor GRM3, transaminase BCAT1 and the glutamine synthetase (encoded by GLUL). Overall, the biomarker panel achieved an accuracy of 88% (95% CI: 87.1, 90.8) in predicting the tumor entity. Gene expression data, however, could not discriminate between patients with seizures from those without.

Conclusion

We have identified a panel of seven genes whose expression may serve as a biomarker panel to discriminate glioblastomas and brain metastases at the molecular level. After further validation, our biomarker signatures could be of great use in the decision making on subsequent treatment regimens after diagnosis.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics

OBJECTIVES

To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM).

MATERIALS AND METHODS

In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison.

RESULTS

The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar.

CONCLUSION

The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM.

CLINICAL RELEVANCE STATEMENT

The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity.

KEY POINTS

• The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis

BACKGROUND

We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM).

METHODS

We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison.

RESULTS

NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model.

CONCLUSION

Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.

Brain Tumor Imaging: Review of Conventional and Advanced Techniques

Approaches to central nervous system (CNS) tumor classification and evaluation have undergone multiple iterations over the past few decades, in large part due to our growing understanding of the influence of genetics on tumor behavior and our refinement of brain tumor imaging techniques. Computed tomography and magnetic resonance imaging (MRI) both play a critical role in the diagnosis and monitoring of brain tumors, although MRI has become especially important due to its superior soft tissue resolution. The purpose of this article will be to briefly review the fundamentals of conventional and advanced techniques used in brain tumor imaging. We will also highlight the applications of these imaging tools in the context of commonly encountered tumors based on the most recently updated 2021 World Health Organization (WHO) classification of CNS tumors framework.

Biogenic Imaging Contrast Agents

Imaging contrast agents are widely investigated in preclinical and clinical studies, among which biogenic imaging contrast agents (BICAs) are developing rapidly and playing an increasingly important role in biomedical research ranging from subcellular level to individual level. The unique properties of BICAs, including expression by cells as reporters and specific genetic modification, facilitate various in vitro and in vivo studies, such as quantification of gene expression, observation of protein interactions, visualization of cellular proliferation, monitoring of metabolism, and detection of dysfunctions. Furthermore, in human body, BICAs are remarkably helpful for disease diagnosis when the dysregulation of these agents occurs and can be detected through imaging techniques. There are various BICAs matched with a set of imaging techniques, including fluorescent proteins for fluorescence imaging, gas vesicles for ultrasound imaging, and ferritin for magnetic resonance imaging. In addition, bimodal and multimodal imaging can be realized through combining the functions of different BICAs, which helps overcome the limitations of monomodal imaging. In this review, the focus is on the properties, mechanisms, applications, and future directions of BICAs.

The role of 3-Tesla magnetic resonance perfusion and spectroscopy in distinguishing glioblastoma from solitary brain metastasis

OBJECTIVES

This study aimed to assess the value of magnetic resonance perfusion (MR perfusion) and magnetic resonance spectroscopy (MR spectroscopy) in 3.0-Tesla magnetic resonanceimaging (MRI) for differential diagnosis of glioblastoma (GBM) and solitary brain metastasis (SBM).

MATERIAL AND METHODS

This retrospective study involved 36 patients, including 24 cases of GBM and 12 of SBM diagnosed using histopathology. All patients underwent a 3.0-Tesla MRI examination with pre-operative MR perfusion and MR spectroscopy. We assessed the differences in age, sex, cerebral blood volume (CBV), relative CBV (rCBV), and the metabolite ratios of choline/N-acetylaspartate (Cho/NAA) and Cho/creatine between the GBM and SBM groups using the Mann-Whitney U-test and Chi-square test. The cutoff value, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value of the significantly different parameters between these two groups were determined using the receiver operating characteristic curve.

RESULTS

In MR perfusion, the CBV of the peritumoral region (pCBV) had the highest preoperative predictive value in discriminating GBM from SBM (cutoff: 1.41; sensitivity: 70.83%; and specificity: 83.33%), followed by the ratio of CBV of the solid tumor component to CBV of normal white matter (rCBVt/n) and the ratio of CBV of the pCBV to CBV of normal white matter (rCBVp/n). In MR spectroscopy, the Cho/NAA ratio of the pCBV (pCho/NAA; cutoff: 1.02; sensitivity: 87.50%; and specificity: 75%) and the Cho/NAA ratio of the solid tumor component (tCho/NAA; cutoff: 2.11; sensitivity: 87.50%; and specificity: 66.67%) were significantly different between groups. Moreover, combining these remarkably different parameters increased their diagnostic utility for distinguishing between GBM and SBM.

CONCLUSION

pCBV, rCBVt/n, rCBVp/n, pCho/NAA, and tCho/NAA are useful indices for differentiating between GBM and SBM. Combining these indices can improve diagnostic performance in distinguishing between these two tumors.

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.

A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis

Simple Summary

Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.

Abstract

Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.

The Role of 3D-pCASL MRI in the Differential Diagnosis of Glioblastoma and Brain Metastases

Purpose

The first aim of this study was to compare the intratumoral and peritumoral blood flow parameters in glioblastomas and brain metastases measured by pseudocontinuous arterial spin labeling MRI (3D pCASL). The second aim of this study was to determine whether pCASL could aid in identifying the source of brain metastases.

Materials and Methods

This study included 173 patients aged 12 to 83 years (median age—61 years), who were observed at the National Medical Research Center for Neurosurgery. All patients underwent preoperative MRI with pCASL perfusion. Thereafter patients were operated on and received histological diagnosis. No patients received preoperative chemo or radiotherapy.

Results

The values of maximum and normalized intratumoral blood flow were significantly higher in the group with gliblastoma than in the group with brain metastases: 168.98 + −91.96 versus 152.1 + −173.32 and 7.6 + −8.4 versus 9.3 + −5.33 respectively (p <0.01). However, ROC analysis showed low AUC specificity and sensitivity (0.64, 70%, 60% for mTBF and 0.66, 77%, 62% for nTBF). Peritumoral blood flow parameters were also higher in the glioblastoma group (29.61 + −22.89 versus 16.58 + −6.46 for mTBF and 1.63 + −1.14 versus 0.88 + −0.38 for nTBF, respectively; p <0.01). ROC analysis showed the following measurements of AUC, specificity, and sensitivity (0.75, 68%, 73% for mTBF and 0.77, 58%, 91% for nTBF). Regarding pCASL and various histological subsets of brain metastases, the study found statistically significant differences between the lung and melanoma metastases and the lung and kidney metastases. ROC analysis gave the following values for lung and melanoma metastases: AUC—0.76, specificity—75%, and sensitivity—73% for mTBF; 0.83, 67%, and 93% respectively, for nTBF. For lung and kidney metastases: AUC—0.74, specificity—70%, and sensitivity—93% for mTBF; 0.75, 70%, and 93% respectively, for nTBF.

Conclusions

pCASL could aid in differential diagnosis between glioblastoma and brain metastases. Measurement of peritumoral blood flow demonstrates higher specificity and sensitivity than with intratumoral blood flow. Moreover, pCASL provides the ability to distinguish lung metastases from kidney and melanoma metastases.

CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone

Due to their similar imaging features, high-grade gliomas (HGGs) and solitary brain metastases (BMs) can be easily misclassified. The peritumoral zone (PZ) of HGGs develops neoplastic cell infiltration, while in BMs the PZ contains pure vasogenic edema. As the two PZs cannot be differentiated macroscopically, this study investigated whether computed tomography (CT)-based texture analysis (TA) of the PZ can reflect the histological difference between the two entities. Thirty-six patients with solitary brain tumors (HGGs, n = 17; BMs, n = 19) that underwent CT examinations were retrospectively included in this pilot study. TA of the PZ was analyzed using dedicated software (MaZda version 5). Univariate, multivariate, and receiver operating characteristics analyses were used to identify the best-suited parameters for distinguishing between the two groups. Seven texture parameters were able to differentiate between HGGs and BMs with variable sensitivity (56.67–96.67%) and specificity (69.23–100%) rates. Their combined ability successfully identified HGGs with 77.9–99.2% sensitivity and 75.3–100% specificity. In conclusion, the CT-based TA can be a useful tool for differentiating between primary and secondary malignancies. The TA features indicate a more heterogenous content of the HGGs’ PZ, possibly due to the local infiltration of neoplastic cells.

Neuroinflammation and immunoregulation in glioblastoma and brain metastases: Recent developments in imaging approaches

Brain tumors and brain metastases induce changes in brain tissue remodeling that lead to immunosuppression and trigger an inflammatory response within the tumor microenvironment. These immune and inflammatory changes can influence invasion and metastasis. Other neuroinflammatory and necrotic lesions may occur in patients with brain cancer or brain metastases as sequelae from treatment with radiotherapy. Glioblastoma (GBM) is the most aggressive primary malignant brain cancer in adults. Imaging methods such as positron emission tomography (PET) and different magnetic resonance imaging (MRI) techniques are highly valuable for the diagnosis and therapeutic evaluation of GBM and other malignant brain tumors. However, differentiating between tumor tissue and inflamed brain tissue with imaging protocols remains a challenge. Here, we review recent advances in imaging methods that have helped to improve the specificity of primary tumor diagnosis versus evaluation of inflamed and necrotic brain lesions. We also comment on advances in differentiating metastasis from neuroinflammation processes. Recent advances include the radiosynthesis of ¹⁸ F-FIMP, an L-type amino acid transporter 1 (LAT1)-specific PET probe that allows clearer differentiation between tumor tissue and inflammation compared to previous probes, and the combination of different advanced imaging protocols with the inclusion of radiomics and machine learning algorithms.