Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models

High-grade gliomas (HGGs) and brain metastases are common malignancies in the central nervous system (CNS). HGGs account for approximately 80% of primary CNS malignancies [1]. Meanwhile, metastatic tumors occur ten times more frequently than primary malignancy in the brain [2]. The differentiation between HGGs and brain metastases is critical, as the management strategies for these two malignant brain tumors are vastly different. For patients with HGGs, surgical resection is the first choice, and it is usually not necessary to perform a systemic examination [2]. However, for patients with suspected brain metastases, comprehensive systemic examinations are needed, and if confirmed, stereotactic radiosurgery or systemic therapy such as targeted therapy and immunotherapy are recommended [3].

Magnetic resonance imaging (MRI) is the mainstay of imaging modalities for the diagnosis of brain tumors. For patients who present multiple cerebral lesions and have a history of primary malignancy, the diagnosis of brain metastases may be straightforward by MRI. However, solitary brain metastases (SBMs) are the first manifestation in nearly 30% of patients with systemic malignancy [4]. Therefore, when patients show a solitary and contrast-enhancing brain lesion, it would be challenging to distinguish HGG from solitary brain metastasis (SBM) because they often show similar signal features and contrast enhancement patterns on conventional MRI, leading to incorrect diagnosis in over 40% of cases [5]. In this case, tumor biopsy is often performed to confirm the histologic diagnosis, whereas it has inherent limitations, such as procedure-related complications, interobserver variability and sampling errors [6]. Thus, a noninvasive method to differentiate HGGs from SBMs is preferable and sometimes mandatory when the patient cannot receive surgery due to poor general condition or when the tumor involves or is adjacent to important brain areas.

Diffusion-weighted imaging (DWI) is one of the most widely used advanced MRI techniques to characterize the microstructural changes in cerebral tumors, which complements the anatomic information provided by conventional MRI [7]. Previously, Gaussian-based DWI and diffusion tensor imaging (DTI) have been used to distinguish HGGs from SBMs, with DWI-based apparent diffusion coefficient (ADC) and DTI-generated fractional anisotropy (FA) being the most commonly used metrics. However, contradictory results have been reported on the ability of ADC and FA to differentiate HGGs from SBMs [8‐10].

Recently, novel diffusion MRI techniques, such as the three-compartment biophysical model neurite orientation dispersion and density imaging (NODDI) and the non-Gaussian-based mean apparent propagator (MAP)-MRI, have emerged as powerful tools to evaluate brain microstructure in vivo, as they can provide new insights into the complexity and inhomogeneity of brain microstructure [11, 12]. Both NODDI and MAP-MRI have shown promising results in lateralization of temporal lobe epilepsy [13], assessment of Parkinson’s disease [14] and grading of gliomas [15]; nonetheless, whether they outperformed the more commonly used non-Gaussian-based DKI and Gaussian diffusion models such as DTI and DWI in differentiation between HGGs and SBMs remains unknown. Therefore, the aim of our study was to compare the diagnostic performance of NODDI, MAP-MRI, DKI, DTI and DWI in distinguishing HGGs from SBMs.

Our results demonstrated that HGGs and SBMs showed distinctive NODDI, MAP-MRI, DKI and DTI-based diffusion metrics in the contrast-enhancing tumor region, while no difference was observed for any of the diffusion parameters in peritumoral edema. NODDI-based tumoral V_iso had the greatest discriminative power between HGGs and SBMs.

Patients with HGGs or SBMs generally have a dismal prognosis, but correct differential diagnosis and appropriate clinical decisions can significantly prolong the survival time [20]. Thus, it is crucial to distinguish HGGs from SBMs. Nonetheless, it is always challenging to differentiate between these two malignancies only by conventional MRI [5]. In recent years, advanced diffusion-weighted MRI techniques have emerged as powerful tools to assess microstructural changes in CNS diseases. As the complex microstructures in neural tissue (e.g., cell membranes and myelin fibers) change water molecule diffusion into a non-Gaussian probability distribution, non-Gaussian diffusion models such as MAP-MRI and DKI are supposed to reflect the real situation of water molecule diffusion more accurately and better characterize the complexity and inhomogeneity of the tissue microenvironment than Gaussian diffusion models [21]. Specifically, DKI is a commonly used and moderately complex physical model that is sensitive to DWI sampling and noise [22]. MAP-MRI is a more recent and highly complex physical model that can evaluate three-dimensional q-space data [11] but shows increased sensitivity to DWI sampling and noise [22]. Comparatively, NODDI is an increasingly popular biophysical model with low complexity that attempts to separate the signal contribution of neural tissue into three compartments, including restricted, hindered, and isotropic diffusion, and to model the dispersion of axonal fibers [17]. NODDI metrics are highly stable to DWI sampling and image quality [22]. In the present study, we found that NODDI-based V_iso outperformed other non-Gaussian or Gaussian diffusion parameters in the differentiation between HGGs and SBMs. Although no single diffusion parameter can fully capture the complexity of neural tissue, our findings suggest that NODDI-V_iso could potentially be a sensitive imaging biomarker in neuro-oncology research and deserves further investigation.

NODDI-V_iso represents isotropic diffusion within the tissue; in our study, HGGs showed a lower tumoral V_iso value than SBMs. This phenomenon can be explained by the fact that HGGs are characterized by enlarged extracellular space and overproduction of certain components of extracellular matrix components, mainly tenascin [23]. These molecules accumulate and orient in the extracellular matrix [24], resulting in less isotropy at DWI. In contrast, metastatic brain tumors degrade the extracellular matrix with heparanase and matrix metalloproteinases, thereby growing into the brain parenchyma in an expansive and noninfiltrating pattern [25], resulting in higher isotropy at DWI. NODDI-based ODI represents the orientation dispersion of fibers in tissue [12]. In this study, the tumoral ODI value was found to be lower in HGGs than in SBMs, which could also be explained by the fact that tumor tissue tends to be less isotropic for HGGs than for SBMs. For MAP-MRI, we found that HGGs showed lower MSD and QIV values than SBMs. MSD indicates the mean square displacement of the water molecules. These results can be explained by the fact that the solid part of HGGs had higher cellularity than did brain metastases [26‐28], which might lead to higher diffusion restriction in HGGs, and the water molecules will thus move shorter distances and result in a lower MSD value [19]. QIV signifies the q-space inverse variance, which is a pseudodiffusivity measure and represents different diffusion components [29]; thus, a higher tumoral QIV value for SBM suggested a higher proportion of fast diffusivity in SBMs. For DTI, we found that HGGs showed a higher tumoral FA value and lower tumoral RD and MD values than SBMs. FA is a measurement of the directionality of water diffusion along with the white matter, which has a positive correlation with tumor cellularity [30]. Higher tumoral FA values for HGGs than SBMs were also described in recent studies [31, 32], where a higher FA value of the contrast-enhancing region of HGGs was reported to be assumed to be due to the higher cellularity of HGGs [26‐28]. MD reveals the rate of water molecule diffusional motion; RD represents the diffusion rate of water perpendicular to white matter fibers [17]. Both MD and RD show an inverse relationship with tumor cellularity [33, 34], which can explain the opposite change trend of MD and RD compared with FA. As an extension of DTI [35], DKI-based RD, MD and FA in our study showed similar change patterns with DTI-based RD, MD and FA.

Previously, the ability of diffusion MR metrics such as ADC and FA to distinguish peritumoral edema of HGGs from that of SBMs has been widely investigated, but the study results remain controversial [8‐10]. In the present study, although five diffusion models were utilized, no significant differences were found in any of the diffusion parameters between the peritumoral edema of HGGs and SBMs. These inconsistent results may contribute to the intrinsic heterogeneity of HGGs. Although it was confirmed histologically that tumor cells exist in the peritumoral edema of HGGs, the magnitude of tumor cell infiltration actually has a substantially wide range [36]. Thus, minimal tumor infiltration may not cause a significant signal change in diffusion MRI. Further studies applying three-dimensional texture analysis of volumetric diffusion MR images could provide additional information on the heterogeneity of tumor cell infiltration in peritumoral edema, which may be helpful in the differentiation of tumor-infiltrated edema from purely vasogenic edema.

Our study has some limitations. First, the sample size was small for both the HGG and SBM groups, as all patients were prospectively enrolled from a single institution. However, our study showed that the advanced diffusion-weighted technique NODDI-based V_iso had a desirable diagnostic performance (AUC = 0.871) for distinguishing HGGs from SBMs. This model deserves further study with a larger sample size to validate the current results. Second, the diffusion MR examination in our study requires a long scan duration of approximately 10 min. In the future, this problem can be overcome by using advanced techniques, such as compressed sensing [37] and simultaneous multislice acquisition techniques [38].

Our study shows that NODDI outperforms MAP-MRI, DKI, DTI and DWI in distinguishing HGGs from SBMs. Among all the diffusion metrics, NODDI-based V_iso has the highest performance in differentiating between HGGs and SBMs.