Perfusion Computed Tomography Parameters Are Useful for Differentiating Glioblastoma, Lymphoma, and Metastasis

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.

Predictors for the Differentiation between Glioblastoma, Primary Central Nervous System Lymphoma, and Metastasis in Patients with a Solitary Enhancing Intracranial Mass

Introduction  Differentiation between glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastasis is important in decision-making before surgery. However, these malignant brain tumors have overlapping features. This study aimed to identify predictors differentiating between GBM, PCNSL, and metastasis. Materials and Methods  Patients with a solitary intracranial enhancing tumor and a histopathological diagnosis of GBM, PCNSL, or metastasis were investigated. All patients with intracranial lymphoma had PCNSL without extracranial involvement. Demographic, clinical, and radiographic data were analyzed to determine their associations with the tumor types. Results  The predictors associated with GBM were functional impairment ( p  = 0.001), large tumor size ( p  < 0.001), irregular tumor margin ( p  < 0.001), heterogeneous contrast enhancement ( p  < 0.001), central necrosis ( p  < 0.001), intratumoral hemorrhage ( p  = 0.018), abnormal flow void ( p  < 0.001), and hypodensity component on noncontrast cranial computed tomography (CT) scan ( p  < 0.001). The predictors associated with PCNSL comprised functional impairment ( p  = 0.005), deep-seated tumor location ( p  = 0.006), homogeneous contrast enhancement ( p  < 0.001), absence of cystic appearance ( p  = 0.008), presence of hypointensity component on precontrast cranial T1-weighted magnetic resonance imaging (MRI; p  = 0.027), and presence of isodensity component on noncontrast cranial CT ( p  < 0.008). Finally, the predictors for metastasis were an infratentorial ( p  < 0.001) or extra-axial tumor location ( p  = 0.035), smooth tumor margin ( p  < 0.001), and presence of isointensity component on cranial fluid-attenuated inversion recovery MRI ( p  = 0.047). Conclusion  These predictors may be used to differentiate between GBM, PCNSL, and metastasis, and they are useful in clinical management.

Advances in the Radiological Evaluation of and Theranostics for Glioblastoma

Imaging is essential for evaluating patients with glioblastoma. Traditionally a multimodality undertaking, CT, including CT cerebral blood profusion, PET/CT with traditional fluorine-18 fluorodeoxyglucose (18F-FDG), and MRI have been the mainstays for diagnosis and post-therapeutic assessment. However, recent advances in these modalities, in league with the emerging fields of radiomics and theranostics, may prove helpful in improving diagnostic accuracy and treating the disease.

Preoperative symptoms of depression, anxiety, and cognitive impairment in glioma patients: A cerebral perfusion CT study

PURPOSE

Glioma patients have varying degrees of psychiatric symptoms, which severely affect the quality of life of patients and their families. The present study investigated the correlation between preoperative psychiatric symptoms and local cerebral perfusion parameters of in glioma patients.

PATIENTS AND METHODS

The depression, anxiety, and cognitive impairment (CI) scores of 39 patients were assessed separately, and all of the patients underwent a preoperative perfusion computed tomography scan.

RESULTS

This study found that: (1) The incidence of preoperative symptoms of depression, anxiety, and CI was 46.15%, 48.72%, and 25.64%, respectively. (2) Cerebral blood volume (CBV) (lesion-sided [LS] occipital lobe white matter [WM] and parietal lobe WM and normal-sided temporal lobe WM), permeability surface (PS) (LS temporal lobe gray matter [GM] and parietal lobe WM) in the depression group were significantly decreased (p < .05). (3) CBV (LS occipital lobe WM), cerebral blood flow (LS parietal lobe GM, centrum ovale and frontal lobe WM and normal-sided frontal lobe WM, temporal lobe WM and parietal lobe WM), and mean transition time (MTT) (normal-sided frontal lobe WM and temporal lobe WM) in the anxiety group were significantly increased (p < .05). (4) CBV (LS temporal lobe GM), MTT (LS anterior limb of internal capsule), and PS (LS thalamus) in the CI group were significantly increased (p < .05).

CONCLUSION

This study showed that glioma patients had different levels of psychological distress in glioma patients before surgery, which may be related to the changes in brain perfusion caused by the tumor.

Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features

Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors.

Assessment of brain tumors by magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging and computed tomography perfusion: a comparison study

PURPOSE

To investigate the association and agreement between magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and computed tomography perfusion (CTP) in determining vascularity and permeability of primary and secondary brain tumors.

MATERIAL AND METHODS

DSC-PWI and CTP studies from 97 patients with high-grade glioma, low-grade glioma and solitary brain metastasis were retrospectively reviewed. Normalized cerebral blood flow (nCBF), cerebral blood volume (nCBV), capillary transfer constant (nK2) and permeability surface area product (nPS) values were obtained. Variables among groups were compared, and correlation and agreement between DSC-PWI and CTP were tested.

RESULTS

All DSC-PWI and CTP parameters were higher in high-grade than in low-grade gliomas (p < 0.01 and p < 0.001). Metastases had greater DSC-PWI nCBV (p < 0.05), nCTP-CBF (p < 0.05), nCTP-CBV (p < 0.01) and nCTP-PS (p < 0.0001) than low-grade gliomas and more elevated nCTP-PS (p < 0.01) than high-grade gliomas. The correlation was strong between DSC-PWI nCBF and CTP nCBF (r = 0.79; p < 0.00001) and between DSC-PWI nCBV and CTP nCBV (r = 0.83; p < 0.00001), weaker between DSC-PWI nK2 and CTP nPS (r = 0.29; p < 0.01). Bland-Altman plots indicated that the agreement was strong between DSC-PWI nCBF and CTP nCBF, good between DSC-PWI nCBV and CTP nCBV and poorer between DSC-PWI nK2 and CTP nPS.

CONCLUSION

DSC-PWI and CTP CBF and CBV maps were comparable and interchangeable in the assessment of tumor vascularity, unlike DSC-PWI K2 and CTP PS maps that were more discordant in the analysis of tumor permeability. CTP could be an alternative method to quantify tumor neoangiogenesis when MRI is not available or when the patient does not tolerate it.

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.

Perfusion CT detects alterations in local cerebral flow of glioma related to IDH, MGMT and TERT status

BACKGROUND

The aim of this study was to investigate the relationship between tumor biology and values of cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), time to peak (TTP), permeability surface (PS) of tumor in patients with glioma.

METHODS

Forty-six patients with glioma were involved in the study. Histopathologic and molecular pathology diagnoses were obtained by tumor resection, and all patients accepted perfusion computed tomography (PCT) before operation. Regions of interests were placed manually at tumor and contralateral normal-appearing thalamus. The parameters of tumor were divided by those of contralateral normal-appearing thalamus to normalize at tumor (relative [r] CBV, rCBF, rMTT, rTTP, rPS). The relationships of the parameters, world health organization (WHO) grade, molecular pathological findings were analysed.

RESULTS

The rCBV, rMTT and rPS of patients are positively related to the pathological classification (P < 0.05). The values of rCBV and rPS in IDH mutated patients were lower than those IDH wild-type. The values of rCBF in patients with MGMT methylation were lower than those MGMT unmethylation (P < 0.05). The MVD of TERT wild-type group was lower than TERT mutated group (P < 0.05). The values of rCBV were significant difference in the four molecular groups divided by the combined IDH/TERT classification (P < 0.05). The progression free survival (PFS) and overall survival (OS) were significant difference in the four molecular groups divided by the combined IDH/TERT classification (P < 0.05).

CONCLUSIONS

Our study introduces and supports the changes of glioma flow perfusion may be closely related to its biological characteristics.

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study

OBJECTIVES

To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma.

METHODS

Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve.

RESULTS

The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909-0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice.

CONCLUSIONS

T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

Utility of dual-energy CT for predicting the vascularity of meningiomas

PURPOSE

Dual-energy computed tomography (DECT) can provide iodine, electron density (ED), and effective atomic number Z (Z_eff) maps, facilitating the identification of tissue types. We investigated whether DECT parameters can predict the vascularity of meningiomas.

METHOD

We acquired DECT and perfusion CT (PCT) images in 24 patients with histologically diagnosed meningioma. Regions of interest (ROIs) were placed at the tumor in iodine, ED, and Z_eff maps derived from DECT and in a blood volume (BV) map derived from PCT. To normalize these parameters' values, we divided them by the values of contralateral normal-appearing white matter, i.e., the relative (r)ED, rZ_eff, and rBV. The vascular density of the tumor specimens was immunohistochemically analyzed by calculating the von Willebrand factor-positive vessel wall. We calculated Pearson's correlation coefficients to determine the correlation with PCT/DECT parameters and an immunohistopathological index.

RESULTS

Contrast rZ_eff (r = 0.7020, p = 0.0001) and iodine (r = 0.5814, p = 0.0029) both had positive correlations with rBV derived from PCT. The rED values were negatively correlated with the rBV values (r = -0.4735, p = 0.0194), and the vascular density results confirmed positive correlations with rBV (r = 0.6909, p = 0.0002) and contrast rZ_eff (r = 0.4982, p = 0.0132) and a negative correlation with rED (r = -0.4265, p = 0.0377). Regarding the radiation exposure, the mean estimated volume CT dose index (CTDI_vol) of DECT was 33.1 ± 1.72 mGy, much lower than that of PCT (103.3 ± 4.65 mGy).

CONCLUSIONS

DECT predicted vascular density with lower radiation exposure compared to PCT. DECT could potentially replace PCT for evaluating the vascularity of meningiomas.