Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging for differentiating malignant from benign orbital lymphproliferative disorders

Evaluation of orbital lesions with DCE-MRI: a literature review

PURPOSE

To provide a major review on the applications of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in evaluating orbital lesions. This review also outlines selected scenarios where DCE-MRI may be helpful.

METHODS

A comprehensive retrospective literature review of all English language publications on PubMed, EMBASE, and Google Scholar between 1994 and 2022. This literature review examined the specific applications and clinical scenarios surrounding the utility of DCE-MRI in orbital lesions and various findings that have been presented in the current literature.

RESULTS

DCE-MRI provides information on tissue physiology and permeability, beyond the anatomical features displayed on static imaging. Various measured parameters (qualitative, semi-quantitative, and quantitative) obtained by DCE-MRI have been used to differentiate between benign and malignant lesions, specific orbital lymphoproliferative diseases (OLPD), lacrimal gland lesions, and various rare orbital tumours. DCE-MRI has a limited role as an initial diagnostic imaging modality. However, DCE-MRI may prove to have benefit in predicting and monitoring treatment response in orbital lymphoma as a critical imaging study, but literature specific to orbital malignancies remains limited.

CONCLUSION

The value of DCE-MRI may be in situations of diagnostic uncertainty, where it may be an additional imaging aid following conventional imaging techniques. It may also act as a critical imaging modality for monitoring of orbital tumour treatment response, but the literature remains limited. Standardisation of imaging protocol, measured parameters, and statistical analysis remain limitations of this imaging technique.

MRI-based radiomics nomogram for distinguishing solitary fibrous tumor from schwannoma in the orbit: a two-center study

OBJECTIVES

To investigate the value of magnetic resonance imaging (MRI) radiomics for distinguishing solitary fibrous tumor (SFT) from schwannoma in the orbit.

MATERIALS AND METHODS

A total of 140 patients from two institutions were retrospectively included. All patients from institution 1 were randomized into a training cohort (n = 69) and a validation cohort (n = 35), and patients from institution 2 were used as an external testing cohort (n = 36). One hundred and six features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CET1WI). A radiomics model was built for each sequence using least absolute shrinkage and selection operator logistic regression, and radiomics scores were calculated. A combined model was constructed and displayed as a radiomics nomogram. Two radiologists jointly assessed tumor category based on MRI findings. The performances of the radiomics models and visual assessment were compared via area under the curve (AUC).

RESULTS

The performances of the radiomics nomogram combining T2WI and CET1WI radiomics scores were superior to those of the pooled readers in the training (AUC 0.986 vs. 0.807, p < 0.001), validation (AUC 0.989 vs. 0.788, p = 0.009), and the testing (AUC 0.903 vs. 0.792, p = 0.093), although significant difference was not found in the testing cohort. Decision curve analysis demonstrated that the radiomics nomogram had better clinical utility than visual assessment.

CONCLUSION

MRI radiomics nomogram can be used for distinguishing between orbital SFT and schwannoma, which may help tumor management by clinicians.

CLINICAL RELEVANCE STATEMENT

It is of great importance and challenging for distinguishing solitary fibrous tumor from schwannoma in the orbit. In the present study, an MRI-based radiomics nomogram were developed and independently validated, which could help the discrimination of the two entities.

KEY POINTS

• It is challenging to differentiate solitary fibrous tumor from schwannoma in the orbit due to similar clinical and image features. • A radiomics nomogram based on T2-weighted imaging and contrast-enhanced T1-weighted imaging has advantages over radiologists. • Radiomics can provide a non-invasive diagnostic tool for differentiating between the two entities.

Extracellular volume fraction derived from equilibrium contrast-enhanced CT as a diagnostic parameter in anterior mediastinal tumors

PURPOSE

To assess the usefulness of extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT (CECT) for diagnosing anterior mediastinal tumors.

METHOD

This study included 161 histologically confirmed anterior mediastinal tumors (55 low-risk thymomas, 57 high-risk thymomas, 32 thymic carcinomas, and 17 malignant lymphomas) that were assessed by pretreatment CECT. ECV fraction was calculated using measurements obtained within the lesion and the aorta on unenhanced and equilibrium phase CECT. ECV fraction was compared among anterior mediastinal tumors using one-way ANOVA or t-test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of ECV fraction to differentiate thymic carcinomas/lymphomas from thymomas.

RESULTS

ECV fraction differed significantly among the anterior mediastinal tumors (p < 0.001). ECV fraction of thymic carcinomas was significantly higher than those of low-risk thymomas, high-risk thymomas, and lymphomas (p < 0.001, p < 0.001, and p = 0.006, respectively). ECV fraction of lymphomas was significantly higher than that of low-risk thymomas (p < 0.001). ECV fraction was significantly higher in thymic carcinomas/lymphomas than in thymomas (40.1 % vs. 27.7 %, p < 0.001). The optimal cutoff value to differentiate thymic carcinomas/lymphomas from thymomas was 38.5 % (AUC, 0.805; 95 %CI, 0.736-0.863).

CONCLUSIONS

ECV fraction derived from equilibrium CECT is helpful in diagnosing anterior mediastinal tumors. High ECV fraction is indicative of thymic carcinomas/lymphomas, particularly thymic carcinomas.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid tumors

Objective

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) histograms were used to investigate whether their parameters can distinguish between benign and malignant parotid gland tumors and further differentiate tumor subgroups.

Materials and methods

A total of 117 patients (32 malignant and 85 benign) who had undergone DCE-MRI for pretreatment evaluation were retrospectively included. Histogram parameters including mean, median, entropy, skewness, kurtosis and 10th, 90th percentiles were calculated from time to peak (TTP) (s), wash in rate (WIR) (l/s), wash out rate (WOR) (l/s), and maximum relative enhancement (MRE) (%) mono-exponential models. The Mann–Whitney U test was used to compare the differences between the benign and malignant groups. The diagnostic value of each significant parameter was determined on Receiver operating characteristic (ROC) analysis. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of the different tumor groups.

Results

For both the benign and malignant groups and the comparisons among the subgroups, the parameters of TTP and MRE showed better performance among the various parameters. WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors. Warthin’s tumors showed significantly lower values on 10th MRE and significantly higher values on skewness TTP and 10th WOR, and the combination of 10th MRE, skewness TTP and 10th WOR showed optimal diagnostic performance (AUC, 0.971) and provided 93.12% sensitivity and 96.70% specificity. After Warthin’s tumors were removed from among the benign tumors, malignant parotid tumors showed significantly lower values on the 10th TTP (AUC, 0.847; sensitivity 90.62%; specificity 69.09%; P < 0.05) and higher values on skewness MRE (AUC, 0.777; sensitivity 71.87%; specificity 76.36%; P < 0.05).

Conclusion

DCE-MRI histogram parameters, especially TTP and MRE parameters, show promise as effective indicators for identifying and classifying parotid tumors. Entropy TTP and kurtosis MRE were found to be independent differentiating variables for malignant parotid gland tumors. The 10th WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors.

DSC and DCE Histogram Analyses of Glioma Biomarkers, Including IDH, MGMT, and TERT, on Differentiation and Survival

RATIONALE AND OBJECTIVES

The World Health Organization 2016 classification of central nervous system tumors added the molecular classification of gliomas and has guiding significance for the operation and prognosis of glioma patients. At present, the perfusion technique plays an important role in judging the malignant degree of glioma. To evaluate the performance of dynamic susceptibility contrast (DSC)- and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) histogram analyses in discriminating the states of molecular biomarkers and survival in glioma patients.

MATERIALS AND METHODS

Forty-three glioma patients who underwent DCE- and DSC-MRI were enrolled. Relevant molecular test results, including those on isocitrate dehydrogenase (IDH), O6-methylguanine-DNA methyltransferase (MGMT) and telomere reverse transcriptase (TERT), were collected. The mean relative cerebral blood volume of DSC-MRI and histogram parameters derived from DCE-MRI (volume transfer coefficient (K^trans), fractional volume of the extravascular extracellular space (V_e), fractional blood plasma volume (V_p), rate constant between the extravascular extracellular space and blood plasma (K_ep) and area under the curve (AUC)) were calculated. Differences in each parameter between gliomas with different expression states (IDH, MGMT, and TERT) were evaluated. The diagnostic efficiency of each parameter was analyzed. The overall survival of all patients was assessed.

RESULTS

The 10th percentile AUC (AUC = 0.830, sensitivity = 0.78, specificity = 0.80), the 90th percentile V_e (AUC = 0.816, sensitivity = 0.84, specificity = 0.79), and the mean K_ep (AUC = 0.818, sensitivity = 0.76, specificity = 0.78) provided the highest differential efficiency for IDH, MGMT, and TERT, respectively. Kaplan-Meier curves showed a significant difference between subjects with a 10th percentile AUC higher or lower than 0.028 (log-rank = 7.535; p = 0.006) for IDH and between subjects with different 90th percentile V_e values (log-rank = 6.532; p = 0.011) for MGMT.

CONCLUSION

Histogram DCE-MRI demonstrates good diagnostic performance in identifying different molecular types and for the prognostic assessment of glioma.

Arterial spin labeling and diffusion-weighted MR imaging: Utility in differentiating idiopathic orbital inflammatory pseudotumor from orbital lymphoma

PURPOSE

To assess arterial spin-labeling (ASL) and diffusion-weighted imaging (DWI) and in combination for differentiating between idiopathic orbital inflammatory pseudotumor (IOIP) and orbital lymphoma.

MATERIAL AND METHODS

A retrospective study was done on 37 untreated patients with orbital masses, suspected to be IOIP or orbital lymphoma that underwent ASL and DWI of the orbit. Quantitative measurement of tumor blood flow (TBF) and apparent diffusion coefficient (ADC) of the orbital lesion was done.

RESULTS

There was a significant difference (P = 0.001) in TBF between patients with IOIP (n = 21) (38.1 ± 6.2, 40.3 ± 7.1 ml/100 g/min) and orbital lymphoma (n = 16) (55.5 ± 7.1, 56.8 ± 7.9 ml/100 g/min) for both observers respectively. Thresholds of TBF used for differentiating IOIP from orbital lymphoma were 48, 46 ml/100 g/min revealed area under the curve (AUC) of (0.958 and 0.921), and accuracy of (86% and 83%) for both observers respectively. There was a significant difference (P = 0.001) in ADC between patients with IOIP (1.04 ± 0.19, 1.12 ± 0.23 × 10^-3 mm²/s) and orbital lymphoma (0.69 ± 0.10, 0.72 ± 0.11 × 10^-3 mm²/s) for both observers respectively. Thresholds of ADC used for differentiating IOIP from orbital lymphoma were 0.84 and 0.86 × 10^-3 mm²/s with AUC of (0.933 and 0.920), and accuracy of 89% and 90% for both observers respectively. The combined TBF and ADC used for differentiating IOIP from orbital lymphoma had AUC of (0.973 and 0.970) and accuracy of (91% and 89%) for both observers respectively.

CONCLUSION

TBF and ADC alone and in combination are useful for differentiating IOIP from orbital lymphoma.

Non-specific orbital inflammation: Current understanding and unmet needs

Non-specific orbital inflammation (NSOI) is a noninfectious inflammatory condition of the orbit. Although it is generally considered the most common diagnosis derived from an orbital biopsy, it is a diagnosis of exclusion, meaning that the diagnosis requires exclusion of a systemic process or another identifiable etiology of orbital inflammation. The clinical diagnosis of NSOI is ill-defined, but it is typically characterized by acute orbital signs and symptoms, including pain, proptosis, periorbital edema, chemosis, diplopia, and less commonly visual disturbance. NSOI poses a diagnostic and therapeutic challenge: The clinical presentations and histological findings are heterogeneous, and there are no specific diagnostic criteria or treatment guidelines. The etiology and pathogenesis of NSOI are poorly understood. Here we recapitulate our current clinical understanding of NSOI, with an emphasis on the most recent findings on clinical characteristics, imaging findings, and treatment outcomes. Furthermore, gene expression profiling of NSOI and its implications are presented and discussed.

Combined Diagnosis of Whole-Lesion Histogram Analysis of T1- and T2-Weighted Imaging for Differentiating Adrenal Adenoma and Pheochromocytoma: A Support Vector Machine-Based Study

PURPOSE

This study aimed to determine whether a combined diagnosis of whole-lesion histogram analysis of T1- and T2-weighted imaging based on support vector machine (SVM) can distinguish pheochromocytoma from adrenal adenoma.

METHODS

A pathology database was retrospectively appraised over a period of 7 years and we obtained 40 histopathologically proven adrenal adenomas and 20 pheochromocytomas with magnetic resonance images. The T1-weighted imaging (T₁WI, including both in phase and opposed phase) and T2-weighted imaging (T₂WI) images of each patients were analyzed using Mazda software. Nine parameters were selected as indicators of comparison: variance, skewness, kurtosis, mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile. The parameters with differential-diagnosis significance were used to establish the combined diagnostic model of SVM.

RESULTS

Among the 9 parameters extracted using histogram analysis, the 1st percentile, 10th percentile, and 50th percentile of T₁WI (in phase) and the skewness of T₂WI and almost all parameters of T₁WI (opposed phase), except variance and 99th percentile, showed statistical significance between groups. Among the above parameters, the area under the curve (AUC) of 10th percentile of T₁WI (opposed phase) was the largest with the value of 0.909 (100.0% sensitivity and 80.0% specificity). After the analysis of combined diagnosis was performed, the AUC of SVM model in testing set showed the value of 0.917 (85.0% accuracy).

CONCLUSIONS

Whole-lesion histogram analysis of T₁WI and T₂WI may help differentiate adrenal adenomas from pheochromocytomas. Furthermore, the combined diagnosis of T₁WI and T₂WI histogram based on SVM was more effective than most of individual histogram parameters.

Feasibility of using dynamic contrast-enhanced MRI for differentiating thymic carcinoma from thymic lymphoma based on semi-quantitative and quantitative models

AIM

To evaluate the value of using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived parameters to differentiate thymic carcinoma and thymic lymphoma based on semi-quantitative and quantitative models.

MATERIALS AND METHODS

Twenty-nine pathologically confirmed anterior mediastinum tumours in 29 patients were enrolled in this retrospective study, including 15 thymic carcinoma and 14 lymphoma patients. All the patients underwent pre-treatment mediastinum DCE-MRI. Both semi-quantitative and quantitative parameters were calculated and the volume transfer constant K^trans, the flux rate constant between extravascular extracellular space and plasma k_ep, the extravascular extracellular volume fraction v_e were obtained based on a modified Tofts model. DCE-MRI derived parameters were compared between thymic carcinoma and thymic lymphoma groups.

RESULTS

Thymic carcinoma had significantly lower k_ep (p=0.040) and higher v_e (p=0.018) than thymic lymphoma; however, there were no significant differences on K^trans and semi-quantitative parameters between the two groups. v_e had the highest area under the curve (cut-off value, 0.282; area under the curve, 0.748; sensitivity, 71.4%; specificity, 80%). The combination of k_ep and v_e could increase the diagnostic performance significantly (area under the curve, 0.752; sensitivity, 57.1%; specificity, 93.3%).

CONCLUSION

DCE-MRI derived parameters may have value in the differentiating thymic carcinoma and thymic lymphoma.