Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas.

PURPOSE: To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas. MATERIAL AND METHODS: A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas. RESULTS: There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10-3 mm2/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%. CONCLUSION: TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas.

Clinical Applications of Arterial Spin Labeling in Brain Tumors.

The aim of this review was to review the basic background, technique, and clinical applications of arterial spin labeling in brain tumors. Arterial spin labeling is used for differentiation of brain tumors from nonneoplastic lesions such as infarction and infection. It has a role in the grading of gliomas and in the differentiation of gliomas from lymphomas and metastasis. It is used for detection of the best biopsy site and prediction of treatment response. Arterial spin labeling is used for the assessment of extra-axial tumors and pediatric tumors. Last, it has a role in the differentiation of tumor recurrence from postradiation changes and in monitoring patients after therapy.

Differentiating Glioblastomas from Solitary Brain Metastases Using Arterial Spin Labeling Perfusion- and Diffusion Tensor Imaging-Derived Metrics.

OBJECTIVE: We sought to differentiate glioblastomas from solitary brain metastasis using arterial spin labeling perfusion (ASL)- and diffusion tensor imaging (DTI)-derived metrics. METHODS: A prospective study was done on 36 patients with provisional diagnosis of glioblastomas versus brain metastasis who underwent ASL and DTI of the brain. The tumor blood flow (TBF) and DTI metrics (fractional anisotropy [FA] and mean diffusivity [MD]) of the enhancing tumoral and peritumoral parts were measured. RESULTS: There was a significant difference of TBF (P = 0.001) and MD (P = 0.001) of the tumoral and peritumoral parts of glioblastoma and metastasis (P = 0.001). There was a significant difference of FA of peritumoral part (P = 0.001) and insignificant difference of tumoral part (P = 0.06) between glioblastomas and metastasis. The cutoff of TBF of tumoral and peritumoral parts used for differentiation were 29.7 and 17.8 (mL/100 g/minute) revealed an area under the curve (AUC) of 0.943 and 0.937 with accuracy of 91.7% and 88.9%. The cutoff of MD of tumoral and peritumoral parts were 1.27 and 1.33 (10-3 mm2/second) revealed AUC of 0.840 and 0.987 and accuracy of 83.3% and 91.7%, respectively. Combined TBF, MD, and FA of the peritumoral part revealed AUC of 0.984 and accuracy of 91.7%. CONCLUSIONS: A combination of ASL- and DTI-derived metrics of the peritumoral part can be used for differentiation of glioblastomas from solitary brain metastasis.

Differentiation of Primary Central Nervous System Lymphoma From Glioblastoma: Quantitative Analysis Using Arterial Spin Labeling and Diffusion Tensor Imaging.

OBJECTIVE: Differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma using arterial spin labeling perfusion and diffusion tensor imaging (DTI). METHODS: We performed a prospective study of 31 patients with a provisional diagnosis of PCNSL and glioblastoma who underwent conventional magnetic resonance imaging, DTI, and arterial spin labeling of the brain. The tumor blood flow (TBF), mean diffusivity (MD) plus fractional anisotropy (FA) of the mass were measured. The final diagnosis was confirmed by pathological examination. RESULTS: The TBF of PCNSL (26.41 ± 4.03 mL/100 g/minute) was significantly lower than that of glioblastoma (51.08 ± 3.9 mL/100 g/minute; P = 0.001). The TBF cutoff (35.73 mL/100 g/minute) used for differentiation showed area under the curve (AUC) of 0.93, accuracy of 95.2%, sensitivity of 91.7%, and specificity of 100%. The MD of PCNSL (0.87 ± 0.2X 10-3 mm2/second) was significantly lower than that of glioblastoma (0.87 ± 0.2 × 10-3 mm2/second; P = 0.01). The MD cutoff (0.935 × 10-3 mm2/second) used for differentiation showed an AUC of 0.73 and accuracy of 66.7% and a sensitivity of 75% and specificity of 55.6%. The FA of PCNSL (0.253 ± 0.05) was significantly greater than that of glioblastoma (0.135 ± 0.06; P = 0.001). The FA cutoff (0.185) used for differentiation revealed an AUC of 0.944 and accuracy of 85.7% and a sensitivity of 83.3% and specificity of 88.9%. The combined TBF, MD, and FA cutoffs revealed an AUC of 0.96 and accuracy of 95.5% and a sensitivity of 83.3% and specificity of 100%. CONCLUSION: The noninvasive imaging parameters using TBF and DTI might help in differentiating PCNSL from glioblastoma.

Differentiation of residual/recurrent gliomas from postradiation necrosis with arterial spin labeling and diffusion tensor magnetic resonance imaging-derived metrics.

PURPOSE: The aim of this study is to differentiate recurrent/residual gliomas from postradiation changes using arterial spin labeling (ASL) perfusion and diffusion tensor imaging (DTI)-derived metrics. METHODS: Prospective study was conducted upon 42 patients with high-grade gliomas after radiotherapy only or prior to other therapies that underwent routine MR imaging, ASL, and DTI. The tumor blood flow (TBF), fractional anisotropy (FA), and mean diffusivity (MD) of the enhanced lesion and related edema were calculated. The lesion was categorized as recurrence/residual or postradiation changes. RESULTS: There was significant differences between residual/recurrent gliomas and postradiation changes of TBF (P = 0.001), FA (P = 0.001 and 0.04), and MD (P = 0.001) of enhanced lesion and related edema respectively. The area under the curve (AUC) of TBF of enhanced lesion and related edema used to differentiate residual/recurrent gliomas from postradiation changes were 0.95 and 0.93 and of MD were 0.95 and 0.81 and of FA were 0.81 and 0.695, respectively. Combined ASL and DTI metrics of the enhanced lesion revealed AUC of 0.98, accuracy of 95%, sensitivity of 93.8%, specificity of 95.8%, positive predictive value (PPV) of 93.8%, and negative predictive value (NPV) of 95.8%. Combined metrics of ASL and DTI of related edema revealed AUC of 0.97, accuracy of 92.5%, sensitivity of 93.8%, specificity of 91.7%, PPV of 88.2%, and NPV of 95.7. CONCLUSION: Combined ASL and DTI metrics of enhanced lesion and related edema are valuable noninvasive tools in differentiating residual/recurrent gliomas from postradiation changes.

Pilot study of ultrasound parotid imaging reporting and data system (PIRADS): Inter-observer agreement.

AIM: To establish proposal ultrasound parotid imaging reporting and data system (PIRADS) for classification and prediction of malignancy of parotid lesions and to assess the inter-observer agreement of this system. SUBJECTS AND METHODS: Retrospective analysis of ultrasound and power Duplex images of 142 patients with parotid lesions by two reviewers. Parotid focal lesions were classified into nine patterns and then categorized into five groups: PIRADS 1, definitively benign; PIRADS 2, probably benign; PIRADS 3, indeterminate; PIRADS 4, probably malignant; and PIRADS 5, highly suggestive malignant. THE RESULTS: There was excellent interobserver agreement of both reviewers for patterns and PIRADS (K=0.84, P=0.001) with 92% percent agreement. There was excellent agreement of PIRADS 1 (K=1.00, P=0.001), PIRADS 2 (K=0.97, P=0.001), PIRADS 3 (K=0.86, P=0.001) and PIRADS 5 (K=0.88, P=0.001) and good agreement of PIRADS 4 (K=0.67, P=0.001). The Odds ratio of PIRADS 3, 4 and 5 were 1.36 (95% CI=0.39-4.55), 7.11 (95% CI=3.02-11.15) and 8.27 (95% CI=3.49-10.27) respectively. The accuracy was 92% and 90%, sensitivity was 79% and 65%, specificity was 94% and 96% of PIRADS of both reviewers respectively. CONCLUSION: The proposed PIRADS is a reliable non-invasive imaging modality that can be used for categorizing parotid lesions and prediction of malignancy.

Dynamic Susceptibility Contrast Perfusion-Weighted Magnetic Resonance Imaging and Diffusion-Weighted Magnetic Resonance Imaging in Differentiating Recurrent Head and Neck Cancer From Postradiation Changes.

PURPOSE: The aim of this study was to assess dynamic susceptibility contrast (DSC) perfusion-weighted magnetic resonance (MR) imaging and diffusion-weighted MR imaging in differentiating recurrent head and neck cancer from postradiation changes. METHODS: A prospective study was done on 41 patients with head and neck cancer after radiotherapy who underwent diffusion-weighted MR imaging, DSC perfusion-weighted MR imaging, and routine postcontrast MR imaging. The apparent diffusion coefficient (ADC) map and time signal intensity curve of the lesion were created. The ADC value, DSC percentage (DSC%), and contrast enhancement percentage of the lesion were calculated. The final diagnosis was done with biopsy. RESULTS: There was significant difference (P = 0.001) in ADC between recurrent cancer (0.94 ± 0.16 × 10mm/s) and postradiation changes (1.37 ± 0.12 × 10mm/s). There was significant difference (P = 0.001) in DSC% of recurrent cancer (30.9% ± 5.16%) and postradiation changes (12.1% ± 3.06%). Selection of ADC equal to or less than 1.07 × 10mm/s and DSC% greater than 16.6% to predict recurrence have areas under the curve of 0.822 and 0.900 and accuracy of 92.7% and 95.1%, respectively. Combination of ADC and DSC% has are under the curve of 0.992 and accuracy of 97.6%. CONCLUSIONS: Combined ADC and DSC% are noninvasive imaging parameters that can play a role in the differentiation of recurrent head and neck cancer from postradiation changes.

Time resolved imaging of contrast kinetics (TRICKS) MR angiography of arteriovenous malformations of head and neck.

PURPOSE: To evaluate vasculature of arteriovenous malformations (AVMs) of head and neck with time resolved imaging of contrast kinetics (TRICKS) MR angiography (MRA). MATERIAL AND METHODS: Prospective study was conducted upon 19 patients (age range, 12-29 years; mean age 18 years; 10 males and 9 females) with AVM of head and neck. TRICKS-MRA of head and neck was performed during injection of contrast medium. Post processing with reconstruction of the images was done. Two independent readers assessed the overall TRICKS-MRA image quality score using a 5-point scale and depiction of the main arterial feeders, nidus, and venous drainage using 3 points scale. The Kappa test for interobserver agreement was done. The AVMs were evaluated morphologically in terms of number and origin of the main arterial feeders, the location and size of nidus either small (>2 cm) or large (>2 cm) and the draining veins into the superficial or deep venous drainage. RESULTS: The average TRICKS-MRA image quality score as judged by reader 1 was 3.89 ± 1.15 and that as judged by reader 2 was 3.89 ± 0.10, which yielded excellent interobserver agreement (k=0.77, 95% CI=0.53-0.98, r=0.78, P=0.001). The interobserver agreement of both readers was excellent for the arterial feeders (k=0.81, 95% CI=0.57-1.00, r=0.83, P=0.001), excellent for the nidus (k=0.91, 95% CI=0.75-1.00, r=0.92, P=0.001), and good for the venous drainage (k=0.77, 95% CI=0.53-0.98, r=0.78, P=0.001). The arterial feeders were single (n=14) or multiple (n=5), the nidus was large (n=16) or small (n=3) and the venous drainage was into the internal jugular (n=17) or the external jugular (n=2) veins. Three patients with small nidus and single arterial feeder were treated with sclerotherapy. Eleven patients with large nidus and single arterial feeder were referred for embolization. Combined embolization and surgery were done for five patients with large nidus and multiple arterial feeders. CONCLUSION: We concluded that TRICKS-MRA is a reliable non invasive tool for evaluation of the feeding arteries, the nidus and the draining veins of AVMs of head and neck. TRICKS-MRA can be used for evaluation and treatment planning of AVMs of head and neck.

Role of perfusion magnetic resonance imaging in cervical lymphadenopathy.

PURPOSE: To assess the role of perfusion magnetic resonance (MR) imaging in patients with cervical lymphadenopathy. MATERIALS AND METHODS: Dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging was performed on 45 cervical lymph nodes after a bolus injection of gadolinium-DTPA (0.1 mmol/kg body weight). Time signal intensity curve was created and dynamic susceptibility contrast (DSC) percentage of the lymph nodes was calculated. Receiver operating characteristic curve analysis was used to investigate whether DSC percentage could aid in the characterization of cervical lymphadenopathy. RESULTS: The mean (SD) DSC percentage of malignant nodes (44.8% [6.2%]) was significantly different (P = 0.001) from that of benign nodes (28.8% [4.8%]). The mean (SD) DSC percentage of metastatic nodes (48.72% [2.4%]) was significantly different (P = 0.001) than that of lymphoma (37.09% [3.5%]). The DSC percentage threshold value used for differentiating malignant from benign nodes and metastatic from lymphomatous nodes were 34.3% and 43.5%, with areas under the curve of 0.95 and 0.97, respectively. CONCLUSIONS: Perfusion MR imaging is a noninvasive promising method that can be used for differentiation of malignant from benign cervical lymph nodes, and it helps in the characterization of malignant cervical lymphadenopathy.

Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors.

The aim of this study was to correlate the apparent diffusion coefficient (ADC) value of invasive ductal carcinoma with pathological prognostic factors. A prospective study was conducted on 59 untreated female patients (mean age 46 years) with invasive ductal carcinoma. All patients were examined at 1.5 Tesla using dedicated bilateral breast coil. They underwent diffusion weighted MR imaging of the breast using a single shot echo planar imaging with a b-factor of 200 and 400 sec/mm2. Apparent diffusion coefficient (ADC) maps were reconstructed. The ADC value of the breast cancer was calculated and correlated with the pathologic prognostic factors (tumor size, grade and lymph nodes). The mean ADC values of invasive ductal carcinoma were significantly lower in patients with high grade, large breast cancer as well as those with axillary lymph nodes metastasis in a statistically significant way (p = 0.001 for the three factors). The mean ADC value of invasive ductal carcinoma was correlated with histologic grade (r = -0.675, p = 0.001), tumor size (r = 0.504, p = 0.001) and showed lower ADC values with positive lymph node metastasis. Apparent diffusion coefficient value is correlated with pathological parameters of invasive ductal carcinoma. The lower ADC values are associated with higher histological grade, larger tumor size and presence of axillary lymph nodes. So, the ADC value can be considered as a promising prognostic parameter that may identify highly aggressive breast cancer.

Diffusion-weighed MR of the thyroid gland in Graves' disease: assessment of disease activity and prediction of outcome.

RATIONALE AND OBJECTIVES: To assess the activity and clinical course of Graves' disease with diffusion-weighted magnetic resonance (MR) imaging. MATERIALS AND METHODS: Fifty-one patients with Graves' disease and 25 volunteers underwent diffusion MR imaging of the thyroid gland using a single shot echo-planar imaging with b-factor of 0, 300 and 600 second/mm(2). The apparent diffusion coefficient (ADC) values of the thyroid gland were calculated. Patients with active Graves' disease included untreated patients at initial diagnosis (n = 12), patients under antithyroid drugs (n = 11), and patients in relapse after withdrawal of therapy (n = 13). Patients with inactive disease had a remission of hyperthyroidism (n = 15). RESULTS: The mean ADC values of thyroid gland with active Graves' disease was 0.65 +/- 0.03 x 10(-3) mm(2)/second in patients at initial diagnosis, 0.81 +/- 0.02 x 10(-3) mm(2)/second in patients undergoing antithyroid drug and 0.72 +/- 0.07 x 10(-3) mm(2)/second in patients with relapse of hyperthyroidism. The mean ADC of patients with remission was 0.94 +/- 0.03 x 10(-3) mm(2)/second and for normal volunteer was 1.06 +/- 0.08 x 10(-3) mm(2)/second. There was significant difference in the ADC value of patients with active disease and remission (P = .001). The cutoff ADC value used for differentiating patients with active disease from patients with remission was 0.82 x 10(-3) mm(2)/second. The mean ADC value of thyroid gland had positive correlation with thyroid-stimulating hormone (r = 0.87, P = .001) and negative correlation with serum T4 (r = -0.82, P = .001) and serum T3 (r = -0.71, P = .001). CONCLUSIONS: The ADC value of the thyroid gland is a promising non invasive parameter for diagnosis of different clinical stages of Graves' disease. Hence it can be used to assess the activity and predict the outcome of patients during and after medical treatment.

Diffusion weighted MR imaging of the breast.

The aim of this work is to review the techniques and clinical applications of diffusion-weighted magnetic resonance (MR) imaging of the breast. Diffusion-weighted MR imaging plays a role in the differentiation breast cancer from benign lesions, the characterization of malignancy, and the detection of tumor extension. The apparent diffusion coefficient of breast cancer is correlated with tumor cellularity and some prognostic factors of breast cancer. It can be used for the differentiation of recurrent tumors from posttreatment changes and monitoring of patients after chemotherapy. Diffusion-weighted MR imaging is used for the characterization of breast mass, diagnosis, and the grading and staging of breast cancer, as well as prediction of the responses of patients with breast cancer to chemotherapy.

Characterization of pediatric head and neck masses with diffusion-weighted MR imaging.

We aimed to assess the clinical usefulness of the ADCs calculated from diffusion-weighted echo-planar MR images in the characterization of pediatric head and neck masses. This study included 78 pediatric patients (46 boys and 32 girls aged 3 months-15 years, mean 6 years) with head and neck mass. Routine MR imaging and diffusion-weighted MR imaging were done on a 1.5-T MR unit using a single-shot echo-planar imaging (EPI) with a b factor of 0.500 and 1,000 s mm(-2). The ADC value was calculated. The mean ADC values of the malignant tumours, benign solid masses and cystic lesions were (0.93 +/- 0.18) x 10(-3), (1.57 +/- 0.26) x 10(-3) and (2.01 +/- 0.21 ) x 10(-3) mm(2) s(-1), respectively. The difference in ADC value between the malignant tumours and benign lesions was statistically significant (p < 0.001). When an apparent diffusion coefficient value of 1.25 x 10(-3) mm(2) s(-1) was used as a threshold value for differentiating malignant from benign head and neck mass, the best results were obtained with an accuracy of 92.8%, sensitivity of 94.4%, specificity of 91.2%, positive predictive value of 91% and negative predictive value of 94.2%. Diffusion-weighted MR imaging is a new promising imaging approach that can be used for characterization of pediatric head and neck mass.