Do We Need Gadolinium-Based Contrast Agents for Routine MRI Surveillance of Unoperated Pituitary Macroadenoma?

BACKGROUND AND PURPOSE: The use of gadolinium-based contrast agents contributes to the cost of MR imaging and prolongs image-acquisition time. There are also recent concerns regarding gadolinium deposition, particularly in patients who require frequent follow-up MRIs. The purpose of this study was to assess whether gadolinium-based contrast agents are needed during MR imaging follow-up for unoperated pituitary macroadenoma. MATERIALS AND METHODS: A total of 105 patients with unoperated pituitary macroadenoma who underwent follow-up MR imaging of the sella were included in this retrospective study. The craniocaudal dimension, cavernous sinus invasion grading, and optic pathway compression were assessed independently on coronal T2WI and compared with coronal T1-weighted images with gadolinium-based contrast agents (T1 postcontrast images). The agreement between the T2WI and T1 postcontrast images for the craniocaudal dimension was assessed using the intraclass correlation coefficient; for the cavernous sinus invasion and optic pathway compression, it was assessed using κ statistics. RESULTS: There was excellent agreement for the craniocaudal dimensions between T2WI and T1 postcontrast images (intraclass correlation coefficient = 0.96, P < .001; 95% CI, 0.84-0.99). Additionally, there was almost-perfect agreement between cavernous sinus invasion and optic pathway compression between T2WI and T1 postcontrast images, with κ = 0.95 and 0.84, respectively (P < .001). CONCLUSIONS: MR imaging of the sella without the use of gadolinium-based contrast agents could potentially be considered for the follow-up of unoperated pituitary macroadenomas. This choice can reduce the MR imaging examination cost and acquisition time and avoids potential adverse effects of gadolinium-based contrast agents.

Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field.

BACKGROUND AND PURPOSE: Accurate follow-up of metastatic brain tumors has important implications for patient prognosis and management. The aim of this study was to develop and evaluate the accuracy of a semiautomated algorithm in detecting growing or shrinking metastatic brain tumors on longitudinal brain MRIs. MATERIALS AND METHODS: We used 50 pairs of successive MR imaging datasets, 30 on 1.5T and 20 on 3T, containing contrast-enhanced 3D T1-weighted sequences. These yielded 150 growing or shrinking metastatic brain tumors. To detect them, we completed 2 major steps: 1) spatial normalization and calculation of the Jacobian operator field to quantify changes between scans, and 2) metastatic brain tumor candidate segmentation and detection of volume-changing metastatic brain tumors with the Jacobian operator field. Receiver operating characteristic analysis was used to assess the detection accuracy of the algorithm, and it was verified with jackknife resampling. The reference standard was based on detections by a neuroradiologist. RESULTS: The areas under the receiver operating characteristic curves were 0.925 for 1.5T and 0.965 for 3T. Furthermore, at its optimal performance, the algorithm achieved a sensitivity of 85.1% and 92.1% and specificity of 86.7% and 91.3% for 1.5T and 3T, respectively. Vessels were responsible for most false-positives. Newly developed or resolved metastatic brain tumors were a major source of false-negatives. CONCLUSIONS: The proposed algorithm could detect volume-changing metastatic brain tumors on longitudinal brain MRIs with statistically high accuracy, demonstrating its potential as a computer-aided change-detection tool for complementing the performance of radiologists, decreasing inter- and intraobserver variability, and improving efficacy.

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. MATERIALS AND METHODS: Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. RESULTS: The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 (P = .021), reader 2 (P = .035), and reader 3 (P = .007). CONCLUSIONS: Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.

Accuracy of brain imaging in the diagnosis of idiopathic intracranial hypertension.

AIM: To investigate the accuracy of individual and combinations of signs on brain magnetic resonance imaging (MRI) and magnetic resonance venography (MRV) in the diagnosis of idiopathic intracranial hypertension (IIH). MATERIALS AND METHODS: This study was approved by the institutional research ethics board without informed consent. Forty-three patients and 43 control subjects were retrospectively identified. Each patient and control had undergone brain MRI and MRV. Images were anonymized and reviewed by three neuroradiologists, blinded to clinical data, for the presence or absence of findings associated with IIH. The severity of stenosis in each transverse sinus was graded and summed to generate a combined stenosis score (CSS). The sensitivity, specificity, and likelihood ratios (LR) were calculated for individual and combinations of signs. RESULTS: Partially empty sella (specificity 95.3%, p < 0.0001), flattening of the posterior globes (specificity 100%, p < 0.0001), and CSS <4 (specificity 100%, p < 0.0001) were highly specific for IIH. The presence of one sign, or any combination, significantly increased the odds of a diagnosis of IIH (LR+ 18.5 to 46, p < 0.0001). Their absence, however, did not rule out IIH. CONCLUSIONS: Brain MRI with venography significantly increased the diagnostic certainty for IIH if there was no evidence of a mass, hydrocephalus, or sinus thrombosis and one of the following signs was present: flattening of the posterior globes, partially empty sella, CSS <4. However, absence of these signs did not exclude a diagnosis of IIH.