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.

Little is known about the neurologic sequelae of coronavirus disease 2019 (COVID-19). We assessed neuroimaging findings in 4 patients positive for COVID-19. All had abnormal mental status, deranged coagulation parameters, and markedly elevated D-dimer levels. CT/MR imaging showed a common pattern of multifocal subcortical/cortical petechial-type hemorrhages, while SWI showed more extensive multifocal abnormalities. The appearances are consistent with a thrombotic microangiopathy and may be due to the heightened level of thrombosis in patients with COVID-19.

BACKGROUND AND PURPOSE: Determining the diagnostic accuracy of different MR sequences is essential to design MR imaging protocols. The purpose of the study was to compare 3T sagittal FSE T2, STIR, and T1-weighted phase-sensitive inversion recovery in the detection of spinal cord lesions in patients with suspected or definite MS. MATERIALS AND METHODS: We performed a retrospective analysis of 38 patients with suspected or definite MS. Involvement of the cervical and thoracic cord segments was recorded on sagittal FSE T2, STIR, and T1-weighted phase-sensitive inversion recovery sequences independently by 2 readers. A consensus criterion standard read was performed with all sequences available. Sensitivity, specificity, and interobserver agreement were calculated for each sequence. RESULTS: In the cervical cord, the sensitivity of T1-weighted phase-sensitive inversion recovery (96.2%) and STIR (89.6%) was significantly higher (P < .05) than that of FSE T2 (50.9%), but no significant difference was found between T1-weighted phase-sensitive inversion recovery and STIR. In the thoracic cord, sensitivity values were 93.8% for STIR, 71.9% for FSE T2, and 50.8% for T1-weighted phase-sensitive inversion recovery. Significant differences were found for all comparisons (P < .05). No differences were detected in specificity. Poor image quality and lower sensitivity of thoracic T1-weighted phase-sensitive inversion recovery compared with the other 2 sequences were associated with a thicker back fat pad. CONCLUSIONS: The use of an additional sagittal sequence other than FSE T2 significantly increases the detection of cervical and thoracic spinal cord lesions in patients with MS at 3T. In the cervical segment, both STIR and T1-weighted phase-sensitive inversion recovery offer high sensitivity and specificity, whereas in the thoracic spine, STIR performs better than T1-weighted phase-sensitive inversion recovery, particularly in patients with a thick dorsal fat pad.

SUMMARY: Abnormally decreased T2/T2 FLAIR signal can be seen on brain imaging of patients who are experiencing clinical or subclinical seizures and can be associated with various intracranial pathologies. We identified 29 such patients. The abnormal signal was unilateral in 75.9% of patients. It affected various lobes of the brain, but only in the anterior circulation. In 28 patients (96.6%), there was corresponding decreased signal on DWI. The ADC was normal in all cases. In 26 patients (89.7%), there was corresponding low signal on SWI/gradient recalled-echo; 44.8% of patients underwent contrast-enhanced scans, and there was no abnormal enhancement. Twenty-two (75.9%) patients had documented clinical seizures on the day of imaging. The most frequent concomitant pathology was a subdural hematoma. Electroencephalograms obtained within 24 hours of imaging were available in 65.5%. Findings of all of these electroencephalograms were abnormal, and these electroencephalogram changes were either localized to the area of the abnormal MR imaging signal (where the signal was unilateral) or were bilateral (where the MR imaging changes were bilateral). In summary, decreased white matter T2/T2 FLAIR signal changes can be seen in patients with remarkably similar clinical findings (particularly seizures). These changes are often correlated with abnormal electroencephalogram activity localized to the involved lobes.