Amide proton transfer and chemical exchange saturation transfer MRI differentiates between growing and non-growing intracranial meningiomas: a pilot study.

AIM: To differentiate between growing and non-growing intracranial meningiomas using magnetisation transfer ratio (MTR) values with amide proton transfer (APT) and chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI). MATERIALS AND METHODS: Seventeen patients with suspected intracranial meningiomas who underwent APT-CEST MRI from November 2020 to April 2021 were evaluated retrospectively. MTR values on APT-CEST imaging as well as conventional MRI features were evaluated. These parameters were compared in growing meningiomas versus non-growing meningiomas and the findings compared with previous MRI examinations. ROC curve analysis was also performed to determine the diagnostic cut-offs for MTR. RESULTS: The cohort comprised 10 patients with growing meningiomas (two men [20%], eight women [80%]; mean age [standard deviation (SD)]: 59.9 years [16]) and seven patients with non-growing meningiomas (seven women [100%]; mean age [SD]: 63.9 years [18.6]). Significant differences were found in MTR values (0.0198 ± 0.0003 versus 0.0131 ± 0.0002; p<0.0001) between the growing meningiomas and non-growing meningiomas groups, respectively. The receiver operating characteristic (ROC) curve analysis showed that MTR values clearly differentiated between growing and non-growing meningiomas. At an area under the ROC curve (AUC) threshold of 0.0151, diagnostic sensitivity, specificity, positive predictive value, and negative predictive values for MTR were 100%, 85.7%, 90.9%, and 100%, respectively. CONCLUSION: Patients with growing meningiomas could be discriminated from patients with non-growing meningiomas, using the MTR values on post-growth tumour APT-CEST imaging.

Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images.

This study examined whether using an artificial neural network (ANN) helps beginners in diagnostic cardiac imaging to achieve similar results to experts when interpreting stress myocardial perfusion imaging (MPI). One hundred and thirty-eight patients underwent stress MPI with Tc-labeled agents. An expert and a beginner interpreted stress/rest MPI with or without the ANN and the results were compared. The myocardium was divided into 5 regions (the apex; septum; anterior; lateral, and inferior regions), and the defect score of myocardial blood flow was evaluated from 0 to 4, and SSS, SRS, and SDS were calculated. The ANN effect, defined as the difference in each of these scores between with and without the ANN, was calculated to investigate the influence of ANN on the interpreters' performance. We classified 2 groups (insignificant perfusion group and significant perfusion group) and compared them. In the same way, classified 2 groups (insignificant ischemia group and significant ischemia group) and compared them. Besides, we classified 2 groups (normal vessels group and multi-vessels group) and compared them. The ANN effect was smaller for the expert than for the beginner. Besides, the ANN effect for insignificant perfusion group, insignificant ischemia group and multi-vessels group were smaller for the expert than for the beginner. On the other hand, the ANN effect for significant perfusion group, significant ischemia group and normal vessels group were no significant. When interpreting MPI, beginners may achieve similar results to experts by using an ANN. Thus, interpreting MPI with ANN may be useful for beginners. Furthermore, when beginners interpret insignificant perfusion group, insignificant ischemia group and multi-vessel group, beginners may achieve similar results to experts by using an ANN.