In terms of the capability to diagnose prostate cancer of high Gleason score (≥8), there was no significant difference between MET and FDG. MET appears to be useful for detecting prostate cancer of both low and high Gleason score.
The feasibility of evaluating systolic and diastolic temporal parameters by a new program was validated. This program has the potential to evaluate both diastolic and systolic heterogeneous wall motions which express dyssynchrony in heart failure.
PurposeThe aim of this retrospective study was to assess the utility of a voxel-based analysis (VBA) method for 201Tl SPECT in glioma, compared to conventional ROI analysis.MethodsWe recruited 24 patients with glioma (high-grade 15; low-grade 9), for whom pre-operative 201Tl SPECT and MRI were performed. SPECT images were coregistered with MRI. The uptake ratio (UR) images of tumor to contralateral normal tissue were measured on early and delayed images, and the 201Tl retention index (RI) map was calculated from the early and delayed uptake ratio maps. In the ROI analysis, tumors were traced on a UR map, and the mean and maximal uptake ratio values on the early images were, respectively, defined as the mean and maximal UR. The mean and maximal RI values (mean and maximal RI) were calculated by division of the mean and maximal UR, respectively, on the delayed image by the mean and maximal UR on the early image. For the RI map calculated voxel by voxel, the maximal RI value was defined as VBA-RI. We evaluated sensitivity and accuracy of differential analysis with the mean and maximal UR, RI, and VBA-RI.ResultsThe high- and low-grade groups showed no significant difference in mean and maximal RI (0.98 ± 0.12 vs. 1.05 ± 0.09 and 0.98 ± 0.18 vs. 1.05 ± 0.14, respectively). The AUC and accuracy of the mean and maximal RI were 0.681 and 66.7 %, and 0.622 and 62.5 %, respectively. In contrast, VBA-RI was higher in high-grade than in low-grade glioma (1.69 ± 0.27 vs. 0.68 ± 0.66, p < 0.001). The AUC and accuracy of VBA-RI were 0.963 and 95.8 %, which are higher than those obtained for mean (p < 0.05) and maximal RI (p < 0.01). There was no significant difference in ROC between the VBA-RI and the mean UR (0.911, p = 0.456) and maximal UR (0.933, p = 0.639); however, the AUC, sensitivity, and diagnostic accuracy of VBA-RI were all higher than those of the mean and maximal UR.ConclusionThe voxel-based analysis method of 201Tl SPECT may improve diagnostic performance for gliomas, compared with ROI analysis.
Purpose
We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the convolutional neural network (CNN) final layer score for distinguishing between UAN and infundibular dilatation (ID).
Materials and methods
EIRL brain aneurysm (EIRL_BA) was used in this study. The subjects were 117 UAN and/or ID cases including 100 UAN lesions (average sizes of 2.56 ± 1.45 mm) and 40 ID lesions (average sizes of 1.75 ± 0.41 mm) in any of internal carotid artery, middle cerebral artery, and anterior communicating artery, and 123 normal controls. The sensitivity, specificity, and accuracy of EIRL_BA were determined for UAN and ID or UAN only. Furthermore, the relationship between the lesion category and score was examined using a linear regression analysis model, and the receiver operating characteristic (ROC) analysis was used to assess whether the scores represent UAN-like characteristics.
Results
EIRL_BA showed a total of 203 candidates (an average of 1.73/case) in UAN and/or ID cases and 98 candidates (an average of 0.80/case) in normal controls. For diagnosing either UAN/ID, EIRL_BA showed an overall sensitivity of 80%, specificity of 84.2%, and accuracy of 83.7%, resulting in the positive likelihood ratio of 5.0. For diagnosing UAN only, the overall sensitivity of 89.0, specificity of 82.6%, and accuracy of 83.2% resulting in the positive likelihood ratio of 5.1. In a linear regression analysis, the scores significantly increased in the candidates’ first and second ranks in UAN (p < 0.05) but not in ID. An ROC analysis using the score for diagnosing UAN showed an area under the curve of 0.836.
Conclusion
EIRL_BA is applicable for detecting small UAN, and the CNN’s final layer scores may be an effective index for discriminating UAN and ID and representing the likelihood of UAN.
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