Table 1. The characteristics and outcomes of the included studies.
| Study | Participants | Study information | Results | |
|---|---|---|---|---|
| Rabinovici et al. 16 (2011) | 62 AD, 45 FTD, 25 control | Visual assessment based on hypometabolism pattern and quantitative classification based on the ROI with the lowest Z score | 1) Visual assessment | |
| - AD vs. FTD: Sn 75%–80%, Sp 83%–85%, PPV 87%–88%, NPV 70-74% | ||||
| 2) Quantitative classification | ||||
| - AD vs. FTD: Sn 73%, Sp 98%, PPV 98%, NPV 71%, AUC 0.910 (95% CI, 0.851–0.971) | ||||
| Dukart et al. 12 (2011) | 21 AD, 14 FTD, 13 control | Quantitative classification with: 1) whole brain voxel-based analysis and 2) disease-specific ROI-based analysis | 1) Accuracy of whole brain-based analysis | |
| - AD vs. FTD: 82.9% | ||||
| 2) Accuracy of ROIs based analysis | ||||
| - AD vs. FTD: 80.0% | ||||
| Poljansky et al. 15 (2011) | 16 AD, 16 FTD (9 bvFTD), 4 nfvPPA, 3 svPPA), 16 MCI | Visual assessment based on hypometabolism pattern (the severity ranged from 0 to 3) and quantitative classification with SPM analysis | 1) Visual assessment | |
| - bvFTD vs. AD: Sn 89%, Sp 94% | ||||
| - FTD (bvFTD, svPPA, PPA) vs. AD: Sn 81%, Sp 94% | ||||
| - FTD vs. MCI: Sn 81%, Sp 64% | ||||
| Panegyres et al. 23 (2009) | 49 AD, 17 FTD, 6 DLB, 6 PPA, 11 depression | Visual assessment with 3D-SSP | 1) AD vs. non-AD: Sn 78%, Sp 81%, PLR 4.11, NLR 0.27 | |
| 2) Diagnostic accuracy of each dementia subtype | ||||
| - FTD: Sn 53% (29%–77%), Sp 95% (90%–100%) | ||||
| - LBD: Sn 83% (53%–100%), Sp 99% (97%–100%) | ||||
| - PPA: Sn 50% (10%–90%), Sp 100% (99%–100%) | ||||
| Mosconi et al. 14 (2008) | 110 controls, 114 MCI, 199 AD, 98 FTD, 27 DLB | Automated voxel-based comparison of disease-specific patterns (cortical and hippocampal pattern) | 1) Analysis of cortical pattern | |
| - AD vs. Normal: Sn 99%, Sp 98% (98% accuracy) | ||||
| - AD vs. DLB: Sn 99%, Sp 71% (97% accuracy) | ||||
| - AD vs. FTD: Sn 99%, Sp 65% (97% accuracy) | ||||
| - DLB vs. FTD: Sn 71%, Sp 65% (68% accuracy) | ||||
| 2) analysis of hippocampal pattern | ||||
| - AD vs. Normal: Sn 98%, Sp 96% (97% accuracy) | ||||
| - AD vs. DLB: Sn 98%, Sp 75% (89% accuracy) | ||||
| - AD vs. FTD: Sn 98%, Sp 75% (89% accuracy) | ||||
| - DLB vs. FTD: Did not significantly discriminate | ||||
| 3) Cortical + hippocampal pattern | ||||
| - AD vs. DLB: Sn 98%, Sp 100% (99% accuracy) | ||||
| - AD vs. FTD: Sn 98%, Sp 94% (97% accuracy) | ||||
| Jagust et al. 20 (2007) | (pathology confirmed) 25 AD, 19 non-AD | Visual assessment based on hypometabolism pattern | - AD vs. non-AD: Sn 84%, Sp 74%, PPV 81%, NPV 78% | |
| Foster et al. 13 (2007) | (pathology confirmed) 31 AD, 14 FTD | Visual assessment of transaxial images and SSP images | 1) Transaxial 18F-FDG PET | |
| - Mean PPV for FTD/NPV for AD 68% | ||||
| - Mean NPV for FTD/PPV for AD 91% | ||||
| - PLR for FTD 14.8, NLR for FTD 0.4 | ||||
| - PLR for AD 2.5, NLR for AD 0.2 | ||||
| 2) SSP 18F-FDG PET | ||||
| - Mean PPV for FTD/NPV for AD 93% | ||||
| - Mean NPV for FTD/PPV for AD 89% | ||||
| - PLR for FTD 36.5, NLR for FTD 0.3 | ||||
| - PLR for AD 3.5, NLR for AD 0.03 | ||||
| Perini et al. 21 (2021) | 177 MCI, 100 dementia with uncertain diagnosis (43 AD, 24 FTD, 14 DLB, 7 others, 12 unspecified dementia) | Visual assessment with standardized uptake value ratios based on ROIs and voxel-wise Z-score SSP analysis | - AD vs. non-AD: Sn 76%, SP 95%, ACC 86%, PLR 13.7, NLR 0.2 | |
| - FTD vs. non-FTD: Sn 82%, SP 90%, ACC 88%, PLR 8.5, NLR 0.2 | ||||
| - DLB vs. non-DLB: Sn 75%, SP 95%, ACC 92%, PLR 15.6, NLR 0.3 | ||||
| Vijverberg et al. 25 (2016) | 27 bvFTD, 84 non-bvFTD | Visual assessment | - bvFTD vs. non-bvFTD: Sn 70%, Sp 93%, PPV 76%, NPV 91% | |
| Taswell et al. 24 (2015) | 24 AD, 19 logopenic PPA, 16 nfvPPA, 13 svPPA, 14 CBS | Visual assessment with 3D-SSP technique | - AD vs. non-AD pathology: PPV 0.95, NPV 0.42, PLR 2.71, NLR 0.19 | |
| O'Brien et al. 18 (2014) | 38 AD, 30 DLB, 30 controls | Visual assessment based on hypometabolism pattern and quantitative classification with SPM analysis | 1) Visual assessment | |
| - AD vs. DLB: Sn 74%, Sp 70%, AUC 0.799 ± 0.059 | ||||
| 2) Quantitative classification | ||||
| - AD vs. DLB: ROI medial occipital/MTL, AUC 0.855 ± 0.055 | ||||
| Spehl et al. 19 (2015) | 15 AD, 6 PCA, 12 DLB | Visual assessment based on hypometabolism pattern and quantitative classification with SPM analysis | 1) Visual assessment: overall accuracy 83% | |
| - PCA: Sn 83%, Sp 85% | ||||
| - DLB: Sn 83%, Sp 81% | ||||
| 2) Quantitative classification: overall accuracy 73% | ||||
| - PCA: Sn 83%, Sp 93%, AUC 0.91 | ||||
| - DLB: Sn 75%, Sp 86%, AUC 0.85 | ||||
| - AD: Sn 67%, Sp 78%, AUC 0.77 | ||||
| Tripathi et al. 22 (2014) | 61 AD, 18 FTD, 9 DLB, 13 others (CJD, VD, PCA, mixed dementia) | Visual assessment | - AD vs. non-AD: Sn 93.4%, Sp 87.5% | |
| - FTD vs. non-FTD: Sn 88.8%, Sp 100% | ||||
| - DLB vs. non-DLB: Sn 66.6%, Sp 98.3% | ||||
| Lim et al. 17 (2009) | 10 AD, 14 DLB | Visual assessment based on hypometabolism pattern | - DLB vs. AD: Sn 83%, Sp 93% | |
AD: Alzheimer’s dementia, FTD: frontotemporal dementia, ROI: region of interest, Sn: sensitivity, Sp: specificity, PPV: positive predictive value, NPV: negative predictive value, AUC: area under curve, CI: confidence interval, bvFTD: behavioral variant frontotemporal dementia, nfvPPA: nonfluent variant primary progressive aphasia, svPPA: semantic variant primary progressive aphasia, MCI: mild cognitive impairment, SPM: statistical parametric mapping, PPA: primary progressive aphasia, DLB: dementia with Lewy bodies, 3D: 3-dimensional, SSP: stereotactic surface projection, PLR: positive-likelihood ratio, NLR: negative-likelihood ratio, CBS: corticobasal syndrome, PCA: posterior cortical atrophy, CJD: Creutzfeldt-Jakob disease, VD: vascular dementia.