| Literature DB >> 26448940 |
Hongchen Zhang1, Jian Zhu1, Fayong Ke1, Mingzhe Weng1, Xiangsong Wu1, Maolan Li1, Zhiwei Quan1, Yingbin Liu1, Yong Zhang1, Wei Gong1.
Abstract
Hilar cholangiocarcinoma (HCC) remains one of the most difficult tumors to stage and treat. The aim of the study was to assess the diagnostic efficiency of computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computer tomography (PET/CT) in evaluating the resectability of HCC. A systematic search was performed of the PubMed, EMBASE, and Cochrane databases. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated for individual studies and pooled data as well as test for heterogeneity and public bias. Our data showed that CT had the highest pooled sensitivity at 95% (95% CI: 91-97), whereas PET/CT had the highest pooled specificity at 81% (95% CI: 69-90). The area under the curve (AUC) of CT, MRI, and PET/CT was 0.9269, 0.9194, and 0.9218, respectively. In conclusion, CT is the most frequently used imaging modality to assess HCC resectability with a good sensitivity and specificity. MRI was generally comparable with that of CT and can be used as an alternative imaging technique. PET/CT appears to be the best technique in detecting lymph node and distant metastasis in HCC but has no clear role in helping to evaluate issues of local resectability.Entities:
Mesh:
Year: 2015 PMID: 26448940 PMCID: PMC4569758 DOI: 10.1155/2015/497942
Source DB: PubMed Journal: Biomed Res Int Impact factor: 3.411
Figure 1Flow chart and study selection.
Main characteristics of the included studies.
| Author | Year of publication | Country | Number of patients | Age, mean (range) | Patients selection | Modality | Study design | QUADAS Score |
|---|---|---|---|---|---|---|---|---|
|
Cha et al. [ | 2000 | Korea | 21 | 56 (34–69) | Consecutive | CT | Retrospective | 11 |
| Lee et al. [ | 2006 | Korea | 55 | 59 (29–76) | NA | CT | Retrospective | 12 |
| Aloia et al. [ | 2007 | USA | 32 | 67 | Consecutive | CT | Prospective | 11 |
| Endo et al. [ | 2007 | Japan | 20 | 65 (50–80) | Consecutive | CT | NA | 12 |
| Unno et al. [ | 2007 | Japan | 24 | 64 | Consecutive | CT | Retrospective | 11 |
| Yin et al. [ | 2007 | China | 31 | 53 (21–74) | Consecutive | MRI | NA | 12 |
| Masselli et al. [ | 2008 | Italy | 15 | 58 (49–74) | NA | MRI | Retrospective | 12 |
| Park et al. [ | 2008 | Korea | 27 | 60 (43–80) | NA | CT | Retrospective | 12 |
| Li et al. [ | 2008 | Germany | 17 | 62 | NA | PET/CT | Prospective | 11 |
| Kim et al. [ | 2008 | Korea | 123 | 60 (28–78) | Consecutive | CT | Prospective | 12 |
| Chen et al. [ | 2009 | China | 75 | 60 | Consecutive | CT | Prospective | 12 |
| Yu et al. [ | 2010 | China | 13 | 65 (54–79) | NA | CT | NA | 11 |
| Ryoo et al. [ | 2010 | Korea | 60 | 66 (45–77) | Consecutive | MRI | Retrospective | 12 |
| Cannon et al. [ | 2012 | USA | 110 | 64 (21–88) | Consecutive | CT | Retrospective | 11 |
| Gu et al. [ | 2012 | China | 32 | 56 | NA | PET/CT | Retrospective | 10 |
| Nagakawa et al. [ | 2014 | Japan | 13 | 65 (39–83) | NA | CT | NA | 11 |
NA = data not available.
TP, FP, FN, TN, and diagnostic performance of CT.
| Author | TP | FP | FN | TN | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | Accuracy (%) |
|---|---|---|---|---|---|---|---|---|---|
| Cha et al. [ | 6 | 6 | 0 | 9 | 100 | 60 | 50.0 | 100 | 71.4 |
| Lee et al. [ | 30 | 12 | 2 | 11 | 93.8 | 47.8 | 71.4 | 84.6 | 74.5 |
| Aloia et al. [ | 17 | 1 | 3 | 11 | 85 | 91.7 | 94.4 | 78.6 | 87.5 |
| Endo et al. [ | 14 | 1 | 0 | 5 | 100 | 83.3 | 93.3 | 100 | 95 |
| Unno et al. [ | 15 | 4 | 0 | 5 | 100 | 55.6 | 78.9 | 100 | 83 |
| Park et al. [ | 16 | 4 | 0 | 7 | 100 | 63.6 | 80.0 | 100 | 85 |
| Kim et al. [ | 74 | 12 | 8 | 29 | 90.2 | 70.7 | 86.0 | 78.4 | 83.7 |
| Chen et al. [ | 45 | 5 | 2 | 23 | 95.7 | 82.1 | 90.0 | 92.0 | 91 |
| Yu et al. [ | 7 | 1 | 0 | 5 | 100 | 83.3 | 87.5 | 100 | 92 |
| Cannon et al. [ | 37 | 22 | 0 | 51 | 100 | 69.9 | 62.7 | 100 | 80 |
| Nagakawa et al. [ | 10 | 2 | 0 | 1 | 100 | 33.3 | 83.3 | 100 | 85 |
TP: true positive; FP: false positive; TN: true negative; FN: false negative.
TP, FP, FN, TN, and diagnostic performance of MRI.
| Author | TP | FP | FN | TN | Sensitivity | Specificity | PPV | NPV | Accuracy |
|---|---|---|---|---|---|---|---|---|---|
| Yin et al. [ | 16 | 3 | 0 | 12 | 100 | 80 | 84.2 | 100 | 90.3 |
| Masselli et al. [ | 11 | 1 | 0 | 3 | 100 | 75 | 91.7 | 100 | 93.3 |
| Park et al. [ | 17 | 4 | 0 | 6 | 100 | 60 | 80.9 | 100 | 85 |
| Kim et al. [ | 74 | 12 | 8 | 29 | 90.2 | 70.7 | 86.0 | 78.4 | 83.7 |
| Ryoo et al. [ | 51 | 2 | 2 | 5 | 96.2 | 71.5 | 96.2 | 71.4 | 93.3 |
TP, FP, FN, TN, and diagnostic performance of PET/CT.
| Author | TP | FP | FN | TN | Sensitivity | Specificity | PPV | NPV | Accuracy |
|---|---|---|---|---|---|---|---|---|---|
| Li et al. [ | 8 | 2 | 1 | 6 | 88.9 | 75 | 80.0 | 85.7 | 82 |
| Kim et al. [ | 76 | 5 | 6 | 36 | 92.7 | 87.8 | 93.8 | 85.7 | 91.1 |
| Gu et al. [ | 16 | 5 | 3 | 8 | 84.2 | 61.5 | 76.2 | 72.7 | 75 |
Figure 8Deeks' funnel plots for assessing the publication bias risk of CT.
Figure 9Deeks' funnel plots for assessing the publication bias risk of MRI.
Figure 10Deeks' funnel plots for assessing the publication bias risk of PET/CT.
Figure 2Sensitivity and specificity of CT.
Figure 3DOR and SROC curve of CT.
Figure 4Sensitivity and specificity of MRI.
Figure 5DOR and SROC curve of MRI.
Figure 6Sensitivity and specificity of PET/CT.
Figure 7DOR and SROC curve of PET/CT.
Summary estimates of sensitivity, specificity, DOR, and AUC for CT, MRI, and PET/CT.
| Modality | Pooled | Pooled | DOR |
| AUC |
|---|---|---|---|---|---|
| CT | 95% (91–97%) | 69% (63–75%) | 38.66 | 0.8615 | 0.9269 |
| MRI | 94% (90–97%) | 71% (60–81%) | 33.50 | 0.8527 | 0.9194 |
| PET/CT | 91% (84–96%) | 81% (69–90%) | 35.01 | 0.8554 | 0.9218 |