| Literature DB >> 31907050 |
Zhao Liu1,2, Jin-Ming Fan1,2, Chen He1,2, Zhi-Fan Li1,2, Yong-Sheng Xu2, Zhao Li1,2, Hai-Feng Liu3, Jun-Qiang Lei2.
Abstract
BACKGROUND: Accurate and early diagnosis of residual tumors or intrahepatic recurrences after TACE is critically needed for determining the success of treatments and for guiding subsequent therapeutic planning. This meta-analysis was performed to assess the efficacy of diffusion weighted imaging (DWI) with the quantitative apparent diffusion coefficient (ADC) value in diagnosing residual or recurrent hepatocellular carcinoma after transarterial chemoembolization (TACE).Entities:
Keywords: Apparent diffusion coefficient (ADC); Diffusion weighted imaging (DWI); Hepatocellular carcinoma (HCC); Meta-analysis; Transarterial chemoembolization (TACE)
Mesh:
Year: 2020 PMID: 31907050 PMCID: PMC6945501 DOI: 10.1186/s40644-019-0282-9
Source DB: PubMed Journal: Cancer Imaging ISSN: 1470-7330 Impact factor: 3.909
Fig. 1The flowchart of the study selection process
Baseine characteristics of included studes
| Author | Year | Country | Design | No. Patient | Gender | Age, Year | time interval | No. lesion | Field Strength | b value | Blind | Reference Standard | ADC cut off value | Threshold |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afifi [ | 2016 | Egypt | Pro | 20 | 18/2 | 56(42–69) | 3w | 20 | 1.5 T | 0, 300,600 | NR | follow up | NR | Visual |
| Du [ | 2014 | China | Re | 89 | 57/32 | 56.0(26–78) | 1mo | 113 | 1.5 T | 0,800 | NR | follow up | 1.54 | ADC |
| Ebraheem [ | 2017 | Egypt | Pro | 50 | 42/8 | 60(40–79) | NR | 50 | 1.5 T | 0,50,1000 | NR | follow up | NR | Visual |
| Goshima [ | 2008 | Japan | Re | 25 | 18/7 | 60(48–79) | 2-6mo | 39 | 1.5 T | 0,500 | Y | follow up | NR | Visual |
| Hassan [ | 2019 | USA | Re | 60 | 45/15 | 60(40–73) | NR | 63 | 1.5 T | 0,400,800 | NR | follow up | N | Visual |
| Kokabi [ | 2015 | Georgia | Pro | 57 | 39/18 | 61.3(25–82) | 3mo | 57 | 1.5 T | 50,400,800 | NR | follow up | 0.83 | ADC |
| Li [ | 2016 | China | Pro | 117 | 86/31 | 51.7(31–74) | 1mo | 117 | NR | NR | NR | follow up | 1.24 | ADC |
| Mannelli [ | 2009 | USA | Re | 21 | 19/2 | 57.7(30–70) | 10d-3mo | 28 | 1.5 T | 0,50,500 | Y | Patholoy | 2.16 | ADC |
| Wu [ | 2017 | China | Pro | 84 | 57/27 | 54.5(25–76) | 1mo-2mo | 84 | 1.5 T | 300,600,800 | NR | follow up | 1.20 | ADC |
| Xiao [ | 2008 | China | Re | 15 | 13/2 | 45.7(17–63) | 10d-2mo | 30 | 1.5 T | 0,500 | Y | patholoy | NR | Visual |
| Yousef [ | 2017 | Egypt | Pro | 45 | 38/7 | 59(38–75) | 3w-5 m | 59 | 1.5 T | 0,400,800 | NR | follow up | NR | Visual |
| Yuan [ | 2014 | China | Pro | 41 | 34/7 | 56.2(23–78) | 6w-8w | 52 | 1.5 T | 0,500 | Y | follow up | 1.84 | Visual |
No Number, Pro Prospective, Re Retrospective, NR Not report, ADC Apparent diffusion coefficient
The distribution of included quality according to QUADAS-2 tool
| Afifi 2016 [ | Du 2014 [ | Ebraheem 2017 [ | Goshima 2008 [ | Hassan 2009 [ | Kokabi 2015 [ | Li 2016 [ | Mannelli 2009 [ | Wu 2017 [ | Xiao 2008 [ | Yousef 2017 [ | Yuan 2014 [ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ris of bias | U | U | U | L | U | U | U | L | U | L | U | L |
| Patient selection | L | L | L | L | L | L | L | L | L | L | L | L |
| Index Test | U | U | U | L | U | U | U | L | U | L | U | L |
| Reference standard | L | L | L | L | L | L | L | L | L | L | L | L |
| Flow and timing | L | L | L | L | L | L | L | L | L | L | L | L |
| Applicability Concerns | L | L | L | L | L | L | L | L | L | L | L | L |
| Patient selection | L | L | L | L | L | L | L | L | L | L | L | L |
| Index Test | U | U | U | L | U | U | U | L | U | L | U | L |
| Reference standard | L | L | L | L | L | L | L | L | L | L | L | L |
L Low, U Unclear
Fig. 2Forest plots of sensitivity for DWI in diagnosing residual or recurrent HCCs after TACE
Fig. 3Forest plots of specificity for DWI in the diagnosis of residual or recurrent HCCs after TACE
Fig. 4Forest plots of ROC for DWI in the detection of residual or recurrent HCCs after TACE
Fig. 5Forest plots of ADC value between residual or recurrent HCCs and necrotic tumors
Subgroup analysis for the diagnostic performance of DWI on overall level
| Characteristic | No.of Studies | Sensitivity | Specificity | AUC (95% CI) | ||
|---|---|---|---|---|---|---|
| All studies | 12 | 0.85(0.74–0.92) | 0.83(0.75–0.88) | 85.0/65.3 | 0.90(0.87–0.92) | |
| Study design | ||||||
| Retrospective | 5 | 0.80(0.73–0.86) | 0.81(0.71–0.88) | 0.00/69.6 | 0.86(0.83–0.89) | 0.68 |
| Prospective | 7 | 0.91(0.69–0.98) | 0.82(0.74–0.88) | 92.1/67.5 | 0.88(0.85–0.90) | |
| b value | ||||||
| < 800 | 5 | 0.88(0.69–0.96) | 0.92(0.83–0.96) | 67.5/0.00 | 0.93(0.91–0.95) | 0.40 |
| ≥ 800 | 6 | 0.85(0.75–0.92) | 0.76(0.66–0.83) | 58.8/49.2 | 0.87(0.83–0.89) | |
| Threshold method | ||||||
| visual | 6 | 0.82(0.71–0.89) | 0.82(0.67–0.91) | 38.663.1 | 0.88(0.85–0.90) | 0.47 |
| ADC | 6 | 0.87(0.65–0.96) | 0.85(0.76–0.91) | 92.9/72.7 | 0.90(0.87–0.92) | |
P value: Comparasion of AUC value between different subgroup analyses
Fig. 6Deeks’ funnel plot for publication bias assessment of DWI for diagnosis of residual or recurrent HCCs after TACE