| Literature DB >> 33330093 |
Ruoning Yang1,2, Xiuhe Zou3, Hao Zeng1, Yunuo Zhao1, Xuelei Ma1.
Abstract
OBJECTIVES: We aimed to evaluate and compare the diagnostic performance of five ultrasound thyroid imaging reporting and data system (TI-RADS) classification guidelines for thyroid nodules through a review and meta-analysis.Entities:
Keywords: TI-RADS; diagnostic performance; malignancy; meta-analysis; thyroid nodule; ultrasound
Year: 2020 PMID: 33330093 PMCID: PMC7717965 DOI: 10.3389/fonc.2020.598225
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Figure 1Flowchart of the literature search and selection schema.
Baseline characteristics of included studies.
| Author | Year | Country | Patients, n | Nodules, n | MeanAge | Guidelines | Standard | Malignant lesions | Benign lesions |
|---|---|---|---|---|---|---|---|---|---|
| Zhang ( | 2020 | China | 1,271 | 1,271 | 48 | ACR/ATA/Kwak/KTA | Needle biopsy, surgical resection | 736 | 535 |
| Gao ( | 2019 | China | 1,764 | 2,544 | — | ACR/ATA/Kwak | Surgical resection | 1,681 | 863 |
| Barbosa ( | 2019 | Brazil | 139 | 140 | 49 | ACR/ATA | Needle biopsy, surgical resection | 66 | 74 |
| Jabar ( | 2019 | India | 127 | 127 | — | ACR/Kwak | Needle biopsy, surgical resection | 23 | 104 |
| Xv ( | 2019 | China | 370 | 432 | 43 | ACR | Needle biopsy, surgical resection | 258 | 174 |
| Yoon ( | 2019 | Korea | 1,836 | 2,274 | 55 | ACR/KTA | Needle biopsy, surgical resection | 300 | 1,974 |
| Huang ( | 2019 | USA | 137 | 250 | 58 | ACR/ATA | Surgical resection | 65 | 185 |
| Ruan ( | 2019 | China | 918 | 1,001 | 46 | ATA | Needle biopsy, surgical resection | 392 | 609 |
| Wang ( | 2017 | China | 1,011 | 1,011 | 51 | Kwak | Surgical resection | 464 | 547 |
| Liu ( | 2015 | China | 2,921 | 3,980 | 52 | Kwak | Needle biopsy | 228 | 3,752 |
| Ha ( | 2018 | Korea | 1,802 | 2,000 | 51 | KTA, ATA, ACR | Needle biopsy, surgical resection | 1,546 | 454 |
| Mohammadi ( | 2019 | Canada | — | 425 | — | ATA | Needle biopsy | 31 | 394 |
| Wu ( | 2019 | China | 894 | 1,000 | — | ATA, ACR | Needle biopsy, surgical resection | 530 | 470 |
| Xu ( | 2019 | China | 2,031 | 2,465 | 48 | KTA, ACR, ETA | Surgical resection | 885 | 1,146 |
| Yoon.J ( | 2017 | Korea | 4,585 | 4,696 | 51 | ATA, Kwak | Needle biopsy, surgical resection | 1,044 | 3,652 |
| Hoang ( | 2018 | USA | 92 | 100 | 52 | ACR | Needle biopsy, surgical resection | 15 | 85 |
| Li ( | 2019 | China | 128 | 130 | 48 | ACR | Needle biopsy, surgical resection | 73 | 57 |
| Trimboli ( | 2019 | Switzerland | 475 | 1,058 | 53 | ETA | Needle biopsy, surgical resection | — | — |
| Maino ( | 2018 | Italy | 340 | 432 | 57 | ATA. ETA | Needle biopsy | — | — |
Pooled estimates of the sensitivity, specificity, PLR, NLR, DOR, AUC, and SE (AUC).
| Reference guideline | Na | Pooled sensitivity (95% CI) | Pooled specificity (95% CI) | Pooled PLR (95% CI) | Pooled NLR (95% CI) | Pooled DOR (95% CI) | AUC | SE (AUC) |
|---|---|---|---|---|---|---|---|---|
| ACR | 13 | 0.85(0.84–0.86) | 0.68(0.6–0.69) | 2.98(2.37–3.75) | 0.22(0.16–0.29) | 15.23(9.23-25.11) | 0.8553 | 0.0311 |
| Kawk | 6 | 0.94(0.94–0.95) | 0.62(0.6–0.63) | 3.23(0.90–11.61) | 0.08(0.04–0.16) | 43.15(19.09–97.53) | 0.9101 | 0.0621 |
| ATA | 10 | 0.94(0–94-0.95) | 0.44(0.43–0.45) | 2.06(1.54–2.75) | 0.16(0.10–0.28) | 13.33(5.90-30.14) | 0.8976 | 0.0414 |
| KTA | 4 | 0.85(0.83-0.86) | 0.47(0.46-0.48) | 2.60(1.2–5.57) | 0.18(0.08-0.39) | 14.57(5.77-36.84) | 0.9022 | 0.0430 |
| ETA | 4 | 0.85(0.83-0.87) | 0.61(0.59-0.62) | 2.84(1.43-5.64) | 0.21(0.13-0.34) | 13.18(4.89-35.5) | 0.8810 | 0.0561 |
na, number of studies; PLR, positive likelihood ratios; NLR, negative likelihood ratios; DOR, diagnostic odds ratio; AUC, area under the curve.
Relative diagnostic odds ratio (RDOR) with 95% confidence limit.
| B A | ACR | ATA | ETA | Kawk | KTA |
|---|---|---|---|---|---|
| ACR | – | 0.6387(0.3678–1.1090) | 0.7308(0.3000–1.7803) | 0.5564(0.2552–1.2131) | 0.5734(0.2759–1.1919) |
| ATA | 1.5658(0.9017–2.7189) | – | 1.1443(0.4532–2.8897) | 0.8713(0.3995–1.8999) | 0.8979(0.4072–1.9802) |
| ETA | 1.3683(0.5617–3.3332) | 0.8739(0.3461–2.2067) | – | 0.7614(0.2498–2.3208) | 0.7846(0.3075–2.0020) |
| Kawk | 1.7972(0.8243–3.9183) | 1.3134(0.5264–2.5028) | 1.3138(0.4309–4.0035) | – | 1.0306(0.3927–2.7048) |
| KTA | 1.7439(0.8390–3.6247) | 1.1137(0.5050–2.4561) | 1.2745(0.4995–3.2518) | 0.9703(0.3697–2.5466) | – |
SROC, summary receiver operator characteristics.
Figure 2(A) Risk of bias and applicability concerns graph: review authors’ judgments about each domain presented as percentages across the included studies. (B) Risk of bias and applicability concerns summary: review authors’ judgments about each domain for each included study.