Diagnostic value of multiple diagnostic methods for lymph node metastases of papillary thyroid carcinoma: A systematic review and meta-analysis

Wang, Sirui; Li, Qiaoli; Tian, Feng; Li, Jun; Li, Wen-Xiao; Chen, Ming; Sang, Tian; Cao, Chun-Li; Shi, Li-Nan

doi:10.3389/fonc.2022.990603

Cited by 5 publications

(5 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The total score of this patient is 353 and the risk rate of HVLNM was 91.6%. meta-analysis reported that US + Computed Tomography FNA cytology + FNA-thyroglobulin (FNA-Tg) showed good diagnostic performance (23). The limitations are that these methods are radiative or invasive.…”

Section: Discussionmentioning

confidence: 99%

“…It was reported that the diagnostic efficacy was unsatisfactory, with a pooled sensitivity between 0.31 and 0.35 for detecting involved lymph nodes in the central neck ( 22 ). Another meta-analysis reported that US + Computed Tomography and FNA cytology + FNA-thyroglobulin (FNA-Tg) showed good diagnostic performance ( 23 ). The limitations are that these methods are radiative or invasive.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Nomogram for preoperative prediction of high-volume lymph node metastasis in the classical variant of papillary thyroid carcinoma

Feng

Chen²,

An³

et al. 2023

Front. Surg.

View full text Add to dashboard Cite

IntroductionThe objective of our study was to construct a preoperative prediction nomogram for the classical variant of papillary thyroid carcinoma (CVPTC) patients with a solitary lesion based on demographic and ultrasonographic parameters that can quantify the individual probability of high-volume (>5) lymph node metastasis (HVLNM).Materials and methodsIn this study, a total of 626 patients with CVPTC from December 2017 to November 2022 were reviewed. Their demographic and ultrasonographic features at baseline were collected and analyzed using univariate and multivariate analyses. Significant factors after the multivariate analysis were incorporated into a nomogram for predicting HVLNM. A validation set from the last 6 months of the study period was conducted to evaluate the model performance.ResultsMale sex, tumor size >10 mm, extrathyroidal extension (ETE), and capsular contact >50% were independent risk factors for HVLNM, whereas middle and old age were significant protective factors. The area under the curve (AUC) was 0.842 in the training and 0.875 in the validation set.ConclusionsThe preoperative nomogram can help tailor the management strategy to the individual patient. Additionally, more vigilant and aggressive measures may benefit patients at risk of HVLNM.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Nomogram for preoperative prediction of high-volume lymph node metastasis in the classical variant of papillary thyroid carcinoma

Feng

Chen²,

An³

et al. 2023

Front. Surg.

View full text Add to dashboard Cite

show abstract

“…Additionally, the methodological quality of studies was appraised using the Cochrane Handbook's risk-of-bias assessment tool (RevMan v.5.3.5, The Cochrane Collaboration) ( 20 ), covering six aspects (selection, performance bias, detection, attrition, reporting bias and other bias), which were categorized as ‘yes’, ‘no’ or ‘unclear’ to indicate the level of bias.…”

Section: Methodsmentioning

confidence: 99%

Predictive value of multiple imaging predictive models for spread through air spaces of lung adenocarcinoma: A systematic review and network meta‑analysis

Liu,

Wang,

Wang

et al. 2024

Oncol Lett

View full text Add to dashboard Cite

Spread Through Air Spaces (STAS) is involved in lung adenocarcinoma (LUAD) recurrence, where cancer cells spread into adjacent lung tissue, impacting surgical planning and prognosis assessment. Radiomics-based models show promise in predicting STAS preoperatively, enhancing surgical precision and prognostic evaluations. The present study performed network meta-analysis to assess the predictive efficacy of imaging models for STAS in LUAD. Data were systematically sourced from PubMed, Embase, Scopus, Wiley and Web of Science, according to the Cochrane Handbook for Systematic Reviews of Interventions) and A Measurement Tool to Assess systematic Reviews 2. Using Stata software v17.0 for meta-analysis, surface under the cumulative ranking area (SUCRA) was applied to identify the most effective diagnostic method. Quality assessments were performed using Cochrane Collaboration's risk-of-bias tool and publication bias was assessed using Deeks' funnel plot. The analysis encompassed 14 articles, involving 3,734 patients, and assessed 17 predictive models for STAS in LUAD. According to comprehensive analysis of SUCRA, the machine learning (ML)_Peri_tumour model had the highest accuracy (56.5), the Features_computed tomography (CT) model had the highest sensitivity (51.9) and the positron emission tomography (pet)_CT model had the highest specificity (53.9). ML_Peri_tumour model had the highest predictive performance. The accuracy was as follows: ML_Peri_tumour vs. Features_CT [relative risk (RR)=1.14; 95% confidence interval (CI), 0.99–1.32]; ML_Peri_tumour vs. ML_Tumour (RR=1.04; 95% CI, 0.83–1.30) and ML_Peri_tumour vs. pet_CT (RR=1.04; 95% CI, 0.84–1.29). Comparative analyses revealed heightened predictive accuracy of the ML_Peri_tumour compared with other models. Nonetheless, the field of radiological feature analysis for STAS prediction remains nascent, necessitating improvements in technical reproducibility and comprehensive model evaluation.

show abstract

“…A recently published meta-analysis of 38 articles with 6,285 patients compared 12 modalities, namely, US, computed tomography (CT), US + CT, contrast-enhanced US (CEUS), magnetic resonance diffusion tensor imaging (MRI), F-18 fluorodeoxyglucose positron emission tomography/CT (PET/CT), 131I whole-body (IWBC), US + CEU, strain elastography (SE-US), fine-needle aspiration thyroglobulin (FNA-Tg), US-guided fine-needle aspiration cytology (FNAC), and FNAC-FNA-Tg. The results showed that the sensitivity was highest for US + CT, and the specificity was highest for FNAC (25). Research has been directed to evaluate the role of contrastenhanced CT (CECT) in assessing neck nodes (26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Role of computed tomography in the evaluation of regional metastasis in well-differentiated thyroid cancer

Vaish,

Mahajan,

Sable

et al. 2023

Front. Radiol.

View full text Add to dashboard Cite

BackgroundAccurate neck staging is essential for performing appropriate surgery and avoiding undue morbidity in thyroid cancer. The modality of choice for evaluation is ultrasonography (US), which has limitations, particularly in the central compartment, that can be overcome by adding a computed tomography (CT).MethodsA total of 314 nodal levels were analyzed in 43 patients with CT, and US; evaluations were done between January 2013 and November 2015. The images were reviewed by two radiologists independently who were blinded to histopathological outcomes. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy of US, CT, and US + CT were calculated using histology as the gold standard.ResultsThe overall sensitivity, specificity, PPV, and NPV for US, CT, and US + CT were 53.9%, 88.8%, 74.1%, and 76.4%; 81.2%, 68.0%, 60.1%, and 85.9%; and 84.6%, 66.0%, 59.6%, and 87.8%, respectively. The overall accuracy of the US was 75.80%, the CT scan was 72.93%, and the US + CT scan was 72.93%. For the lateral compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 56.6%, 91.4%, 77.1%, and 80.5%; 80.7%, 70.6%, 58.3%, and 87.8%; and 84.3%, 68.7%, 57.9%, and 89.6%, respectively. The accuracy of the US was 79.67%, the CT scan was 73.98%, and the US + CT scan was 73.98% for the lateral compartment. For the central compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 47.1%, 76.5%, 66.7%, and 59.1%; 82.4%, 55.9%, 65.1%, and 76.0%; and 85.3%, 52.9%, 64.4%, and 78.3%, respectively. The accuracy of the US was 61.76%, the CT scan was 69.12%, and the US + CT scan was 69.12% for the central compartment.ConclusionsThis study demonstrated that CT has higher sensitivity in detecting nodal metastasis; however, its role is complementary to US due to low specificity.

show abstract

Diagnostic value of multiple diagnostic methods for lymph node metastases of papillary thyroid carcinoma: A systematic review and meta-analysis

Cited by 5 publications

References 53 publications

Nomogram for preoperative prediction of high-volume lymph node metastasis in the classical variant of papillary thyroid carcinoma

Nomogram for preoperative prediction of high-volume lymph node metastasis in the classical variant of papillary thyroid carcinoma

Predictive value of multiple imaging predictive models for spread through air spaces of lung adenocarcinoma: A systematic review and network meta‑analysis

Role of computed tomography in the evaluation of regional metastasis in well-differentiated thyroid cancer

Contact Info

Product

Resources

About