Determination of lymph node metastasis using quantitative ultrasound elastography of papillary thyroid carcinoma nodule: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background and purpose papillary thyroid carcinoma (PTC) as the most common thyroid tumor, tends to invade adjacent organs, especially lymphatic system. This study aimed to evaluate the discrimination performance of ultrasound elastography (USE) in assessing PTC nodule for determination of cervical lymph node metastasis (CLNM). Methods The protocol was pre-registered at ( https://osf.io/r5tc8 ). Using PubMed, Web of Science, Embase, and Cochrane Library, studies published up to March 10, 2025, were identified. Data extraction was conducted independently, and a random-effects bivariate model was applied to estimate pooled differentiation accuracy estimates. Results Twenty-one studies were included involving 7559 patients; 2790 (36%) were positive for CLNM, while 4769 (63%) were negative. The pooled Emean values for positive and negative CLNM were 51.2k Pa (95% CI: 42.6 to 59.7) and 44.8 kPa (95% CI: 35.2 to 54.4), respectively. It represents an absolute increase of 6.14 kPa (95% CI: 2.70 to 9.59) in the metastatic group compared to the benign group. Additionally, the pooled Emax value for positive and negative CLNM were 87.9 kPa (95% CI: 49.5 to 126.4) and 68.7 kPa (95% CI: 44.2 to 93.1), respectively. This corresponds to an absolute increase of + 19.57 kPa (95% CI: 2.96 to 36.18) in the metastatic group, representing a more dramatic elevation compared to Emean values. The thyroid nodule Emax and Emean were significantly higher for positive CLNM of + 27.5% (95% CI: 10.5–44.5%) and + 12.9% (95% CI: 5.1–20.7%) respectively. Combining USE with conventional ultrasound improved differentiation accuracy, achieving a sensitivity of 80% (95% CI: 62–90%), specificity of 79% (95% CI: 70–85%), and an AUC of 0.85 (95% CI: 0.81 to 0.88). Conclusion USE parameters demonstrated potential as a discrimination tool for the preoperative assessment of CLNM, particularly when combined with conventional ultrasound, which enhances its performance. Clinical trial number N/A.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".