ArticleBMC cancer2022
A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT07391514 (Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma), which is not on this map. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma: A Prospective Cohort Study
Who cites it
13 citing papers in PubMed, 15 citations in OpenAlex.
- Dual-Tracer Imaging and Deep Learning for Real-Time Prediction of Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.Cancers · 2026Article
- Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a study of ultrasound and clinical features.Frontiers in endocrinology · 2026Article
- Early prediction of thyroid capsule invasion in papillary microcarcinoma using ultrasound-based deep learning models: a retrospective multicenter study.Insights into imaging · 2025Article
- Exploration of immunocytochemical biomarkers related to central lymph node metastasis in papillary thyroid microcarcinoma.CytoJournal · 2025Article
- Predicting lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis using regression and network analysis.Scientific reports · 2024Article
- The role of intraoperative central lymph node biopsy in the treatment of clinically low-risk PTMC.Endocrine · 2024Article
- Predicting central lymph node metastasis in papillary thyroid cancer: A nomogram based on clinical, ultrasound and contrast‑enhanced computed tomography characteristics.Oncology letters · 2024Article
- A multivariable model of ultrasound and biochemical parameters for predicting high-volume lymph node metastases of papillary thyroid carcinoma with Hashimoto's thyroiditis.Frontiers in endocrinology · 2024Article
- Article
- Risk factors associated with high-risk nodal disease in patients considered for active surveillance of papillary thyroid microcarcinoma without extrathyroidal extension.Gland surgery · 2023Article
- A clinical and molecular pathology prediction model for central lymph node metastasis in cN0 papillary thyroid microcarcinoma.Frontiers in endocrinology · 2023Article
- Prediction of papillary thyroid metastases to the central compartment: proposal of a model taking into consideration other thyroid conditions.Frontiers in endocrinology · 2023Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCentral lymph node status in papillary thyroid microcarcinoma (PTMC) plays an important role in treatment decision-making clinically, however, it is not easy to predict central lymph node metastasis (CLNM). The present work focused on finding the more rational alternative for evaluating central lymph node status while identifying influencing factors to construct a model to predict CLNM incidence.
methodsIn this study, we retrospectively analyzed the typical sonographic and clinicopathologic features of 546 PTMC patients who underwent surgery, among which, the data of 382 patients were recruited in the training cohort and that of 164 patients in the validation cohort. Based on the outcome of the training cohort, significant influencing factors were further identified through univariate analysis and were considered as independent variables in multivariable logistic regression analysis and incorporated in and presented with a nomogram.
resultsIn total, six independent predictors, including the age, sex, tumor size, multifocality, capsular invasion, Hashimotos thyroiditis were entered into the nomogram. Both internal validation and external validation revealed the favorable discrimination of our as-constructed nomogram. Calibration curves exhibited high consistency. As suggested by decision-curve analyses, the as-constructed nomogram might be applied in clinic. Besides, the model also distinguished patients according to risk stratification.
conclusionsThe novel nomogram containing remarkable influencing factors for CLNM cases was established in the present work. The nomogram can assist clinicians in clinical decision-making.
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