Predictors of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma

A retrospective study of 710 patients (971 lesions) with clinically node-negative T1–T2 papillary thyroid carcinoma. The dataset integrates pathological, ultrasound features, thyroid function, and systemic inflammatory indicators to predict central lymph node metastasis. The model was developed by Yalin Zhu and last updated in April 2026.

Use Cases

• Predicting central lymph node metastasis based on features like bilateral laterality and tumor size.
• Validating the role of inflammatory biomarkers like the systemic immune-inflammation index (SII) in cancer prognosis.
• Training interpretable machine learning models using a combination of clinical and laboratory variables.
• Assessing clinical utility of a prediction model across different decision thresholds.

Strengths

• Model achieved an AUC of 0.830 in the test set, indicating strong predictive performance.
• Study integrates multimodal predictors from 710 patients, including pathological, ultrasound, and inflammatory data.
• Model was externally validated on an independent temporal cohort of 50 patients (AUC 0.800).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is contained in a single 11.3 KB DOCX file, suggesting limited raw data scope.

Provenance

Source

figshare

Collection Method

Retrospective analysis of patient records.

Freshness

Last updated 2026-04-27 04:12:03; freshness should be verified.