Table 3_An interpretable machine learning model for predicting central lymph node metastas

Yalin Zhu's retrospective study developed an interpretable gradient boosting decision tree model to predict central lymph node metastasis in papillary thyroid carcinoma. The model was trained on data from 710 patients (971 lesions) and achieved an AUC of 0.830 in the test set. It was last updated on 2026-04-27.

Use Cases

• Predicting central lymph node metastasis based on patient age, tumor size, and laterality.
• Evaluating the role of inflammatory biomarkers like systemic immune-inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) in cancer prognosis.
• Building transparent clinical decision aids for surgical planning in thyroid oncology.
• Validating machine learning models on independent temporal cohorts for robustness.

Strengths

• Model was validated on an independent temporal cohort of 50 patients, achieving an AUC of 0.800.
• The study integrated multimodal predictors, including pathological, ultrasound, thyroid function, and systemic inflammatory indicators.
• Model performance was assessed with calibration curves, decision curve analysis, and SHAP interpretability frameworks.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying dataset is unknown, which may limit suitability assessment.
• Data reflects the specific context of a single retrospective study; generalizability may be limited.

Provenance

Source

Yalin Zhu via figshare

Collection Method

Retrospective analysis of patient records.

Freshness

Last updated 2026-04-27 04:12:01; freshness should be verified.