To develop a machine learning model that predicts metastasis to lymph nodes around the entrance point of the recurrent laryngeal nerve in patients with clinically lymph node-negative papillary thyroid carcinoma.
Key Findings:
Metastasis to LN-epRLN occurred in 29 out of 149 patients (19%).
Larger tumors and younger age (≤45 years) were associated with LNM-epRLN.
The Random Forest model achieved an area under the curve of 0.92 in predicting LNM-epRLN.
Interpretation:
The Random Forest model can assist surgeons in predicting lymph node metastasis, aiding in intraoperative decision-making while considering oncologic outcomes and nerve preservation.
Limitations:
Conducted at a single center, limiting generalizability.
Retrospective data may introduce biases.
Small number of patients with LNM-epRLN (n = 29).
Model sensitivity in the test set was 0.50.
Conclusion:
The interpretable Random Forest-based ML model may enhance surgical decision-making regarding LN-epRLN dissection.
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