Background and Aims Patients with papillary thyroid carcinoma (PTC) often present with central lymph node metastasis, and unilateral/bilateral thyroid lobectomy combined with central lymph node dissection (CLND) is the primary treatment approach. However, due to anatomical variations, there is still controversy regarding whether dissection of the lymph nodes posterior to right recurrent laryngeal nerve (LN-prRLN) should be performed in PTC patients with clinically negative neck lymph nodes (cN0). Currently, there is limited research on the factors influencing LN-prRLN metastasis in cN0-stage PTC patients, and there is a lack of personalized quantitative prediction tools for assessing the risk of LN-prRLN metastasis. Therefore, this study was conducted to explore the factors for LN-prRLN metastasis in cN0-stage PTC patients and develop an individualized prediction model to provide decision-making guidance for LN-prRLN dissection.Methods The clinicopathologic data of 410 patients with papillary thyroid cancer who underwent thyroid surgery at Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University from June 2019 to December 2022 were retrospectively analyzed. The patients were randomly divided into a training group and a validation group in a 7∶3 ratio. Based on the postoperative pathological results of LN-prRLN metastasis, the patients were categorized into LN-prRLN positive and LN-prRLN negative groups. Data including patient age, sex, BMI, thyroid ultrasound results, thyroid function, postoperative pathology, and lymph node metastasis were collected. Univariate analysis and multivariate Logistic regression analysis were performed to determine independent risk factors for LN-prRLN metastasis in cN0-stage PTC. A visualized prediction nomogram model was constructed based on the selected independent risk factors. The model's performance was validated by plotting the ROC curve to calculate the area under the curve (AUC), calibration curves and decision curve analysis.Results In comparison between the LN-prRLN positive group and the LN-prRLN negative group, univariate analysis revealed statistically significant differences in tumor size (P<0.001), tumor multifocality (P=0.021), capsular/extrathyroidal invasion (P=0.011), and positive lymph nodes in the right neck level VIA (P<0.001). The results of multivariate Logistic regression analysis showed that larger tumor size (P=0.037), tumor multifocality (P=0.031), capsular/extrathyroidal invasion (P=0.033), and positive lymph nodes of level VIA on the right side (P<0.001) were independent risk factors for LN-prRLN metastasis in cN0-stage PTC patients. A prediction model based on these factors was established and presented a visual nomogram. After validation, the AUC of this model in the training group and validation group were 0.870 (95% CI=0.807-0.933) and 0.857 (95% CI=0.750-0.964), respectively. The calibration curves for both the training and validation groups closely approximated the ideal curve, indicating that the predicted probabilities from the model were consistent with the actual probabilities. Decision curve analysis also demonstrated that applying this model in clinical practice resulted in clinical gains.Conclusion The visualized predictive nomogram model established based on independent risk factors for LN-prRLN metastasis in cN0-stage PTC, as determined by this study, helps to objectively and individually assess cervical lymph nodes, particularly the metastasis of LN-prRLN. It balances the surgical anatomical benefits and the risk of surgical complications, and provides evidence for whether LN-prRLN dissection should be performed, optimizing diagnosis and treatment.