Development and validation of a risk score model for patient stratification and personalized management of papillary thyroid cancer.

[BACKGROUND] The status of central lymph node (CLN) is a crucial determinant for the initial treatment of papillary thyroid cancer (PTC), but preoperative ultrasound (US) has limited ability to accurately assess their condition. This study aimed to develop a risk score model for risk stratification of CLN metastasis in unifocal PTC patients to guide the initial treatment.

[METHODS] A total of 5,374 patients diagnosed with unifocal PTC at Union Hospital between November 2009 and August 2022 were finally enrolled in the analysis, including 3,542 patients in derivation cohort and 1,832 patients in validation cohort. Stepwise multivariable logistic regression was used to build the risk score of CLN metastasis. Risk score weights were assigned by dividing the coefficients of the predictors with the lowest coefficient value in the final model and rounding to the nearest integer. Points were calculated for each patient by adding these weights.

[RESULTS] Ten multivariable predictors constructed the final model, including age, gender, body mass index, Hashimoto's disease, tumor location, calcification, capsule abnormalities, CLN and lateral lymph node (LN) abnormalities and tumor size. Based on the scores derived from these variables, patients were classified into four risk categories: low [0-9], low to intermediate [10-13], intermediate to high [14-17] and high [≥18], corresponding to 20.34%, 37.42%, 59.65%, and 83.82% of the observed incidence of CLN metastasis in the derivation cohort, respectively. In derivation and validation cohorts, the area under the curve of the final model was 0.764 and 0.72, respectively.

[CONCLUSIONS] Compared to relying solely on tumor size and LNs US findings, our risk score, incorporating demographic characteristics and routine pre-operative examinations, served as a more practical and effective tool for risk stratification of CLN metastasis in unifocal PTC patients, facilitating in clinical decision-making.