Nomogram predicts risk and prognostic factors for lung metastasis of anaplastic thyroid carcinoma: a retrospective study in the Surveillance Epidemiology and End Results (SEER) database.

BACKGROUND

Lung metastasis (LM) is a frequent occurrence in patients with anaplastic thyroid cancer (ATC) and is often associated with a poor prognosis. However, there is currently a lack of specific research focusing on the diagnostic and prognostic evaluation of LM in ATC patients using nomograms. Consequently, the establishment of effective predictive models holds significant importance in providing guidance for clinical practice.

METHODS

We screened patients from Surveillance Epidemiology and End Results (SEER) database between 2000 and 2018. To identify independent risk factors for LM in patients with ATC, we conducted univariate and multivariate logistic regression analyses. We also conducted univariate and multivariate Cox proportional hazards regression analyses to identify independent prognostic factors for ATC patients with LM. Based on these analyses, we developed two novel nomograms. The performance of the nomograms was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

A cohort of 540 ATC patients was enrolled in the study, among whom 181 patients (33.5%) were identified with LM at the time of initial diagnosis. The independent risk factors for LM in patients with ATC included tumor size, extent of surgery, lateral cervical lymph node metastasis, and radiotherapy. Furthermore, tumor size, extent of surgery, radiotherapy, and chemotherapy were identified as independent factors influencing the prognosis of ATC patients with LM. The accuracy of the two nomograms in predicting the occurrence and prognosis of LM in ATC patients was confirmed through the analysis of ROC curves, calibration, DCA curves, and Kaplan-Meier (K-M) survival curves on both the training and validation sets.

CONCLUSIONS

The two nomograms are highly accurate in predicting LM in patients with ATC and in forecasting patient outcomes for patients with lung metastases. Consequently, they offer valuable support for personalized clinical decision-making in future clinical practice.