# Nomogram Model for Prognosis of Distant Metastatic DTC Based on Inflammatory and Clinicopathological Factors

## TL;DR

This study developed a nomogram model to predict disease progression in distant metastatic differentiated thyroid cancer using inflammatory and clinical factors.

## Contribution

A novel nomogram model combining inflammatory markers and clinicopathological features for predicting progression in DM-DTC patients.

## Key findings

- Age, histological type, metastatic site, T stage, and LMR were identified as independent risk factors for disease progression.
- The nomogram model showed good predictive accuracy with a C-index of 0.775 in the training set and 0.731 in the validation set.
- The model provided clinical net benefit when the risk threshold was greater than 0.2.

## Abstract

Inflammatory markers may serve as potential biomarkers in predicting prognosis in patients with distant metastasis differentiated thyroid cancer (DM-DTC).

This study aimed to evaluate the predictive ability of inflammatory markers and clinicopathological features for disease progression (PD) in patients with DM-DTC.

A retrospective analysis was conducted on 230 DM-DTC patients from May 2016 to January 2022. Patients were divided into a training set and a validation set at a 7:3 ratio. Inflammatory markers were obtained within 1 week before the last 131I treatment. The primary outcome was progression-free survival (PFS). Univariable and multivariable Cox proportional hazards models identified significant prognostic factors, and a nomogram based on inflammatory markers and clinicopathological features was constructed and validated using R software.

Multivariable Cox regression analysis showed that age (hazard ratio [HR] = 2.191; 95% CI, 1.387-3.462), histological type (HR = 2.030; 95% CI, 1.216-3.389), distant metastatic site (HR = 3.379; 95% CI, 1.832-6.233), T stage (HR = 6.061; 95% CI, 2.469-14.925), and LMR (HR = 2.050; 95% CI, 1.194-3.519) were identified as independent risk factors for the progression of DM-DTC. A predictive nomogram was constructed to estimate the probability of DM-DTC progression. The C-index of the PFS model was calculated to be 0.775 (0.749-0.802) for the training set and 0.731 (95% CI, 0.686-0.775) for the validation set. The calibration curve of the validation set closely approached the reference line. The decision curve analysis indicated that when the risk threshold was greater than 0.2, this nomogram model provided clinical net benefit.

The study identified significant inflammatory markers and clinical factors for predicting PD in DM-DTC patients, providing a robust prognostic model with potential clinical application.