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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Prognostic nomogram models for elderly patients with differentiated thyroid carcinoma: A population-based study.

Dasong Wang¹, Yan Yang, Hongwei Yang

¹Department of Breast and Thyroid Surgery, Suining Central Hospital, Suining, Sichuan Province, China.

|November 4, 2024

Summary

A new prognostic model accurately predicts survival for elderly patients with differentiated thyroid carcinoma (DTC). Key factors like age and tumor stage help estimate overall and cancer-specific survival outcomes.

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Area of Science:

Oncology
Geriatric Medicine
Biostatistics

Background:

Differentiated thyroid carcinoma (DTC) affects elderly populations, necessitating accurate prognostic tools.
Predicting outcomes in elderly DTC patients is challenging due to age-related comorbidities and diverse clinical presentations.

Purpose of the Study:

To develop and validate a robust prognostic model for elderly DTC patients.
To identify key demographic and clinical factors influencing overall survival (OS) and cancer-specific survival (CSS).

Main Methods:

Utilized the Surveillance, Epidemiology, and End Results (SEER) database for patient data (2010-2019).
Employed Cox proportional hazards regression for factor identification and nomogram construction.
Validated the model using internal (7:3 split) and external datasets, assessing discrimination (concordance index) and calibration.

Main Results:

Identified significant prognostic factors for OS: age, marital status, sex, multifocality, T, N, and M stages.
Identified key determinants for CSS: age, tumor size, multifocality, T, N, and M stages.
Developed accurate nomograms with high predictive accuracy and clinical utility, confirmed by decision curve analysis.

Conclusions:

The developed nomograms provide reliable prediction of OS and CSS for elderly DTC patients.
These models offer significant clinical net benefit, aiding in treatment planning and patient management.
The study highlights the importance of integrating demographic and clinical factors for personalized prognostication in elderly DTC.