Establishment and validation of a nomogram model for poorly differentiated thyroid cancer based on the Surveillance, Epidemiology, and End Result database.
2/5 보강
OpenAlex 토픽 ·
Thyroid Cancer Diagnosis and Treatment
Advanced Statistical Modeling Techniques
Infrared Thermography in Medicine
[PURPOSE] To retrospectively analyze the clinicopathological characteristics and prognosis-related factors of poorly differentiated thyroid cancer (PDTC), construct a nomogram model, and predict the 3
APA
Jing Liang, Yingying Yang, et al. (2026). Establishment and validation of a nomogram model for poorly differentiated thyroid cancer based on the Surveillance, Epidemiology, and End Result database.. Nuclear medicine communications. https://doi.org/10.1097/MNM.0000000000002163
MLA
Jing Liang, et al.. "Establishment and validation of a nomogram model for poorly differentiated thyroid cancer based on the Surveillance, Epidemiology, and End Result database.." Nuclear medicine communications, 2026.
PMID
42011027 ↗
Abstract 한글 요약
[PURPOSE] To retrospectively analyze the clinicopathological characteristics and prognosis-related factors of poorly differentiated thyroid cancer (PDTC), construct a nomogram model, and predict the 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS).
[METHODS] PDTC patients between 1975 and 2020 from the Surveillance, Epidemiology, and End Result Program were divided into training (70%) and validation (30%) sets. Univariate and multivariate Cox regression analyses identified independent prognostic factors for PDTC, and a nomogram prognostic model was constructed. The predictive ability of the model was assessed using the receiver operating characteristic curve, decision curve analysis, and calibration curve.
[RESULTS] A total of 983 PDTC patients were enrolled. Univariate and multivariate Cox regression analyses showed that age, gender, malignant tumor behavior, time from diagnosis to treatment, tumor size, treatment modality, N and M stages were independent prognostic factors for OS. For CSS, age, malignant tumor behavior, time from diagnosis to treatment, treatment modality, regional nodes examined, N and M stages were identified as independent prognostic factors. The nomogram model constructed based on these factors exhibited good predictive ability, clinical applicability, and calibration accuracy.
[CONCLUSION] The nomogram model constructed based on clinicopathological characteristics such as age, gender, malignant tumor behavior, time from diagnosis to treatment, tumor size, treatment modality, N and M stages can effectively predict the OS of PDTC patients. Age, malignant tumor behavior, time from diagnosis to treatment, treatment modality, regional nodes examined, N and M stages can effectively predict the CSS.
[METHODS] PDTC patients between 1975 and 2020 from the Surveillance, Epidemiology, and End Result Program were divided into training (70%) and validation (30%) sets. Univariate and multivariate Cox regression analyses identified independent prognostic factors for PDTC, and a nomogram prognostic model was constructed. The predictive ability of the model was assessed using the receiver operating characteristic curve, decision curve analysis, and calibration curve.
[RESULTS] A total of 983 PDTC patients were enrolled. Univariate and multivariate Cox regression analyses showed that age, gender, malignant tumor behavior, time from diagnosis to treatment, tumor size, treatment modality, N and M stages were independent prognostic factors for OS. For CSS, age, malignant tumor behavior, time from diagnosis to treatment, treatment modality, regional nodes examined, N and M stages were identified as independent prognostic factors. The nomogram model constructed based on these factors exhibited good predictive ability, clinical applicability, and calibration accuracy.
[CONCLUSION] The nomogram model constructed based on clinicopathological characteristics such as age, gender, malignant tumor behavior, time from diagnosis to treatment, tumor size, treatment modality, N and M stages can effectively predict the OS of PDTC patients. Age, malignant tumor behavior, time from diagnosis to treatment, treatment modality, regional nodes examined, N and M stages can effectively predict the CSS.
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