Nuclear medicine communications · 2026
Establishment and validation of a nomogram model for poorly differentiated thyroid cancer based on the Surveillance, Epidemiology, and End Result database.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Liang Jing, Yang Yingying, Mi Ru, Zheng Wei, Jia Qiang, Tan Jian, Li Ning, Meng Zhaowei
Аннотация
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.