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A nomogram and risk classification system for predicting cancer-specific survival in tall cell variant of papillary
1Department of Thyroid & Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background:
Tall cell variant (TCV) of papillary thyroid cancer (PTC) is the most common aggressive subtype of PTC. The factors that affect survival of patients with TCV remain unclear. We aimed to develop a model to predict the cancer-specific survival (CSS).
Methods:
A total of 1615 patients diagnosed with TCV between 2004 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomized into training and validation cohorts (7:3). A predictive nomogram for predicting CSS was constructed by Cox proportional hazards regression and validated by concordance index (C-index), calibration curve, and decision curve analyses (DCA). A risk classification system was built based on the total nomogram scores of each case.
Results:
A nomogram was constructed including five independent prognostic factors (age, tumor size, T stage, M stage, and extent of surgery) associated with CSS in TCV patients. Various validations proved that the nomogram model had good consistency and discrimination for TCV prognosis. The risk classification system could perfectly classify TCV patients into three risk groups with significantly different CSS. Compared with traditional AJCC TNM staging system, the nomogram could better predict CSS in TCV patients.
Conclusions:
A nomogram and corresponding risk classification system were developed for predicting CSS in TCV patients. The model has excellent performance and can be used to help clinicians make accurate prognostic assessment and individualized treatment.
