The Increased Risk of Thyroid Cancer-Specific Mortality With Tumor Size in Stage IVB Patients

Abstract

Purpose: To investigate the risk-stratifying utility of tumor size and a threshold for further stratification on cancer-specific mortality of thyroid cancer (TC) patients in stage IVB. Methods: One thousand three hundred and forty-five patients (620 males and 725 females) with initial distant metastasis over 55 years between 2004 and 2016 from Surveillance, Epidemiology, and End Results databases were investigated, with a median follow-up time of 23 months [interquartile range (IQR), 5-56 months] and a median age of 70 years (IQR, 63-77 years). TC-specific mortality rates were calculated under different classifications. Cox regressions were used to calculate hazard ratios (HRs) and Kaplan-Meier Analyses were conducted to investigate TC-specific survivals. Results: In the whole cohort, patients with tumors >4 cm had the highest TC-specific mortality (67.9%, 330/486), followed by tumor size >1 cm but ≤4 cm (43.08%, 190/441), and tumor size ≤1 cm (32.69%, 34/104). Kaplan-Meier curves showed the increased tumor size was associated with a statistically significant decrease in TC-specific survival (P < 0.001). Papillary thyroid cancer (PTC) patients with tumors >4 cm had significantly higher hazard ratios (HRs) of 2.84 (1.72-4.70) and 3.11 (1.84-5.26) after adjusting age, gender, race, and radiation treatment, compared with patients with tumors ≤1 cm (P < 0.001). The TC-specific mortalities and survivals were further investigated among more detailed subgroups divided by different tumor size, and a threshold of 3 cm could be observed (P < 0.005) for risk stratification. Conclusions: Mortality risk increased with tumor size in PTC patients in stage IVB. Our findings demonstrated the possibility of further stratification in IVB stage in current TNM staging system. Patients with tumor size over 3 cm had an excessively high risk of PTC-specific mortality, which may justify the necessity of more aggressive treatment for them.

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A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images

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