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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
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Tumor size interpretation for predicting cervical lymph node metastasis using a differentiated thyroid cancer risk
Rong-Liang Shi1, Ning Qu2, Shu-Wen Yang2
1Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University; Department of General Surgery, Minhang Hospital, Fudan University, Shanghai, People's Republic of China.
Oncotargets and Therapy
|August 31, 2016
Summary
Primary tumor size is a key predictor of lymph node metastasis (LNM) in differentiated thyroid cancer (DTC). Larger tumors correlate with increased LNM risk, aiding surgical management decisions for DTC patients.
Area of Science:
- Oncology
- Surgical Pathology
- Epidemiology
Background:
- Lymph node metastasis (LNM) is frequent in differentiated thyroid cancer (DTC).
- Management of clinically node-negative DTC remains controversial.
- Predictive factors for LNM require further elucidation.
Purpose of the Study:
- To evaluate primary tumor size as a predictor of LNM in DTC.
- To develop a predictive model for LNM risk in DTC patients.
Main Methods:
- Analysis of 80,565 DTC patients from the SEER database (2002-2012).
- Multivariate logistic regression to assess tumor size association with LNM.
- Development of a predictive model incorporating tumor size and clinicopathological factors.
Main Results:
- Tumor size was a significant independent prognostic factor for LNM.
- Increasing tumor size correlated with higher odds of lateral lymph node metastasis.
- Larger tumors were associated with increased prevalence of male sex, white race, follicular histology, extrathyroidal extension, and distant metastasis.
Conclusions:
- Primary tumor size is a crucial factor in predicting LNM in DTC.
- Larger tumors often exhibit aggressive features and higher metastatic potential.
- Stratifying DTC patients by tumor size can refine LNM risk assessment pre-operatively.

