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Updated: Aug 10, 2026
03:55
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
Predicting Structural Progression-Free Survival After Surgery for Medullary Thyroid Carcinoma
I Ros-Madrid1,2, B Febrero2,3,4, R P Cano-Mármol1
1Endocrinology Service, Virgen de la Arrixaca University Clinical Hospital, Murcia, Spain.
World Journal of Surgery
|August 8, 2026
Summary
Prognostic factors for structural disease-free survival (SDFS) in medullary thyroid cancer (MTC) were identified. Lymph node ratio (LNR), T category, and postoperative calcitonin (Ct) levels are key predictors influencing MTC patient outcomes.
Area of Science:
- Endocrinology
- Oncology
- Surgical Oncology
Background:
- Medullary thyroid cancer (MTC) is an aggressive malignancy where surgery is the primary curative option.
- Post-surgical recurrence or persistence necessitates understanding prognostic factors for structural disease-free survival (SDFS).
Purpose of the Study:
- To evaluate SDFS in MTC patients and identify associated prognostic factors.
- To inform follow-up strategies based on identified risk predictors.
Main Methods:
- Retrospective observational study of 140 MTC patients without distant metastases.
- Analysis included demographic, clinical, surgical, and biochemical variables (CEA, Ct).
- Statistical analysis involved univariate and multivariate Cox regression to determine predictors of SDFS.
Main Results:
- Structural disease occurred in 24% of patients.
- Univariate analysis identified older age, sporadic MTC, preoperative CEA, tumor size, lymph node involvement, higher LNR, vascular invasion, high-risk status, and elevated Ct (>150pg/mL at 6-12 months) as influencing SDFS.
- Multivariate analysis revealed age, LNR, T category, and postoperative Ct as independent predictors.
Conclusions:
- Lymph node ratio (LNR), T category, and postoperative calcitonin (Ct) levels at 6-12 months are significant independent predictors of SDFS in MTC.
- These factors are crucial for optimizing patient follow-up and management strategies.
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