Related Experiment Video
Updated: Aug 27, 2025

04:01
Transoral Robotic Total Thyroidectomy and Bilateral Central Regional Lymph Node Dissection for Papillary Thyroid Carcinoma
Published on: September 15, 2023
875
[Risk factors for recurrence and survival analysis in locally advanced T4a papillary thyroid carcinoma after R0
Summary
Papillary thyroid carcinoma (PTC) stage T4a patients with complete tumor removal (R0 resection) show good outcomes. Key recurrence risks include preoperative recurrent laryngeal nerve palsy and lateral cervical lymph node metastasis.
Area of Science:
- Oncology
- Head and Neck Surgery
- Thyroid Cancer Research
Background:
- Locally advanced T4a papillary thyroid carcinoma (PTC) presents unique treatment challenges.
- Understanding recurrence risk factors is crucial for improving patient outcomes in T4a PTC.
Purpose of the Study:
- To evaluate treatment outcomes in T4a PTC patients.
- To identify risk factors associated with postoperative recurrence in T4a PTC.
Main Methods:
- Retrospective analysis of 185 T4a PTC patients (2006-2019).
- Kaplan-Meier survival analysis and logistic regression for recurrence risk factors.
- Assessment of invasion into adjacent structures (recurrent laryngeal nerve, trachea, esophagus).
Main Results:
- The 5-year and 10-year overall survival rates were 95.21% and 93.10%, respectively.
- Recurrence or metastasis occurred in 9.73% of patients, with a 4.86% mortality rate.
- Independent risk factors for recurrence included preoperative recurrent laryngeal nerve palsy (OR=3.27) and lateral cervical lymph node metastasis (OR=4.71).
Conclusions:
- T4a PTC patients achieving R0 resection demonstrate favorable treatment efficacy.
- Preoperative recurrent laryngeal nerve palsy and lateral cervical lymph node metastasis are significant independent risk factors for recurrence in T4a PTC.
More Related Videos
Related Concept Videos
Cancer Survival Analysis
428
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
428
Introduction To Survival Analysis
350
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
350
Comparing the Survival Analysis of Two or More Groups
256
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
256
Assumptions of Survival Analysis
175
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
175

