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Risk stratification for reoperation outcomes in recurrent or persistent papillary thyroid carcinoma: development and
Zimei Tang1, Anwen Ren1, Gang Tian1
1Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Road, Wuhan, 430022, China.
BMC Medical Informatics and Decision Making
|June 19, 2026
Summary
Machine learning accurately predicts outcomes for papillary thyroid carcinoma (PTC) reoperation. This tool aids clinicians in identifying high-risk patients for personalized treatment strategies.
Area of Science:
- Oncology
- Medical Informatics
- Machine Learning
Background:
- Reoperation for recurrent or persistent papillary thyroid carcinoma (PTC) has heterogeneous outcomes.
- Early risk stratification is crucial for guiding clinical decisions and improving patient prognosis.
- Interpretable machine learning (ML) models can enhance prediction of reoperation response in PTC.
Purpose of the Study:
- To develop and validate interpretable ML models for early prediction of reoperation response in patients with recurrent or persistent PTC.
- To identify key predictors of reoperation outcomes in PTC patients.
Main Methods:
- Retrospective study of 670 PTC patients undergoing reoperation (January 2012 - January 2024).
- Development and comparison of multiple ML algorithms, including random forest (RF).
- Model performance evaluated using AUC; feature importance assessed with SHapley Additive exPlanations.
Main Results:
- The RF model achieved the highest predictive accuracy with an AUC of 0.923 (training) and 0.865 (validation).
- Thirteen key variables were identified, including clinical, pathological, and treatment-related factors.
- Significant predictors included age, BMI, Hashimoto's thyroiditis, tumor characteristics, lymph node status, and immune-inflammation index.
Conclusions:
- An interpretable ML model was successfully developed and validated for predicting PTC reoperation outcomes.
- The model can assist clinicians in identifying high-risk patients.
- Personalized treatment strategies can be tailored based on these predictions.
