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TC check: a web app for thyroid cancer recurrence prediction using explainable machine learning.

Huashu Wen¹, Xiaohua Li¹, Xia Zhao²

¹Information and Data Center, General Hospital of Southern Theater Command of PLA, Guangzhou, 510010, Guangdong, China.

Journal of Cancer Research and Clinical Oncology

|December 17, 2025

Summary

This study introduces a new machine learning model to predict thyroid cancer recurrence, achieving high accuracy. The developed tool, TCCheck, offers personalized clinical decision support for patients.

Keywords:

Explainable machine learning Multiple algorithms Recurrence Stacking learning Thyroid cancer Web app

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Endocrinology
Oncology
Machine Learning

Background:

Thyroid cancer (TC) is a common endocrine malignancy.
TC recurrence poses a significant clinical challenge, impacting patient prognosis.
Traditional statistical models struggle to accurately predict TC recurrence.

Purpose of the Study:

To develop a novel stacking ensemble learning framework for predicting TC recurrence.
To improve the accuracy and interpretability of TC recurrence prediction.
To create a user-friendly tool for clinical decision support.

Main Methods:

A dataset of 383 patients (108 recurrence, 275 non-recurrence) was used.
A stacking ensemble framework integrated SGD, ET, and DT with XGBoost as meta-learner.
SHAP method was employed for model interpretability and factor identification.

Main Results:

The stacking model achieved 96.52% accuracy, 93.55% F1-score, and 0.9921 AUC.
Top predictors identified: treatment response, age, N-stage, risk stratification, and adenopathy.
An interactive web tool, TCCheck, was developed for online prediction.

Conclusions:

The developed framework is effective and interpretable for TC recurrence prediction.
The TCCheck tool provides explainable, individualized clinical decision support.
The framework serves as a reference for recurrence prediction in other cancers.