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Related Experiment Video

Updated: Mar 14, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

CT Radiomics-Based Machine Learning Model for Predicting Capsular and Neural Invasion in Thyroid Carcinoma:

Fang-Fang Cong¹, Ke Tian², Qian Gao²

¹Department of MRI, Zhoukou Medical Science Research Center, Zhoukou Central Hospital, Zhoukou, China.

JMIR Medical Informatics

|March 12, 2026

Summary

This summary is machine-generated.

Radiomic analysis of CT scans can help predict capsular invasion in thyroid carcinoma, a key factor for neural invasion (NI) risk. Machine learning models integrating imaging and clinical data show promise for improved risk stratification.

Keywords:

CT artificial intelligence capsular invasion computed tomography machine learning neural invasion tadiomics thyroid carcinoma

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Area of Science:

Radiology
Oncology
Artificial Intelligence

Background:

Thyroid carcinoma incidence is rising globally, making prognostic factors crucial.
Capsular invasion and neural invasion (NI) significantly impact recurrence and survival.
Preoperative noninvasive assessment of these factors remains a clinical challenge.

Purpose of the Study:

Identify CT radiomic biomarkers for capsular invasion in thyroid carcinoma.
Develop machine learning models integrating radiomic and clinical data for NI risk stratification.
Evaluate the utility of these models in predicting NI risk.

Main Methods:

Retrospective analysis of 111 thyroid carcinoma patients.
Extraction of radiomic features from CT images (arterial and venous phases).
Construction of nomogram, random forest (RF), and neural network (NN) models using clinical and radiomic data.
Validation using 5-fold cross-validation and bootstrap resampling.

Main Results:

Clinical indicator-based nomogram achieved an AUC of 0.9418 for capsular invasion prediction.
Radiomic-based nomogram showed an AUC of 0.9334 for capsular invasion and 0.8001 for cross-label analysis.
The integrated NN model achieved an AUC of 0.775 for cross-label analysis, with good calibration.

Conclusions:

Capsular invasion is a strong predictor of NI risk in thyroid carcinoma.
Preoperative CT radiomic models show potential for NI risk stratification.
Multimodal models integrating imaging and clinical data enhance postoperative risk assessment.