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The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study Cover

The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study

Open Access
|Sep 2024

Abstract

Background

Extrathyroidal extension was related with worse survival for patients with papillary thyroid carcinoma. For its preoperative evaluation, we measured and compared the predicting value of sonographic method and ultrasonic radiomics method in nodules of papillary thyroid carcinoma.

Patients and methods

Data from 337 nodules were included and divided into training group and validation group. For ultrasonic radiomics method, a best model was constructed based on clinical characteristics and ultrasonic radiomic features. The predicting value was calculated then. For sonographic method, the results were calculated using all samples.

Results

For ultrasonic radiomics method, we constructed 9 models and selected the extreme gradient boosting model for its highest accuracy (0.77) and area under curve (0.813) in validation group. The accuracy and area under curve of sonographic method was 0.70 and 0.569. Meanwhile. We found that the top-6 important features of xgboost model included no clinical characteristics, all of whom were high-dimensional radiomic features.

Conclusions

The study showed the superior value of ultrasonic radiomics method to sonographic method for preoperative detection of extrathyroidal extension in papillary thyroid carcinoma. Furthermore, high-dimensional radiomic features were more important than clinical characteristics.

DOI: https://doi.org/10.2478/raon-2024-0040 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
Language: English
Page range: 386 - 396
Submitted on: Apr 4, 2024
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Accepted on: Jul 1, 2024
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Published on: Sep 15, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Hui Zhu, Hongxia Luo, Yanyan Li, Yuhua Zhang, Zhijing Wu, Yan Yang, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution 4.0 License.