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Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology

Authors
 Jiyoung Yoon  ;  Eunjung Lee  ;  Sang-Wook Kang  ;  Kyunghwa Han  ;  Vivian Youngjean Park  ;  Jin Young Kwak 
Citation
 EUROPEAN RADIOLOGY, Vol.31(7) : 5059-5067, 2021-07 
Journal Title
EUROPEAN RADIOLOGY
ISSN
 0938-7994 
Issue Date
2021-07
MeSH
Humans ; Logistic Models ; ROC Curve ; Retrospective Studies ; Thyroid Neoplasms* / diagnostic imaging ; Thyroid Nodule* / diagnostic imaging ; Ultrasonography
Keywords
Diagnosis, computer-assisted ; Radiographic image interpretation, computer-assisted ; Thyroid neoplasms ; Thyroid nodule ; Ultrasonography
Abstract
Objectives: The purpose of this study was to evaluate the role of the radiomics score using US images to predict malignancy in AUS/FLUS and FN/SFN nodules.

Methods: One hundred fifty-five indeterminate thyroid nodules in 154 patients who received initial US-guided FNA for diagnostic purposes were included in this retrospective study. A representative US image of each tumor was acquired, and square ROIs covering the whole nodule were drawn using the Paint program of Windows 7. Texture features were extracted by in-house texture analysis algorithms implemented in MATLAB 2019b. The LASSO logistic regression model was used to choose the most useful predictive features, and ten-fold cross-validation was performed. Two prediction models were constructed using multivariable logistic regression analysis: one based on clinical variables, and the other based on clinical variables with the radiomics score. Predictability of the two models was assessed with the AUC of the ROC curves.

Results: Clinical characteristics did not significantly differ between malignant and benign nodules, except for mean nodule size. Among 730 candidate texture features generated from a single US image, 15 features were selected. Radiomics signatures were constructed with a radiomics score, using selected features. In multivariable logistic regression analysis, higher radiomics score was associated with malignancy (OR = 10.923; p < 0.001). The AUC of the malignancy prediction model composed of clinical variables with the radiomics score was significantly higher than the model composed of clinical variables alone (0.839 vs 0.583).

Conclusions: Quantitative US radiomics features can help predict malignancy in thyroid nodules with indeterminate cytology.
Full Text
https://link.springer.com/article/10.1007%2Fs00330-020-07670-3
DOI
10.1007/s00330-020-07670-3
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
Yonsei Authors
Kang, Sang Wook(강상욱) ORCID logo https://orcid.org/0000-0001-5355-833X
Kwak, Jin Young(곽진영) ORCID logo https://orcid.org/0000-0002-6212-1495
Park, Vivian Youngjean(박영진) ORCID logo https://orcid.org/0000-0002-5135-4058
Yoon, Jiyoung(윤지영)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184225
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