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Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules
Joint Authors
Alis, Deniz
Colakoglu, Bulent
Yergin, Mert
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-31
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Aim.
The aim of this study is to evaluate the diagnostic value of machine learning- (ML-) based quantitative texture analysis in the differentiation of benign and malignant thyroid nodules.
Materials and methods.
A sum of 306 quantitative textural features of 235 thyroid nodules (102 malignant, 43.4%; 133 benign, 56.4%) of a total of 198 patients were investigated using the random forest ML classifier.
Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method.
The diagnostic accuracy, sensitivity, specificity, and area under curve (AUC) of the proposed method were compared with the histopathological or cytopathological findings as reference methods.
Results.
Of the 306 initial texture features, 284 (92.2%) showed good reproducibility (intraclass correlation ≥0.80).
The random forest classifier accurately identified 87 out of 102 malignant thyroid nodules and 117 out of 133 benign thyroid nodules, which is a diagnostic sensitivity of 85.2%, specificity of 87.9%, and accuracy of 86.8%.
The AUC of the model was 0.92.
Conclusions.
Quantitative textural analysis of thyroid nodules using ML classification can accurately discriminate benign and malignant thyroid nodules.
Our findings should be validated by multicenter prospective studies using completely independent external data.
American Psychological Association (APA)
Colakoglu, Bulent& Alis, Deniz& Yergin, Mert. 2019. Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules. Journal of Oncology،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1184428
Modern Language Association (MLA)
Colakoglu, Bulent…[et al.]. Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules. Journal of Oncology No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1184428
American Medical Association (AMA)
Colakoglu, Bulent& Alis, Deniz& Yergin, Mert. Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules. Journal of Oncology. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1184428
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1184428