Abstract
Background: Hashimoto's thyroiditis (HT) is a common benign thyroid disease that often coexists with papillary thyroid carcinoma (PTC). Owing to the diffuse changes in the thyroid caused by HT, PTCs can be challenging to detect using conventional imaging modalities such as ultrasound and CT. The aim of this study is to develop a radiomics model that uses nonenhancedCT (NECT) to predict the presence of PTC in the patients with HT, thereby improving early diagnostic accuracy.
Materials and Methods: This retrospective study included pathologically confirmed HT patients with or without PTC who underwent NECT scans within 30 days before surgery from January 2017 to April 2023 at the Second Affiliated Hospital of Nanchang University (Hospital I) or Ganzhou People's Hospital (Hospital II). Radiomic features were extracted using PyRadiomics. Interclass correlation coefficient, Pearson correlation and LASSO analyses were conducted to reduce the dimensionality of the radiomicfeatures. Five machine learning algorithms, including logistic regression, naive Bayes, support vector machine, k-nearest neighbor, and multilayer perceptron (MLP) classifiers, were employed to develop and validate the prediction models based on the remaining features.
Results: A total of 130 patients, 89 from Hospital I and 41 from Hospital II, were included. Six features with nonzero coefficients were retained by the LASSO algorithm for inclusion in the machine learning models. The MLP model performed the best in the external validation cohort, with an area under the curve of 0.783, a sensitivity of 64.29%, and a specificity of 92.31%.
Conclusion: A radiomics model based on NECT can identify PTCs in patients with HT and has the potential to enhance early diagnosis and intervention for these patients.