ArticleBMC cancer2025
Nonenhanced CT-Based radiomics model enhances PTC detection in Hashimoto's thyroiditis.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHashimoto'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 was to develop a radiomics model based on nonenhanced CT (NECT) to predict the presence of PTC in the patients with HT, thereby improving early diagnostic accuracy. MATERIALS AND
methodsThis 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 Hospital I and Hospital II. The patients from hospital I were divided randomly at a ratio of 8:2 into a training cohort and an internal validation cohort. The patients from hospital II were assigned to the external validation cohort. Radiomic features were extracted using PyRadiomics. Intraclass correlation coefficient, Pearson correlation and LASSO analyses were conducted to reduce the dimensionality of the radiomic features. Four machine learning algorithms, including logistic regression (LR), naive bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP) classifiers, were employed to develop and validate the prediction models based on the remaining features.
resultsA total of 130 patients, 89 from Hospital I [71 in the training cohort and 18 internal validation cohort] and 41 from Hospital II [external validation cohort], were included. Six features with nonzero coefficients were retained by the LASSO algorithm for inclusion in the machine learning models. In the external validation cohort, the LR, NB, SVM, and MLP models obtained AUCs of 0.736, 0.690, 0.751 and 0.783, respectively. The MLP model performed the best in the external validation cohort, with an area under the curve of 0.783, a sensitivity of 0.643, and a specificity of 0.923.
conclusionA radiomics model based on NECT could identify PTCs in patients with HT and had the potential to enhance early diagnosis and intervention for these patients.
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