Quantitative imaging in medicine and surgery · 2025
Prediction of thyroid function based on CT radiomics: a two-center study.
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Yu Rui, Tan Yanhuan, Hou Jinpeng, Chen Yiting, Ni Xiaoqiong, Xu Liang, Fan Guohua, Jin Dan
Аннотация
BACKGROUND: In clinical practice, some patients with hyperthyroidism or hypothyroidism may lack typical clinical manifestations. Due to its popularity, this study sought to explore whether the computed tomography (CT) examination method could be used to conduct radiomics research to identify such patients. Specifically, this study aimed to investigate the value of radiomics based on non-contrast CT in the auxiliary diagnosis of hyperthyroidism/hypothyroidism.
METHODS: The data of 1,181 patients at The Second Affiliated Hospital of Soochow University were retrospectively collected. The patients were randomly divided into a training group (n=827) and an internal validation group (n=354) at a ratio of 7:3. Additionally, the data of 415 patients from Changshu Hospital Affiliated to Nanjing University of Chinese Medicine were collected to serve as the external validation set. Radiomics features were extracted from non-contrast CT images, and the features related to hyperthyroidism/hypothyroidism were selected by a significance analysis and Pearson correlation analysis. A clinical model was then built based on clinical indicators, and a combined model was developed by integrating radiomics-predicted risk values with clinical indicators. The model's three-class results were analyzed using a confusion matrix, and its binary classification diagnosis results were assessed using receiver operating characteristic (ROC) curves.
RESULTS: A total of 21 radiomics features and four clinical features were screened for modeling. The average F1 scores and kappa values for the three-classification diagnosis of thyroid function in the clinical, radiomics, and combined models were 0.664, 0.709, 0.739 and 0.493, 0.556, and 0.609 in the internal validation set, and 0.540, 0.660, 0.697, and 0.310, 0.483, and 0.531 in the external validation set, respectively. The ROC curve analysis showed that the combined model outperformed the clinical and radiomics model in the two-classification diagnosis in both the internal and external validation sets.
CONCLUSIONS: The predictive model based on non-contrast CT radiomics had good diagnostic efficacy for hyperthyroidism/hypothyroidism. Its accuracy was further improved by combining clinical information. This model could alert clinicians to patients lacking typical clinical manifestations.
METHODS: The data of 1,181 patients at The Second Affiliated Hospital of Soochow University were retrospectively collected. The patients were randomly divided into a training group (n=827) and an internal validation group (n=354) at a ratio of 7:3. Additionally, the data of 415 patients from Changshu Hospital Affiliated to Nanjing University of Chinese Medicine were collected to serve as the external validation set. Radiomics features were extracted from non-contrast CT images, and the features related to hyperthyroidism/hypothyroidism were selected by a significance analysis and Pearson correlation analysis. A clinical model was then built based on clinical indicators, and a combined model was developed by integrating radiomics-predicted risk values with clinical indicators. The model's three-class results were analyzed using a confusion matrix, and its binary classification diagnosis results were assessed using receiver operating characteristic (ROC) curves.
RESULTS: A total of 21 radiomics features and four clinical features were screened for modeling. The average F1 scores and kappa values for the three-classification diagnosis of thyroid function in the clinical, radiomics, and combined models were 0.664, 0.709, 0.739 and 0.493, 0.556, and 0.609 in the internal validation set, and 0.540, 0.660, 0.697, and 0.310, 0.483, and 0.531 in the external validation set, respectively. The ROC curve analysis showed that the combined model outperformed the clinical and radiomics model in the two-classification diagnosis in both the internal and external validation sets.
CONCLUSIONS: The predictive model based on non-contrast CT radiomics had good diagnostic efficacy for hyperthyroidism/hypothyroidism. Its accuracy was further improved by combining clinical information. This model could alert clinicians to patients lacking typical clinical manifestations.