A Comparison of Different Radiomics Methods Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.
Authors
Affiliations (2)
Affiliations (2)
- Department of Ultrasound in Medicine, Sir Run Run Shaw Hospital, Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China.
- Department of Ultrasound in Medicine, Shangyu People's Hospital of Shaoxing, Shaoxing, Zhejiang, China.
Abstract
The preoperative identification of cervical lymph node metastasis in papillary thyroid carcinoma is essential in tailoring surgical treatment. We aim to develop an ultrasound-based handcrafted radiomics model, a deep learning radiomics model, and a combined model for better predicting cervical lymph node metastasis in papillary thyroid carcinoma patients. A retrospective cohort of 441 patients was included (308 in the training set, 133 in the testing set). Handcrafted radiomics features, manually selected by physicians, were extracted using Pyradiomics software, whereas deep learning radiomics features were extracted from a pretrained DenseNet121 network, a fully automatic process that eliminates the need for manual selection. A combined model integrating radiomics signatures from the above models was developed. ROC analysis was used to evaluate the performance of three models. DeLong's tests were conducted to compare the AUC values of the different models in the training and testing sets. In the training set, the AUC value of the combined model (0.790) was significantly higher than that of the handcrafted radiomics (0.743, p = 0.021) and deep learning radiomics (0.730, p = 0.003) models. In the testing set, although the AUC value of the combined model (0.761) was higher than that of the handcrafted radiomics model (0.734, p = 0.368) and deep learning radiomics model (0.719, p = 0.228), statistical significance was not reached. The handcrafted radiomics model exhibited high accuracy in both the training and testing sets (0.714 and 0.707), while the deep learning radiomics model showed accuracy below 0.7 in both the training and testing sets (0.698 and 0.662). The combined model based on conventional ultrasound images enhances the predictive performance compared to different radiomics models alone.