Abstract
In this paper, we mainly adopted 337 patients who had undergone the surgery on lymph node metastasis of papillary thyroid carcinoma (PTC) as the sample population. In order to provide clinical reference for the intelligent decision-making in treatment plan and improvement of prognosis, we utilized ultrasound features and imaging features to construct five early diagnosis models for patients based on the ultrasound features, imaging features, and combined features. The model integrated with broad learning system (BLS) showed the best performance, with the area under the curve (AUC) of 0.857 (95% confidence interval (CI): 0.811–0.902)) and the accuracy of 0.805 (95% CI: 0.759–0.850). For demographic and clinical features, the prediction effect was also good, with the AUC more than 0.700.
Highlights
Papillary thyroid carcinoma (PTC) is one of the most common pathologic types of thyroid cancer [1]. e current clinical problem is to find regions where lymph node metastasis is prone to occur [2]. is problem is usually solved by utilizing the ultrasound technology, which is the first choice for thyroid cancer examination
Ultrasound technology can determine whether the patient has cervical lymph node metastasis before surgery, which is of great significance for the selection of surgical methods, radiotherapy and chemotherapy, and the judgment of prognosis [3]. e major advantage of machine learning is that the learning model can improve treatment decisions for cancers and provide clinical references to improve the prognosis [4]
From the map of feature importance (Figure 4), it can be found that the most important variable was the maximum diameter of nodules, followed by GLSZM zone entropy in imaging features, and the third was carcinoembryonic antigen
Summary
Papillary thyroid carcinoma (PTC) is one of the most common pathologic types of thyroid cancer [1]. e current clinical problem is to find regions where lymph node metastasis is prone to occur [2]. is problem is usually solved by utilizing the ultrasound technology, which is the first choice for thyroid cancer examination. E major advantage of machine learning is that the learning model can improve treatment decisions for cancers and provide clinical references to improve the prognosis [4]. If combined with imaging omics, broad learning features can be utilized in establishing the lymph node metastasis model [6,7,8]. Imaging omics is mainly based on the extraction and analysis of images features from CT, MRI, PET, and other medical images to quantitatively evaluate diseases such as thyroid papillary carcinoma and lymph nodes [9]. Imaging omics was proved to be objective in image extraction of lymph node features in PTC and had important implications for prediction of clinical outcome [11,12,13,14,15]. Since imaging omics has been successfully applied to the diagnosis of thyroid cancer, lung cancer, liver cancer, breast cancer, and other diseases [16,17,18,19,20,21,22], it will be employed in the present study
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