Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
Highlights
Endocrine neoplasms are derived from specialized hormone-secreting cells
Based on MRI, Zhang et al conducted a study aiming to differentiate pituitary adenoma from the Rathke cleft cyst, and the results showed that two radiomic features had promising and practical values in distinguishing those two tumors, with an area under the ROC curve (AUC) of more than 0.75 [20]. e subtype of Pituitary adenomas (PAs) plays a major role in determining subsequent treatment
Wang et al showed that the accuracy of the US-based radiomic method was much higher than that of the US examination in the prediction of metastasis of Papillary thyroid carcinoma (PTC) [35]
Summary
Endocrine neoplasms are derived from specialized hormone-secreting cells. Most of these tumors specialize in synthesizing and secreting hormones with a small portion lacking hormone-secreting ability [1]. Erefore, it is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Imaging is widely accepted as an important and useful tool in oncologic research because of its noninvasiveness, convenience, and repeatability, which is used for the diagnosis and staging of tumors and for tumor anatomical characteristics assessment and cancer. With subsequent data analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually, which may supplement traditional imaging analysis and assist in providing precision medicine for patients. We would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms. e limitations of current radiomic research and future development directions would be discussed
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