Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

Abstract

Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.

The functional roles of the circRNA/Wnt axis in cancer

“…The incidence rate of thyroid cancer has been increasing throughout the world [296][297][298][299][300]. CircRNA_102171 and circRNA_NEK6 are relatively upregulated [226,227], while circ-ITCH is downregulated in thyroid cancer tissues and cell lines [188].…”

Application of radiomics in adrenal incidentaloma: a literature review

“…Predicting the risk of distant metastasis and recurrence, genotyping of AI tumors such as pheochromocytoma and monitoring treatment response are important aspects for personalized patient care. Fortunately, most of the radiomic approaches have been studied extensively in various cancers [ 12 , 14 ], which should be adoptable for AI tumors. For example, Huynh et al analyzed CT images of 113 patients with stage I–II non-small cell lung cancer treated with stereotactic body radiation therapy.…”

“…It transforms the medical digital images into deep-level data for quantitative analysis [ 9 – 11 ]. Radiomics has been used for tumor classification, assessment of prognosis and recurrence, and evaluation of treatment effect in various cancers including cancers in lung, breast, thyroid and adrenal gland [ 12 – 14 ]. However, there is relative lack of literature on radiomics in AI.…”

Artificial Intelligence in Thyroid Field—A Comprehensive Review

“…Ultrasound imaging is the recommended method for early detection and diagnosis of thyroid lesions due to its economy, effectivity and absence of radiation [46][47][48][49]. It is widely accepted as the first imaging modality for thyroid disease, for instance by American and European associations of endocrinology [50].…”