Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives

Abstract

Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

“…In this case the accuracy is the only performance parameter to be analyzed and ANN, SVM and LDA with k-NN (Linear discriminant analysis with neural networks) have 100% accuracy. [23] Olatunji et al obtain models in their study using ANN, SVM, RF (Random Forrest), NB (Naïve Byes). In the training part they consider a number of 15 types of inputs most of which are biological parameters like Hematocrit level, hemoglobin level.…”

“…According to the statistics of Rebecca Siegel et al, CRC is one of the deadliest cancer types in the United States. [23] In the charts it is on the second place. Their statistics show that in the year of 2020 approximately 150000 individuals are diagnosed with the disease and one third of the patients die from it.…”

Artificial intelligence and thyroid disease management

“…The COVID-19 pandemic also triggers the development of CDSS using imaging techniques and biomarkers for mortality prediction ( 29 ). Same strategy could be applied to thyroid cancer using the progresses of AI to separate and break down morphological, textural, and molecular features ( 30 ). Clinical decision support systems might help physicians to accelerate the decision-making process in thyroid diseases.…”

Editorial: Differentiated Thyroid Cancer - Risk Adapted Therapy, Genetic Profiling and Clinical Staging

“…According to a recent review, artificial intelligence and machine learning are very promising methods to improve the diagnostic sensitivity and specificity of ultrasound thyroid imaging (11). Up to now, the focus has been mainly on specification of malignancy in thyroid nodules.…”