Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review.

Simple Summary: The incidence of endocrine cancers (e.g., thyroid, pancreas, and adrenal) has been increasing; these cancers have a high premature mortality rate. Traditional medical imaging methods (e.g., MRI and CT) might not be sufficient for accurate cancer screening. Elastography complements conventional medical imaging modalities by identifying abnormal tissue stiffness of the tumor, in which machine learning techniques can further improve accuracy and reliability. This review focuses on the applications and performance of machine-learning-based elastography in classifying endocrine tumors. Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN–long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images. [ABSTRACT FROM AUTHOR]