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

• Published in: Cancers Vol. 15; no. 3; p. 837
• Main Authors: Mao, Ye-Jiao, Zha, Li-Wen, Tam, Andy Yiu-Chau, Lim, Hyo-Jung, Cheung, Alyssa Ka-Yan, Zhang, Ying-Qi, Ni, Ming, Cheung, James Chung-Wai, Wong, Duo Wai-Chi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.01.2023; MDPI
• Subjects: Biopsy; Cancer screening; Cancer therapies; Classification; Data processing; Deep learning; Diagnosis; Elasticity; Endocrine gland tumors; Endocrine system; Endoscopy; Health aspects; Hormones; Human subjects; Image processing; Keywords; Learning algorithms; Long short-term memory; Machine learning; Magnetic resonance imaging; Medical imaging; Medical prognosis; Metastasis; Mortality; Neural networks; Pancreas; Pancreatic cancer; Patients; Physiological aspects; Radiation; Review; Reviews; Segmentation; Systematic review; Thyroid; Thyroid cancer; Tomography; Tumors; Ultrasonic imaging; Ultrasound; China; deep learning; sonoelastography; neuroendocrine tumor; cancer; neoplasm; computer-aided diagnosis; artificial intelligence; neoplasia
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15030837

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.