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

Ye Jiao Mao, Li Wen Zha, Andy Yiu Chau Tam, Hyo Jung Lim, Alyssa Ka Yan Cheung, Ying Qi Zhang, Ming Ni, James Chung Wai Cheung, Duo Wai Chi Wong

Research output: Journal article publicationReview articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number837
JournalCancers
Volume15
Issue number3
DOIs
Publication statusPublished - 29 Jan 2023

Keywords

  • artificial intelligence
  • cancer
  • computer-aided diagnosis
  • deep learning
  • neoplasia
  • neoplasm
  • neuroendocrine tumor
  • sonoelastography

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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