Multimodal Imaging under Artificial Intelligence Algorithm for the Diagnosis of Liver Cancer and Its Relationship with Expressions of EZH2 and p57.

Journal: Computational intelligence and neuroscience

Published Date: Mar 14, 2022

Abstract

OBJECTIVE: It aimed to explore the diagnostic efficacy of multimodal ultrasound images based on mask region with convolutional neural network (M-RCNN) segmentation algorithm for small liver cancer and analyze the expression of zeste gene enhancer homolog 2 (EZH2) and p57 (P57 Kip2) genes in cancer cells.

Authors

Yamin Zhang

Department of Oncology, Xi'an International Medical Center Hospital, Xi'an City 710000, China.
Jie Cui

Department of Oncology, The First Affiliated Hospital of Xi'an Medical College, Xi'an City 710000, China.
Wei Wan

Department of Oncology, Xi'an International Medical Center Hospital, Xi'an City 710000, China.
Jinpeng Liu

Department of Oncology, Xi'an International Medical Center Hospital, Xi'an City 710000, China.

Keywords

Algorithms Artificial Intelligence Cyclin-Dependent Kinase Inhibitor p57 Enhancer of Zeste Homolog 2 Protein Gene Expression Humans Liver Neoplasms Multimodal Imaging

External Resources

View on PubMed Access via DOI PubMed (35321452)

Multimodal Imaging under Artificial Intelligence Algorithm for the Diagnosis of Liver Cancer and Its Relationship with Expressions of EZH2 and p57.

Abstract

Authors

Keywords

External Resources

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