DOAJ Open Access 2023

Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence

Yu Jiang Master of Engineering Kang Wang Master of Medicine Yu-Ran Wang Master of Engineering Yan-Jun Xiang Master of Medicine Zong-Han Liu Doctor of Medicine +2 lainnya

Abstrak

Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.

Penulis (7)

Y

Yu Jiang Master of Engineering

K

Kang Wang Master of Medicine

Y

Yu-Ran Wang Master of Engineering

Yan-Jun Xiang Master of Medicine

Zong-Han Liu Doctor of Medicine

J

Jin-Kai Feng Doctor of Medicine

S

Shu-Qun Cheng Doctor of Medicine

Format Sitasi

Engineering, Y.J.M.o., Medicine, K.W.M.o., Engineering, Y.W.M.o., Medicine, Y.X.M.o., Medicine, Z.L.D.o., Medicine, J.F.D.o. et al. (2023). Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence. https://doi.org/10.1177/15330338231212726

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Informasi Jurnal
Tahun Terbit
2023
Sumber Database
DOAJ
DOI
10.1177/15330338231212726
Akses
Open Access ✓