Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning

PURPOSE: Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively. METHODS: In total, 405 patients w...

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Detalles Bibliográficos
Autores principales: Jiang, Yi-Quan, Cao, Su-E, Cao, Shilei, Chen, Jian-Ning, Wang, Guo-Ying, Shi, Wen-Qi, Deng, Yi-Nan, Cheng, Na, Ma, Kai, Zeng, Kai-Ning, Yan, Xi-Jing, Yang, Hao-Zhen, Huan, Wen-Jing, Tang, Wei-Min, Zheng, Yefeng, Shao, Chun-Kui, Wang, Jin, Yang, Yang, Chen, Gui-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Original Article – Clinical Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873117/
https://www.ncbi.nlm.nih.gov/pubmed/32852634
http://dx.doi.org/10.1007/s00432-020-03366-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873117/
https://www.ncbi.nlm.nih.gov/pubmed/32852634
http://dx.doi.org/10.1007/s00432-020-03366-9

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