@inproceedings{afcdc5bea07d442288406f64e8f9e921,
title = "Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma",
abstract = "Repeat liver resection or transarterial chemoembolization (TACE) can be used for disease progression (PD) of hepatocellular carcinoma (HCC), but when patients developed extrahepatic metastasis or macrovascular invasion which was aggressive disease progression (aggressive-PD), the treatments became a challenge. Therefore, it was meaningful to predict aggressive-PD as early as possible considering the current prediction method in clinical was unreliable. In this study, a deep learning model was conducted to predict aggressive-PD. 333 patients receiving hepatectomy or TACE were enrolled from five hospitals. For each patient, deep learning score was calculated from a convolutional neural network model constructed based on resnet block. The model showed excellent performance for individualized, non-invasive prediction of the progression of Hepatocellular carcinoma (training set: ACC=75.61\%, AUC=0.81, validation set: ACC=87.36\%, AUC=0.82). Pearson correlation analysis showed albumin concentration were significantly correlated with deep learning score.",
keywords = "Deep learning, Hepatocellular carcinoma, Radiomics",
author = "Meiqing Pan and Zhenchao Tang and Sirui Fu and Wei Mu and Jie Zhang and Xiaoqun Li and Hui Zhang and Ligong Lu and Jie Tian",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Computer-Aided Diagnosis ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581057",
language = "英语",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Mazurowski, \{Maciej A.\} and Karen Drukker",
booktitle = "Medical Imaging 2021",
address = "美国",
}