Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma

Meiqing Pan; Zhenchao Tang; Sirui Fu; Wei Mu; Jie Zhang; Xiaoqun Li; Hui Zhang; Ligong Lu; Jie Tian

doi:10.1117/12.2581057

5 March 2021 Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma

Meiqing Pan, Zhenchao Tang, Sirui Fu, Wei Mu, Jie Zhang, Xiaoqun Li, Hui Zhang, Ligong Lu, Jie Tian

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115972Y (2021) https://doi.org/10.1117/12.2581057
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

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.

Conference Presentation

Citation Download Citation

Meiqing Pan, Zhenchao Tang, Sirui Fu, Wei Mu, Jie Zhang, Xiaoqun Li, Hui Zhang, Ligong Lu, and Jie Tian "Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115972Y (5 March 2021); https://doi.org/10.1117/12.2581057

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