Journal
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
Volume 82, Issue -, Pages 295-305Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ejmp.2021.02.013
Keywords
Hepatocellular Carcinoma (HCC); Overall survival; Radiomics; Deep learning; Variational autoencoder (VAE); Convolutional neural network (CNN); Computed tomography (CT)
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This study successfully identified a new radiomics signature using deep learning for risk prediction of overall survival in HCC patients treated with SBRT. Liver function was found to be crucial in patients' OS, and radiomics was shown to aid in their management.
This work aims to identify a new radiomics signature using imaging phenotypes and clinical variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with stereotactic body radiation therapy (SBRT). 167 patients were retrospectively analyzed with repeated nested cross-validation to mitigate overfitting issues. 56 radiomic features were extracted from pre-treatment contrast-enhanced (CE) CT images. 37 clinical factors were obtained from patients? electronic records. Variational autoencoders (VAE) based survival models were designed for radiomics and clinical features and a convolutional neural network (CNN) survival model was used for the CECT. Finally, radiomics, clinical and raw image deep learning network (DNN) models were combined to predict the risk probability for OS. The final models yielded c-indices of 0.579 (95%CI: 0.544?0.621), 0.629 (95%CI: 0.601?0.643), 0.581 (95%CI: 0.553?0.613) and 0.650 (95%CI: 0.635?0.683) for radiomics, clinical, image input and combined models on nested cross validation scheme, respectively. Integrated gradients method was used to interpret the trained models. Our interpretability analysis of the DNN showed that the top ranked features were clinical liver function and liver exclusive of tumor radiomics features, which suggests a prominent role of side effects and toxicities in liver outside the tumor region in determining the survival rate of these patients. In summary, novel deep radiomic analysis provides improved performance for risk assessment of HCC prognosis compared with Cox survival models and may facilitate stratification of HCC patients and personalization of their treatment strategies. Liver function was found to contribute most to the OS for these HCC patients and radiomics can aid in their management.
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