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Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is one of the leading causes of death. The prediction of the ER of HCC before treatment contributes to guiding treatment and follow-up protocols. In recent years, CT radiomics signatures have been proven effective in several studies in predicting early recurrence of HCC, there are still two major challenges. First, the radiomics features extracted were low or mid-level features, which may not fully characterize HCC heterogeneity. Second, the fusion approach of clinical textual data and image information is in little consensus. In this paper, we proposed a deep-learning based prediction model to extract high-level features from the triple-phase CT images and compare its performance with traditional radiomics model and clinical model. The accuracy and area under the curve (AUC) of receiver operating characteristics of three models was 69.52%/0.723, 67.04%/0.64, 76.03%/0.75, respectively. In addition, we proposed four fusion models to combine clinical data and highlevel features. Among them, Fusion model D performed best, achieving a higher prediction accuracy of 78.66% and AUC of 0.8248. Moreover, fusion models with a joint loss function can further improve the prediction performance to 80.49% and 0.8331.

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Non-small cell lung cancer (NSCLC) is a serious disease and has a high recurrence rate after surgery. Recently, many machine learning methods have been proposed for recurrence prediction. The methods using gene expression data achieve high accuracy rates but expensive. While, the radiomics features using computer tomography (CT) image is a cost-effective method, but their accuracy is not competitive. In this paper, we propose a genotype-guided radiomics method (GGR) for obtaining high prediction accuracy at a low cost. We used a public radiogenomics dataset of NSCLC, which includes CT images and gene expression data. Our proposed method is two steps method that uses two models. The first model is a gene estimation model, which is used to estimate the gene expression from radiomics features and deep features extracted from CT images. The second model is used to predict the recurrence using the estimated gene. The proposed GGR method is designed based on hybrid features which is the fusion of handcrafted-and deep learning-based features. The experiments demonstrated that the prediction accuracy can be improved significantly from 78.61% (existing radiomics method) and 79.09% (ResNet50) to 83.28% by the proposed GGR.

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