Mining Whole-liver Information with Deep Learning for Preoperatively Predicting HCC Recurrence-free Survival

Hepatocellular carcinoma ( HCC) is globally a leading cause of cancer death. Non-invasive pre-operative prediction of HCC recurrence-free survival (RFS) after resection is essential but remains challenging. Previous models based on medical imaging focus only on tumor area while neglecting the whole liver condition. In fact, HCC patients usually suffer from chronic liver diseases which also hamper the patient survival. This work aims to develop a novel convolutional neural network (CNN) to mine whole-liver information from contrast-enhanced computed tomography (CECT) to predict RFS after hepatic resection in HCC. Our proposed RFSNet takes liver regions from CECT as input, and outputs a risk score for each patient. Cox proportional-hazards loss was applied for model training. A total of 215 patients with primary HCC and treated with hepatic resection were included for analysis. Patients were randomly split into developing subcohort and testing subcohort by 4:1. The developing subcohort was further split into the training subcohort and validation subcohort for model training. Baseline models were built with tumor region, radiomics features and/or clinical features the same as previous tumor-based approaches. Results showed that RFSNet achieved the best performance with concordance-indinces (CIs) of 0.88 and 0.65 for the developing and testing subcohorts, respectively. Adding clinical features did not improve RFSNet. Our findings suggest that the proposed RFSNet based on whole liver is able to extract more valuable information concerning RFS prognosis compared to features from only tumor and the clinical indicators.