The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.

Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems. We also conduct feature importance analysis to understand how lung cancer patients\' relevant factors impact their survival periods. We contribute to identifying an approach to estimate survivability that are commonly and practically appropriate for medical use.
We have compared the performance across three of the most popular deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) while comparing the performing of deep learning models against traditional machine learning models. The data was obtained from the lung cancer section of Surveillance, Epidemiology, and End Results (SEER) cancer registry.
The deep learning models outperformed traditional machine learning models across both classification and regression approaches. We obtained a best of 71.18 % accuracy for the classification approach when patients\' survival periods are segmented into classes of \'<=6 months\',\' 0.5 - 2 years\' and \'>2 years\' and Root Mean Squared Error (RMSE) of 13.5 % andR2 value of 0.5 for the regression approach for the deep learning models while the traditional machine learning models saturated at 61.12 % classification accuracy and 14.87 % RMSE in regression.
This approach can be a baseline for early prediction with predictions that can be further improved with more temporal treatment information collected from treated patients. In addition, we evaluated the feature importance to investigate the model interpretability, gaining further insight into the survival analysis models and the factors that are important in cancer survival period prediction.