Deep Integrative Analysis for Survival Prediction

Abstract

Survival prediction is very important in medical treatment. However, recent leading research is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample sizes are relatively small. To solve the above challenges, we developed a deep survival learning model to predict patients' survival outcomes by integrating multi-view data. The proposed network contains two sub-networks, one view-specific and one common sub-network. We designated one CNN-based and one FCN-based sub-network to efficiently handle pathological images and molecular profiles, respectively. Our model first explicitly maximizes the correlation among the views and then transfers feature hierarchies from view commonality and specifically fine-tunes on the survival prediction task. We evaluate our method on real lung and brain tumor data sets to demonstrate the effectiveness of the proposed model using data with multiple modalities across different tumor types.

Prediction of conversion to Alzheimer’s disease using deep survival analysis of MRI images

“…The deep survival analysis was first introduced by Faraggi and Simon (1995) , who developed an expanded Cox proportional hazards model with a neural network structure. Thereafter, other groups developed different approaches to the deep survival analysis ( Ranganath et al , 2016 ; Luck and Lodi, 2017 ; Chaudhary et al , 2018 ; Huang et al , 2018 ; Katzman et al , 2018 ). In this study, we compared our model with DeepHit, a deep learning model that lacks any strong assumptions and can directly estimate the probability in each year.…”

“…A survival analysis is an analysis of time-to-event data, which describe the interval between a time origin to an endpoint of interest ( Kartsonaki, 2016 ). A recently developed approach that combines survival analysis and deep learning enables the estimation of the survival durations of individual patients ( Liao and Hyung-il, 2016 ; Ranganath et al , 2016 ; Luck and Lodi, 2017 ; Chaudhary et al , 2018 ; Huang et al , 2018 ; Katzman et al , 2018 ). For instance, Ranganath et al (2016) introduced the deep survival analysis, a hierarchical generative approach to a survival analysis in the context of electronic health records.…”

A Deep Active Survival Analysis approach for precision treatment recommendations: Application of prostate cancer

“…for sentiment analysis. They used restricted Boltzmann machines (RBM) for feature learning based on labeled reviews and large amount of unlabeled reviews, then applied gradient-descent Zhou et al (2013) proposed semi-supervised sentiment classification algorithm Vinzamuri et al (2014) developed survival regression for censored data for electronic health records Ranganath et al (2016) introduced a deep hierarchical generative approach for survival analysis in heart disease Nie et al (2016) proposed a survival analysis model applied on high-dimensional multi-modal brain images Liao & Ahn (2016) proposed a survival analysis framework using a LSTM model Huang et al (2017) developed a survival model using CNN-based and one FCNbased sub-network and applied on pathological images and molecular profiles Chaudhary et al (2017) introduced a DL based, survival model on hepatocellular carcinoma patients using genomic data Liu et al (2017) proposed an active learning approach using DBN for classification of hyperspectral images Luck et al (2017) developed a patient-specific kidney graft survival model using principle of multi-task learning Sener & Savarese (2017) Table 1 which indicates no research have been developed to address a survival approach using deep learning and active learning.…”

Multimodal fusion of imaging and genomics for lung cancer recurrence prediction

“…With the increase in the availability of multimodal public datasets which provide patient information for more than one modality, such as TCGA-TCIA [6], there is potential to greatly improve recurrence prediction by fusing information from imaging and genomics since they originate from different physical scales. This has been demonstrated recently for survival prediction with histology and genomics [7,8].…”