Survival Prediction Method Research Articles

Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images

Early survival prediction is vital for the clinical management of cancer patients, as tumors can be better controlled with personalized treatment planning. Traditional survival prediction methods are based on radiomics feature engineering and/or clinical indicators (e.g., cancer staging). Recently, survival prediction models with advances in deep learning techniques have achieved state-of-the-art performance in end-to-end survival prediction by exploiting deep features derived from medical images. However, existing models are heavily reliant on the prognostic information within primary tumors and cannot effectively leverage out-of-tumor prognostic information characterizing local tumor metastasis and adjacent tissue invasion. Also, existing models are sub-optimal in leveraging multi-modality medical images as they rely on empirically designed fusion strategies to integrate multi-modality information, where the fusion strategies are pre-defined based on domain-specific human prior knowledge and inherently limited in adaptability. Here, we present an Adaptive Multi-modality Segmentation-to-Survival model (AdaMSS) for survival prediction from multi-modality medical images. The AdaMSS can self-adapt its fusion strategy based on training data and also can adapt its focus regions to capture the prognostic information outside the primary tumors. Extensive experiments with two large cancer datasets (1380 patients from nine medical centers) show that our AdaMSS surmounts the state-of-the-art survival prediction performance (C-index: 0.804 and 0.757), demonstrating the potential to facilitate personalized treatment planning.

Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.

Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer. Women with cervical cancer diagnosed between 2013 and 2015 were included in our study using data from the Surveillance, Epidemiology, and End Results (SEER) database. Using this dataset, the study assesses the performance of Weibull, Cox proportional hazards models, and Random Survival Forests in terms of predictive accuracy and risk factor identification. The findings reveal that machine learning models, particularly Random Survival Forests (RSF), outperform traditional statistical methods in both predictive accuracy and the discernment of crucial prognostic factors, underscoring the advantages of machine learning in handling complex survival data. However, for a survival dataset with a small number of predictors, statistical models should be used first. The study finds that RSF models enhance survival analysis with more accurate predictions and insights into survival risk factors but highlights the need for larger datasets and further research on model interpretability and clinical applicability.

The cross-modality survival prediction method of glioblastoma based on dual-graph neural networks

Glioma, the highly lethal malignant brain tumor originating from abnormal proliferation of glial cells, exhibits a varied overall survival rate influenced by multiple factors. Accurate prediction rate of survival periods assist physicians in selecting the most suitable treatment plans to improve patient overall survival (OS) rates. The paper proposes a dual-graph neural network (GNN) with manually constructed feature relational graph for OS prediction and inference of different survival periods in glioblastoma based on cross-modality data. Specifically, five radiomic features from magnetic resonance imaging are extracted to construct two sets of feature relational graphs. The main GNN is utilized to extract comprehensive features, including age, brain MRI features, and radiomics features of gliomas. The branch GNN additionally extracts radiomics features specific to gliomas, constraining the feature weights of the main GNN through attention mechanisms. Pretraining an autoencoder to extract deep features from patient text information. The text features and image features are then reorganized based on features from different modalities through a transformer decoder. Finally, a multi-layer perceptron is utilized for regression and classification, thus enabling the classification and prediction of patient survival. The proposed method achieved an accuracy of 0.586 for classifying and predicting the survival of glioma patients in the short, medium, and long term on the BraTS20 dataset, outperforming state-of-the-art methods.

A decentralized federated learning-based cancer survival prediction method with privacy protection

BackgroundSurvival prediction is one of the crucial goals in precision medicine, as accurate survival assessment can aid physicians in selecting appropriate treatment for individual patients. To achieve this aim, extensive data must be utilized to train the prediction model and prevent overfitting. However, the collection of patient data for disease prediction is challenging due to potential variations in data sources across institutions and concerns regarding privacy and ownership issues in data sharing. To facilitate the integration of cancer data from different institutions without violating privacy laws, we developed a federated learning-based data integration framework called AdFed, which can be used to evaluate patients’ survival while considering the privacy protection problem by utilizing the decentralized federated learning technology and regularization method. ResultsAdFed was tested on different cancer datasets that contain the patients’ information from different institutions. The experimental results show that AdFed using distributed data can achieve better performance in cancer survival prediction (AUC = 0.605) than the compared federated-learning-based methods (average AUC = 0.554). Additionally, to assess the biological interpretability of our method, in the case study we list 10 identified genes related to liver cancer selected by AdFed, among which 5 genes have been proved by literature review. ConclusionsThe results indicate that AdFed outperforms better than other federated-learning-based methods, and the interpretable algorithm can select biologically significant genes and pathways while ensuring the confidentiality and integrity of data.

Agnostic-Specific Modality Learning for Cancer Survival Prediction from Multiple Data.

Cancer is a pressing public health problem and one of the main causes of mortality worldwide. The development of advanced computational methods for predicting cancer survival is pivotal in aiding clinicians to formulate effective treatment strategies and improve patient quality of life. Recent advances in survival prediction methods show that integrating diverse information from various cancer-related data, such as pathological images and genomics, is crucial for improving prediction accuracy. Despite promising results of existing approaches, there are great challenges of modality gap and semantic redundancy presented in multiple cancer data, which could hinder the comprehensive integration and pose substantial obstacles to further enhancing cancer survival prediction. In this study, we propose a novel agnostic-specific modality learning (ASML) framework for accurate cancer survival prediction. To bridge the modality gap and provide a comprehensive view of distinct data modalities, we employ an agnostic-specific learning strategy to learn the commonality across modalities and the uniqueness of each modality. Moreover, a cross-modal fusion network is exerted to integrate multimodal information by modeling modality correlations and diminish semantic redundancy in a divide-and-conquer manner. Extensive experiment results on three TCGA datasets demonstrate that ASML reaches better performance than other existing cancer survival prediction methods for multiple data.

MIF: Multi-Shot Interactive Fusion Model for Cancer Survival Prediction Using Pathological Image and Genomic Data.

Accurate cancer survival prediction is crucial for oncologists to determine therapeutic plan, which directly influences the treatment efficacy and survival outcome of patient. Recently, multimodal fusion-based prognostic methods have demonstrated effectiveness for survival prediction by fusing diverse cancer-related data from different medical modalities, e.g., pathological images and genomic data. However, these works still face significant challenges. First, most approaches attempt multimodal fusion by simple one-shot fusion strategy, which is insufficient to explore complex interactions underlying in highly disparate multimodal data. Second, current methods for investigating multimodal interactions face the capability-efficiency dilemma, which is the difficult balance between powerful modeling capability and applicable computational efficiency, thus impeding effective multimodal fusion. In this study, to encounter these challenges, we propose an innovative multi-shot interactive fusion method named MIF for precise survival prediction by utilizing pathological and genomic data. Particularly, a novel multi-shot fusion framework is introduced to promote multimodal fusion by decomposing it into successive fusing stages, thus delicately integrating modalities in a progressive way. Moreover, to address the capacity-efficiency dilemma, various affinity-based interactive modules are introduced to synergize the multi-shot framework. Specifically, by harnessing comprehensive affinity information as guidance for mining interactions, the proposed interactive modules can efficiently generate low-dimensional discriminative multimodal representations. Extensive experiments on different cancer datasets unravel that our method not only successfully achieves state-of-the-art performance by performing effective multimodal fusion, but also possesses high computational efficiency compared to existing survival prediction methods.

De-Biased Disentanglement Learning for Pulmonary Embolism Survival Prediction on Multimodal Data.

Health disparities among marginalized populations with lower socioeconomic status significantly impact the fairness and effectiveness of healthcare delivery. The increasing integration of artificial intelligence (AI) into healthcare presents an opportunity to address these inequalities, provided that AI models are free from bias. This paper aims to address the bias challenges by population disparities within healthcare systems, existing in the presentation of and development of algorithms, leading to inequitable medical implementation for conditions such as pulmonary embolism (PE) prognosis. In this study, we explore the diverse bias in healthcare systems, which highlights the demand for a holistic framework to reducing bias by complementary aggregation. By leveraging de-biasing deep survival prediction models, we propose a framework that disentangles identifiable information from images, text reports, and clinical variables to mitigate potential biases within multimodal datasets. Our study offers several advantages over traditional clinical-based survival prediction methods, including richer survival-related characteristics and bias-complementary predicted results. By improving the robustness of survival analysis through this framework, we aim to benefit patients, clinicians, and researchers by enhancing fairness and accuracy in healthcare AI systems.

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram

BackgroundCervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients.MethodsPredictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox’s proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model’s prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics.ResultsThe 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively.ConclusionOur nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.

Survival Prediction via Hierarchical Multimodal Co-Attention Transformer: A Computational Histology-Radiology Solution.

The rapid advances in deep learning-based computational pathology and radiology have demonstrated the promise of using whole slide images (WSIs) and radiology images for survival prediction in cancer patients. However, most image-based survival prediction methods are limited to using either histology or radiology alone, leaving integrated approaches across histology and radiology relatively underdeveloped. There are two main challenges in integrating WSIs and radiology images: (1) the gigapixel nature of WSIs and (2) the vast difference in spatial scales between WSIs and radiology images. To address these challenges, in this work, we propose an interpretable, weakly-supervised, multimodal learning framework, called Hierarchical Multimodal Co-Attention Transformer (HMCAT), to integrate WSIs and radiology images for survival prediction. Our approach first uses hierarchical feature extractors to capture various information including cellular features, cellular organization, and tissue phenotypes in WSIs. Then the hierarchical radiology-guided co- attention (HRCA) in HMCAT characterizes the multimodal interactions between hierarchical histology-based visual concepts and radiology features and learns hierarchical co- attention mappings for two modalities. Finally, HMCAT combines their complementary information into a multimodal risk score and discovers prognostic features from two modalities by multimodal interpretability. We apply our approach to two cancer datasets (365 WSIs with matched magnetic resonance [MR] images and 213 WSIs with matched computed tomography [CT] images). Our results demonstrate that the proposed HMCAT consistently achieves superior performance over the unimodal approaches trained on either histology or radiology data alone, as well as other state-of-the-art methods.

FGCNSurv: dually fused graph convolutional network for multi-omics survival prediction.

Survival analysis is an important tool for modelling time-to-event data, for example, to predict the survival time of patient after a cancer diagnosis or a certain treatment. While deep neural networks work well in standard prediction tasks, it is still unclear how to best utilize these deep models in survival analysis due to the difficulty of modelling right censored data, especially for multi-omics data. Although existing methods have shown the advantage of multi-omics integration in survival prediction, it remains challenging to extract complementary information from different omics and improve the prediction accuracy. In this work, we propose a novel multi-omics deep survival prediction approach by dually fused graph convolutional network named FGCNSurv. Our FGCNSurv is a complete generative model from multi-omics data to survival outcome of patients, including feature fusion by a factorized bilinear model, graph fusion of multiple graphs, higher-level feature extraction by Graph convolutional network (GCN) and survival prediction by a Cox proportional hazard model. The factorized bilinear model enables to capture cross-omics features and quantify complex relations from multi-omics data. By fusing single-omics features and the cross-omics features, and simultaneously fusing multiple graphs from different omics, GCN with the generated dually fused graph could capture higher-level features for computing the survival loss in the Cox-PH model. Comprehensive experimental results on real-world datasets with gene expression and microRNA expression data show that the proposed FGCNSurv method outperforms existing survival prediction methods, and imply its ability to extract complementary information for survival prediction from multi-omics data. The codes are freely available at https://github.com/LiminLi-xjtu/FGCNSurv. Supplementary data are available at Bioinformatics online.

Rectal Cancer Prediction and Performance Based on Intelligent Variational Autoencoders Machine Using Deep Learning on CDAS Dataset

A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival. However, there isn't yet a valid prediction model available. An efficient feature extraction technique is also required to increase a prediction model's precision. CDAS (cancer data access system) program is a great place to look for cancer along with images or biospecimens. In this study, we look at data from the CDAS system, specifically Bowel cancer (colorectal cancer) datasets. This study suggested a survival prediction method for rectal cancer. In addition, determines which deep learning algorithm works best by comparing their performance in terms of prediction accuracy. The initial job that leads to correct findings is corpus cleansing. Moving forward, the data pre-processing activity will be performed, which will comprise "exploratory data analysis and pruning and normalization or experimental study of data, which is required to obtain data features to design the model for cancer detection at an early stage." Aside from that, the data corpus is separated into two sub-corpora: training data and test data, which will be utilized to assess the correctness of the constructed model. This study will compare our auto-encoder accuracy to that of other deep learning algorithms, such as ANN, CNN, and RBM, before implementing the suggested methodology and displaying the model's accuracy graphically after the suggested new methodology or algorithm for patients with rectal cancer. Various criteria, including true positive rate, ROC curve, and accuracy scores, are used in the experiments to determine the model's high accuracy. In the end, we determine the accuracy score for each model. The outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction models. It is shown that variational deep encoders have excellent accuracy of 94% in this cancer prediction and 95% for ROC curve regions. The findings demonstrate that automated prediction algorithms are capable of properly estimating rectal cancer patients’ chances of survival. The best results, with 95% accuracy, were generated by deep autoencoders.

Survival prediction of heart failure patients using motion-based analysis method

Background and Objective: Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment.Methods: We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients.Results: The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods.Conclusions: The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.

Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset

Efforts have been made to improve the risk stratification model for patients with diffuse large B-cell lymphoma (DLBCL). This study aimed to evaluate the disease prognosis using machine learning models with iterated cross validation (CV) method. A total of 122 patients with pathologically confirmed DLBCL and receiving rituximab-containing chemotherapy were enrolled. Contributions of clinical, laboratory, and metabolic imaging parameters from fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scans to the prognosis were evaluated using five regression models, namely logistic regression, random forest, support vector classifier (SVC), deep neural network (DNN), and fuzzy neural network models. Binary classification predictions for 3-year progression free survival (PFS) and 3-year overall survival (OS) were conducted. The 10-iterated fivefold CV with shuffling process was conducted to predict the capability of learning machines. The median PFS and OS were 41.0 and 43.6 months, respectively. Two indicators were found to be independent predictors for prognosis: international prognostic index and total metabolic tumor volume (MTVsum) from FDG PET/CT. For PFS, SVC and DNN (both with accuracy 71%) have the best predictive results, of which outperformed other algorithms. For OS, the DNN has the best predictive result (accuracy 76%). Using clinical and metabolic parameters as input variables, the machine learning methods with iterated CV method add the predictive values for PFS and OS evaluation in DLBCL patients.

CAMR: cross-aligned multimodal representation learning for cancer survival prediction.

Accurately predicting cancer survival is crucial for helping clinicians to plan appropriate treatments, which largely improves the life quality of cancer patients and spares the related medical costs. Recent advances in survival prediction methods suggest that integrating complementary information from different modalities, e.g. histopathological images and genomic data, plays a key role in enhancing predictive performance. Despite promising results obtained by existing multimodal methods, the disparate and heterogeneous characteristics of multimodal data cause the so-called modality gap problem, which brings in dramatically diverse modality representations in feature space. Consequently, detrimental modality gaps make it difficult for comprehensive integration of multimodal information via representation learning and therefore pose a great challenge to further improvements of cancer survival prediction. To solve the above problems, we propose a novel method called cross-aligned multimodal representation learning (CAMR), which generates both modality-invariant and -specific representations for more accurate cancer survival prediction. Specifically, a cross-modality representation alignment learning network is introduced to reduce modality gaps by effectively learning modality-invariant representations in a common subspace, which is achieved by aligning the distributions of different modality representations through adversarial training. Besides, we adopt a cross-modality fusion module to fuse modality-invariant representations into a unified cross-modality representation for each patient. Meanwhile, CAMR learns modality-specific representations which complement modality-invariant representations and therefore provides a holistic view of the multimodal data for cancer survival prediction. Comprehensive experiment results demonstrate that CAMR can successfully narrow modality gaps and consistently yields better performance than other survival prediction methods using multimodal data. CAMR is freely available at https://github.com/wxq-ustc/CAMR. Supplementary data are available at Bioinformatics online.

An Optimized Survival Prediction Method for Kidney Transplant Recipients

An Optimized Survival Prediction Method for Kidney Transplant Recipients

Multi-Modal Analysis and Federated Learning Approach for Classification and Personalized Prognostic Assessment in Myeloid Neoplasms

Background Myeloid neoplasms (MN) present clinical and molecular heterogeneity and therefore a risk-adapted treatment strategy is mandatory. In MN, classification and prognostic tools based on clinical and morphologic criteria are being complemented by introducing genomic features. The clinical implementation of next-generation classifications and prognostic systems requires the availability of a robust methodological framework together with a solution to provide access to these technologies for clinicians. Aims Machine learning (ML) and Deep Learning (DL) approaches produce powerful predictive models and offer explainable solutions to assure full interpretability of a model when applied in clinical settings. Here we provided a comprehensive assessment of explainable ML/DL-based methods for classification and prognostic assessment of MN and we developed a solution to apply these methods across different clinical Centres through a Federated Learning (FL) approach. Methods We analysed two cohorts of patients from GenoMed4All consortium with myelodysplastic syndrome (MDS), n=2,043 and n=2,384, with available clinical and molecular features to train and validate the models. Methods were then applied to other MN, i.e. acute myeloid leukemia (AML, n=1154) and chronic myelomonocytic leukemia (CMML, n=1037). We stratified patients by two clustering approaches based on Hierarchical Dirichlet Process (HDP) and HDBSCAN combined with UMAP data reduction. We trained a Random Forest (RF) classifier to assign new patients to the existing clusters, considering Balanced Accuracy (BA) and Cohen's K (CK) as performance metrics. We then compared different survival prediction methods: CoxPH model (and its penalized version), Random Survival Forests, DeepCox, Gradient Boosting and XGboost survival methods. Models’ explainability was performed through SHapley Additive exPlanations approach (SHAP). C-index was used to evaluate the models performance. Finally, we developed a Federated Learning (FL) environment together with an imputation approach to handle missing values by a deep decoder model. Results In MDS training cohort, we identified 18 and 8 clusters by using HDBSCAN and HDP, respectively (Figure 1). We measured the average Silhouette Coefficient on the data space obtaining the following performance in terms of classification task: HDBSCAN (BA:92.7±1.3%, CK:92.1±1.4%) and HDP (BA:85.8±0.8%, CK:83.3±0.9%). Similar distributions were observed when focusing on the validation cohort. Model explainability analysis (SHAP) showed that in both populations similar features drive patients’ classification. Comparison of survival prediction for MDS is displayed in Figure 2, showing the models’ performance in the two cohorts considering demographics, clinical, cytogenetics and genomic features. Non-linear ML/DL-based methods outperformed classical CoxPH-based approaches without requiring huge data pre-processing. Moreover, all the models showed higher C-indices with respect to that of conventional IPSS-R score. SHAP analysis showed similar feature importance ranking for both training and validation cohorts. Models were then applied to AML and CMML cohorts, showing consistent results across different type of MN. Finally, we aimed to develop a federated learning (FL) solution (FedAvg, with a deep decoder model for missing data imputation) to favour a wide clinical implementation of the models. Data were collected to a single server and used to build and train a centralized model. Using global data training was expected to improve the model efficiency. This approach also ensured that the data in each node adhere to data privacy policies. We implemented CoxPH model in a setting of 3 nodes (Centers) respectively contributing to 60%, 30% and 10% of the training data. We observed that the poor node (i.e., node contributing to 10% of data) benefit from FedAvg with respect to working on an isolated setting (C-index 0.63 vs. 0.54). The centralized model trained on the whole dataset presented the highest efficiency (C-index 0.74). Conclusion Machine Learning/Deep Learning approach produces explainable and robust solutions to optimize classification and prognostic assessment in MN, as a basis for personalized medicine programs in these disorders. Federate learning algorithms allow a wide clinical implementation of the models by ensuring high performance and data protection. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

State-of-the-art techniques using pre-operative brain MRI scans for survival prediction of glioblastoma multiforme patients and future research directions.

ObjectiveGlioblastoma multiforme (GBM) is a grade IV brain tumour with very low life expectancy. Physicians and oncologists urgently require automated techniques in clinics for brain tumour segmentation (BTS) and survival prediction (SP) of GBM patients to perform precise surgery followed by chemotherapy treatment.MethodsThis study aims at examining the recent methodologies developed using automated learning and radiomics to automate the process of SP. Automated techniques use pre-operative raw magnetic resonance imaging (MRI) scans and clinical data related to GBM patients. All SP methods submitted for the multimodal brain tumour segmentation (BraTS) challenge are examined to extract the generic workflow for SP.ResultsThe maximum accuracies achieved by 21 state-of-the-art different SP techniques reviewed in this study are 65.5 and 61.7% using the validation and testing subsets of the BraTS dataset, respectively. The comparisons based on segmentation architectures, SP models, training parameters and hardware configurations have been made.ConclusionThe limited accuracies achieved in the literature led us to review the various automated methodologies and evaluation metrics to find out the research gaps and other findings related to the survival prognosis of GBM patients so that these accuracies can be improved in future. Finally, the paper provides the most promising future research directions to improve the performance of automated SP techniques and increase their clinical relevance.

The Concordance Index Decomposition - A Measure for a Deeper Understanding of Survival Prediction Models

The Concordance Index (C-index) is a commonly used metric in Survival Analysis to evaluate how good a prediction model is. This paper proposes a decomposition of the C-Index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition allows a more fine-grained analysis of the pros and cons of survival prediction methods. The utility of the decomposition is demonstrated using three benchmark survival analysis models (Cox Proportional Hazard, Random Survival Forest, and Deep Adversarial Time-to-Event Network) together with a new variational generative neural-network-based method (SurVED), which is also proposed in this paper. The demonstration is done on four publicly available datasets with varying censoring levels. The analysis with the C-index decomposition shows that all methods essentially perform equally well when the censoring level is high because of the dominance of the term measuring the ranking of events versus censored cases. In contrast, some methods deteriorate when the censoring level decreases because they do not rank the events versus other events well.

Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units.

Background A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability. We collected information on patients with various diseases in coronary care units (CCUs) from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The purpose of this study was to use this information to construct a neural-network model based on deep learning to predict the survival probabilities of patients with conditions that are common in CCUs. Method We collected information on patients in the United States with five common diseases in CCUs from 2001 to 2012. We randomly divided the patients into a training cohort and a testing cohort at a ratio of 7 : 3 and applied a survival prediction method based on deep learning to predict their survival probability. We compared our model with the Cox proportional-hazards regression (CPH) model and used the concordance indexes (C-indexes), receiver operating characteristic (ROC) curve, and calibration plots to evaluate the predictive performance of the model. Results The 3,388 CCU patients included in the study were randomly divided into 2,371 in the training cohort and 1,017 in the testing cohort. The stepwise regression results showed that the important factors affecting patient survival were the type of disease, age, race, anion gap, glucose, neutrophils, white blood cells, potassium, creatine kinase, and blood urea nitrogen (P < 0.05). We used the training cohort to construct a deep-learning model, for which the C-index was 0.833, or about 5% higher than that for the CPH model (0.786). The C-index of the deep-learning model for the test cohort was 0.822, which was also higher than that for the CPH model (0.782). The areas under the ROC curve for the 28-day, 90-day, and 1-year survival probabilities were 0.875, 0.865, and 0.874, respectively, in the deep-learning model, respectively, and 0.830, 0.843, and 0.806 in the CPH model. These values indicate that the survival analysis model based on deep learning is better than the traditional CPH model in predicting the survival of CCU patients. Conclusion A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability.

A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients.

Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.