The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

Moradmand, Hajar; Aghamiri, Seyed Mahmoud Reza; Ghaderi, Reza; Emami, Hamid

doi:10.1002/cam4.4230

Cited by 18 publications

(18 citation statements)

References 36 publications

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“…Groups from India proposed k-adaptive partitioning derived simple stage system using five baseline parameters and validated higher values of C-index on both MMIn and MMRF datasets, which outperformed ISS for OS calculation but was equivalent in the prognosis of PFS ( 22 ). However, most of these data came from clinical trials, and their role in real-world MM predictions, especially in older patients, is unclear ( 23 ).Although there have also been researches that applied machine learning and deep learning algorithms to build models and make survival predictions using real-world oral cancer ( 24 , 25 ), breast cancer ( 26 ) and glioblastoma ( 27 ) patient data, the implementation of these methods on elderly MM patient data have not been fully discussed.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning–Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data

Wang

Zhang

et al. 2022

Front. Oncol.

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ObjectiveTo use machine learning methods to explore overall survival (OS)-related prognostic factors in elderly multiple myeloma (MM) patients.MethodsData were cleaned and imputed using simple imputation methods. Two data resampling methods were implemented to facilitate model building and cross validation. Four algorithms including the cox proportional hazards model (CPH); DeepSurv; DeepHit; and the random survival forest (RSF) were applied to incorporate 30 parameters, such as baseline data, genetic abnormalities and treatment options, to construct a prognostic model for OS prediction in 338 elderly MM patients (>65 years old) from four hospitals in Beijing. The C-index and the integrated Brier score (IBwere used to evaluate model performances.ResultsThe 30 variables incorporated in the models comprised MM baseline data, induction treatment data and maintenance therapy data. The variable importance test showed that the OS predictions were largely affected by the maintenance schema variable. Visualizing the survival curves by maintenance schema, we realized that the immunomodulator group had the best survival rate. C-indexes of 0.769, 0.780, 0.785, 0.798 and IBS score of 0.142, 0.112, 0.108, 0.099 were obtained from the CPH model, DeepSurv, DeepHit, and the RSF model respectively. The RSF model yield best scores from the fivefold cross-validation, and the results showed that different data resampling methods did affect our model results.ConclusionWe established an OS model for elderly MM patients without genomic data based on 30 characteristics and treatment data by machine learning.

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Section: Discussionmentioning

confidence: 99%

Machine Learning–Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data

Wang

Zhang

et al. 2022

Front. Oncol.

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“…Moreover, the authors in [ 38 ] employed a deep learning method to diagnose Alzheimer’s disease. Moreover, SAVAE-COX [ 39 ], PubMed [ 40 ], and Page-Net [ 41 ] employ deep learning for survival time prediction. Similarly, a statistical model (SM) was proposed in [ 42 ], which efficiently predicts the survival time.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, to evaluate the classification performance, the proposed model is compared with 3DCNN [ 55 ] using five modalities, i.e., t1, t1ce, t2, Flair, and segmented. The survival prediction efficiency is evaluated in comparison with SAVAE-COX [ 39 ], CoxPH [ 40 ], PAGE-Net [ 41 ], the statistical machine learning algorithm [ 42 ], SVM [ 35 ], and the random forest classifier [ 20 ]. The following subsection details the simulation parameters used in this work.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Moreover, for the classification comparative analysis, this work used the confusion matrix for tumor classification [ 14 , 28 , 55 ]. Finally, the Concordance Index (

) was utilized to evaluate the performance of our proposed model in terms of survival prediction, which is widely used for survival analyses in different models [ 39 , 40 ].…”

Section: Performance Evaluationmentioning

confidence: 99%

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ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

et al. 2023

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Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction challenging. Existing literature provides several survival prediction models that use different parameters, such as patient age, gross total resection status, tumor size, or tumor grade. However, accuracy is often lacking in these models. The use of tumor volume instead of size may improve the accuracy of survival prediction. In response to this need, we propose a novel model, the enhanced brain tumor identification and survival time prediction (ETISTP), which computes tumor volume, classifies it into low- or high-grade glioma, and predicts survival time with greater accuracy. The ETISTP model integrates four parameters: patient age, survival days, gross total resection (GTR) status, and tumor volume. Notably, ETISTP is the first model to employ tumor volume for prediction. Furthermore, our model minimizes the computation time by allowing for parallel execution of tumor volume computation and classification. The simulation results demonstrate that ETISTP outperforms prominent survival prediction models.

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“…Katzman et al also developed a novel deep feed-forward neural network based on Cox assumption called DeepSurv, which combined survival analysis with deep learning and had the advantage to perform a prediction of time-to-event data. It has been successfully applied in the survival analysis of multiple diseases and showed promising performance in predicting patients' outcomes, such as oncological diseases, Covid-19, and atherosclerotic cardiovascular disease (22)(23)(24)(25)(26). Several online calculation tools were constructed based on the DeepSurv method (27)(28)(29).…”

Section: Introductionmentioning

confidence: 99%

Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients

Ren

et al. 2022

Front. Cardiovasc. Med.

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BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717–0.817), AUC of 0.780(95%CI: 0.721–0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

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The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 18 publications

References 36 publications

Machine Learning–Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data

Machine Learning–Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients

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