Sequential Representation of Clinical Data for Full-Fitting Survival Prediction

2017 ◽  
Author(s):  
Jianfei Zhang ◽  
Lifei Chen ◽  
Aurelien Bach ◽  
Josiane Courteau ◽  
Alain Vanasse ◽  
...  
Keyword(s):  
Clinical Data ◽  
Survival Prediction

scholarly journals Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 956
Author(s):  
Marcello Andrea Tipaldi ◽  
Edoardo Ronconi ◽  
Elena Lucertini ◽  
Miltiadis Krokidis ◽  
Marta Zerunian ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Clinical Data ◽  
Transarterial Chemoembolization ◽  
Prognostic Value ◽  
Texture Features ◽  
Response To Therapy ◽  
Survival Prediction ◽  
Ct Examination ◽  
Drug Eluting ◽  
Pre Treatment

(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 (p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03–2.35) using texture features, of 1.7 (95% CI: 1.54–1.9) using clinical data and of 4.61 (95% CI: 4.24–5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.

Deep learning to develop transcriptomic model for survival prediction in TCGA patients with hepatocellular carcinoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14057-e14057
Author(s):  
Hao Yu ◽  
Wei Dai ◽  
Chi Leung Chiang ◽  
Shisuo Du ◽  
Zhao-Chong Zeng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Creatinine Level ◽  
Clinical Data ◽  
Cox Regression ◽  
P Value ◽  
Survival Prediction ◽  
Signal Pathways ◽  
Test Set ◽  
Hidden Layer

e14057 Background: This study aimed to investigate the prognostic value of transcriptome and clinical data of Hepatocellular carcinoma (HCC) patients for overall survival (OS) by deep learning method. Methods: A total of 371 HCC patients with 20530 level three RNA-sequencing data were from The Cancer Genome Atlas (TCGA). Cox-nnet model, a deep learning model through an artificial neural network extension of the Cox regression model, was used for OS prediction. The patients were randomly split into train-set and test-set (7:3). In train-set, the significant genes associated with OS under univariate Cox regression were considered for modeling. Clinical parameters, including age, gender, pathologic stage, child pugh classification, creatinine level etc. were also considered. The Cox-nnet model was developed by cross-validation. Its discrimination was determined by the concordance index (CI) in the independent test-set and compared with multivariable Cox regression. The clustering method Uniform Manifold Approximation and Projection (UMAP) was used for revealing biological information from the hidden layer in the model. Results: In the train-set (n = 259), 1505 genes and two clinical variables (child pugh score and creatinine level) were significantly associated with OS (adjusted P-value < 0.05). To avoid overfitting, only 40 most significant genes were included in the Cox-nnet model. In the test-set (n = 112), the CI of Cox-nnet (0.76, se = 0.04) is better than the CI of multivariable Cox regression (0.71, se = 0.05). The difference between good or poor survival subgroups classified by Cox-nnet was remarkably significant ( P-value = 1e-4, median OS: 80.7 vs. 25.1 months). In the Cox-nnet model with all significant variables, the weights in the hidden layer were clustered by UMAP into 3 positive clusters and 2 negative clusters, which are enriched in GO/KEGG. The “cell cycle” and “complement and coagulation cascades” are the most important signal pathways in positive and negative clusters, respectively. Conclusions: Combining transcriptomic and clinical data, and with deep learning algorithm, we built and validated a robust model for survival prediction in HCC patients. Our study would be useful to explore the clinical implications in survival prediction and corresponding genetic mechanisms. Clinical trial information: 5U24CA143799, 5U24CA143835, 5U24CA143840, 5U24CA143843, 5U24CA143845, 5U24CA143848, 5U24CA1438.

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Weizhou Guo ◽  
Wenbin Liang ◽  
Qingchun Deng ◽  
Xianchun Zou
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Data ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Survival Prediction ◽  
Term Survival ◽  
Multimodal Data ◽  
Structured Information ◽  
Fusion Feature

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.

scholarly journals A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

2021 ◽  
Author(s):  
Benjamin Haibe-Kains ◽  
Michal Kazmierski ◽  
Mattea Welch ◽  
Sejin Kim ◽  
Chris McIntosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Clinical Data ◽  
Neck Cancer ◽  
Evaluation Framework ◽  
Cancer Prognosis ◽  
Survival Prediction ◽  
Precision Oncology

Abstract Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative predictive and prognostic biomarkers from routine medical imaging, but evidence for computed tomography radiomics for prognosis remains inconclusive. We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis. Using a large, retrospective dataset of 2,552 patients and a rigorous evaluation framework, we compared 12 different submissions using imaging and clinical data, separately or in combination. The winning approach used non-linear, multitask learning on clinical data and tumour volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction and outperforming models relying on clinical data only, engineered radiomics and deep learning. Combining all submissions in an ensemble model resulted in improved accuracy, with the highest gain from a image-based deep learning model. Our results show the potential of machine learning and simple, informative prognostic factors in combination with large datasets as a tool to guide personalized cancer care.

Survival Prediction Based on Histopathology Imaging and Clinical Data: A Novel, Whole Slide CNN Approach

2021 ◽  
pp. 762-771
Author(s):  
Saloni Agarwal ◽  
Mohamedelfatih Eltigani Osman Abaker ◽  
Ovidiu Daescu
Keyword(s):  
Clinical Data ◽  
Survival Prediction

scholarly journals Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Hongling Chen ◽  
Mingyan Gao ◽  
Ying Zhang ◽  
Wenbin Liang ◽  
Xianchun Zou
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profile ◽  
Clinical Data ◽  
Expression Profile ◽  
Nonnegative Matrix ◽  
Survival Prediction ◽  
Profile Data ◽  
Prognostic Prediction ◽  
Gene Expression Profile Data

Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medical decisions. As well known, cancer is a partly inherited disease with various important biological markers, especially the gene expression profile data and clinical data. Therefore, the accuracy of prediction model can be improved by integrating gene expression profile data and clinical data. In this paper, we proposed an end-to-end model, Attention-based Multi-NMF DNN (AMND), which combines clinical data and gene expression data extracted by Multiple Nonnegative Matrix Factorization algorithms (Multi-NMF) for the prognostic prediction of breast cancer. The innovation of this method is highlighted through using clinical data and combining multiple feature selection methods with the help of Attention mechanism. The results of comprehensive performance evaluation show that the proposed model reports better predictive performances than either models only using data of single modality, e.g., gene or clinical, or models based on any single NMF improved methods which only use one of the NMF algorithms to extract features. The performance of our model is competitive or even better than other previously reported models. Meanwhile, AMND can be extended to the survival prediction of other cancer diseases, providing a new strategy for breast cancer prognostic prediction.

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