Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients
Zhenyu Tang,Yuyun Xu,Lei Jin,Abudumijiti Aibaidula,Junfeng Lu,Zhicheng Jiao,Jinsong Wu,Han Zhang,Dinggang Shen +8 more
TLDR
A new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction, achieves the highest OS prediction accuracy compared to other state-of-the-art methods.Abstract:
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly . As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.read more
Citations
More filters
Journal ArticleDOI
An overview of deep learning methods for multimodal medical data mining
F. Behrad,Mohammad Saniee Abadeh +1 more
TL;DR: Deep learning methods have achieved significant results in various fields as discussed by the authors , and many researchers have used deep learning algorithms in medical analyses, using multimodal data to achieve more accurate results.
Journal ArticleDOI
Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma.
Yiping Lu,Markand Patel,K. Natarajan,Ismail Ughratdar,Paul Sanghera,Raj Jena,Colin Watts,Vijay Sawlani +7 more
TL;DR: VASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI.
Journal ArticleDOI
Weakly supervised deep learning for determining the prognostic value of 18 F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type
Rui Guo,Xiaobin Hu,Haoming Song,Pengpeng Xu,Haoping Xu,Axel Rominger,xiaozhu Lin,Bjoern H. Menze,Bjoern H. Menze,Biao Li,Kuangyu Shi,Kuangyu Shi +11 more
TL;DR: The WSDL-based framework was more effective for extracting 18F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method and PSI confirmed to be an independent significant predictor of PFS in both the methods.
Journal ArticleDOI
DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing.
Jiawen Yao,Yu Shi,Kai Cao,Le Lu,Jianping Lu,Qike Song,Gang Jin,Jing Xiao,Yang Hou,Ling Zhang +9 more
TL;DR: Wang et al. as discussed by the authors proposed a 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE-ConvLSTM), which can derive the tumor attenuation signatures or patterns from patient CE-CT imaging studies.
Journal ArticleDOI
Dynamic Architecture Based Deep Learning Approach for Glioblastoma Brain Tumor Survival Prediction
TL;DR: In this paper , the authors proposed the dynamic architecture of multilevel layer modelling in Faster R-CNN (MLL-CNN) approach based on feature weight factor and relative description model to build the selected features.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book ChapterDOI
Nonparametric Estimation from Incomplete Observations
Edward L. Kaplan,Paul Meier +1 more
TL;DR: In this article, the product-limit (PL) estimator was proposed to estimate the proportion of items in the population whose lifetimes would exceed t (in the absence of such losses), without making any assumption about the form of the function P(t).
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Proceedings Article
A study of cross-validation and bootstrap for accuracy estimation and model selection
TL;DR: The results indicate that for real-word datasets similar to the authors', the best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.