Radiomics and Radiogenomics in Neuro-oncology: First International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, ... (Lecture Notes in Computer Science (11991))

This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging. Preface Organization Contents Current Status of the Use of Machine Learning and Magnetic Resonance Imaging in the Field of Neuro-Radiomics Abstract 1 Introduction 2 Neuro-Radiomics 3 Standard MRI Protocols 4 Machine Learning in Neuro-Radiomics 4.1 Survival of Brain Tumors 4.2 Predictions of Infiltration and Recurrence 4.3 Response Assessment of Brain Tumors 4.4 Characterization of Imaging Heterogeneity 5 Discussion 6 Conclusion References Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology 1 Introduction 1.1 What Is Radiomics? 1.2 What Is Radiogenomics? 2 The Radiomics/Radiogenomics Pipeline 2.1 Pre-processing and Segmentation 2.2 Quantitative Features Extraction 2.3 Classifier Construction and Analysis 2.4 Radiogenomic Analysis 3 Applications in Neuro-Oncology 3.1 Classification and Grading of Brain Tumors 3.2 Survival Risk Stratification in Brain Tumors 3.3 Characterizing Post-treatment Changes in Brain Malignancies 3.4 Radiogenomic Analysis of Brain Tumors 4 Limitations 5 Future Scope References A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology 1 Introduction 2 Radiomics Using Handcrafted Features 3 Radiomics Using Deep Learning 4 Discussion and Conclusion References Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma Abstract 1 Introduction 2 Materials and Methods 2.1 Datasets 2.2 Proposed Deep Radiomic Features (DRFs) 2.3 Classifications and Survival Analysis 3 Results 4 Discussion 5 Conclusions References cuRadiomics: A GPU-Based Radiomics Feature Extraction Toolkit Abstract 1 Introduction 2 Methods 2.1 Computation of First-Order Features 2.2 Computation of Texture Features 3 Quantitative Evaluation 3.1 Environment 3.2 Dataset 3.3 Experimental Results 4 Discussion 5 Conclusion References On Validating Multimodal MRI Based Stratification of IDH Genotype in High Grade Gliomas Using CNNs and Its Comparison to Radiomics Abstract 1 Introduction 2 Method 2.1 Study Cohort and Imaging 2.2 Image Processing 2.3 Radiomics 2.4 CNNs with Class Activation Maps 2.5 Training and Testing 3 Results 4 Conclusion References Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma Abstract 1 Introduction 2 Materials and Methods 2.1 Data Acquisition 2.2 Image Preprocessing Applied on the Dataset 2.3 Segmentation of Sub-regions of Tumor 2.4 Quantitative Imaging Features Extracted from MRI Scans 2.5 Spatial Distribution and Pattern of the Tumor 2.6 Selection of Features and Development of Predictive Model 3 Results and Application 3.1 Classification Performance of Predictive Model 3.2 Selected Quantitative Features 3.3 Clinical Implications 4 Conclusion and Future Work References A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma 1 Introduction 2 Materials and Methods 2.1 Feature Extraction 2.2 Feature Selection 2.3 Statistical Analysis 2.4 Model Development 3 Results 4 Discussion 5 Conclusion References Radiomics-Enhanced Multi-task Neural Network for Non-invasive Glioma Subtyping and Segmentation 1 Introduction 2 Related Work 2.1 Grading and Subtyping Gliomas Using Radiomic Methods 2.2 Automated Brain Tumor Segmentation 3 Methods 3.1 Radiomic Feature Extraction 3.2 Shared CNN Encoder and Segmentation Decoder 3.3 Subtyping Branch 3.4 Multi-task Loss Function 4 Experiments and Results 4.1 Dataset 4.2 Training and Inference 4.3 Results 5 Discussion and Conclusion References Author Index