Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data

Cover
The Elsevier and Miccai Society Book Series
Advisory Board
Titles
Radiomics and Its Clinical ApplicationArtificial Intelligence and Medical Big Data
Copyright
Preface
Biographies
1. Introduction
1.1 Background of medical image analysis in cancer
1.2 Multidimensional complexity of biomedical research
1.3 Concept of radiomics
1.4 Value of radiomics
1.5 Workflow of radiomics
1.5.1 Image acquisition and reconstruction
1.5.2 Image segmentation
1.5.3 Feature extraction and selection
1.5.4 Database and data sharing
1.5.5 Informatics analysis
1.5.6 Medical image acquisition
1.5.7 Segmentation of the tumor
1.5.8 Tumor image phenotype
1.5.9 Clinical prediction for tumor
1.5.10 New technology of artificial intelligence
1.6 Prospect of clinical application of radiomics
References
2. Key technologies and software platforms for radiomics
2.1 Tumor detection
2.1.1 Data preprocessing
2.1.2 Detection of candidate nodules
2.2 Tumor segmentation
2.2.1 Segmentation of pulmonary nodules based on the central-focused convolutional neural network
2.2.2 Segmentation of brain tumor based on the convolutional neural network
2.2.3 Fully convolutional networks
2.2.4 Voxel segmentation algorithm based on MV-CNN
2.3 Feature extraction
2.3.1 The features of artificial design
2.3.2 Deep learning features
2.4 Feature selection and dimension reduction
2.4.1 Classical linear dimension reduction
2.4.2 Dimension reduction method based on feature selection
2.4.3 Feature selection based on the linear model and regularization
2.5 Model building
2.5.1 Linear regression model
2.5.2 Linear classification model
2.5.3 Tree models
2.5.4 AdaBoost
2.5.5 Model selection
2.5.6 Convolutional neural network
2.5.7 Migration learning
2.5.8 Semisupervised learning
2.6 Radiomics quality assessment system
2.7 Radiomics software platform
2.7.1 Radiomics software
2.7.2 Pyradiomics—radiomics algorithm library
References
3. Precision diagnosis based on radiomics
3.1 Application of radiomics in cancer screening
3.1.1 Lung cancer screening
3.1.2 Gastrointestinal cancer screening
3.1.3 Breast cancer screening
3.1.4 Prostate cancer screening
3.2 Application of radiomics in cancer staging
3.2.1 Prediction of parametrial invasion in cervical cancer
3.2.2 Correlation between PET and CT features in lymph node metastasis
3.2.3 Prediction of lymph node metastasis in colorectal cancer
3.2.4 Prediction of axillary lymph node status in breast cancer
3.2.5 Prediction of lymph node metastases in gastric cancer
3.2.6 Prediction of distant metastasis in lung adenocarcinoma
3.2.7 Prediction of distant metastasis in oropharyngeal cancer
3.2.8 Prediction of distant metastasis in nasopharyngeal carcinoma
3.2.9 Prediction of occult peritoneal metastasis in gastric cancer
3.3 Application of radiomics in histopathological diagnosis of cancer
3.3.1 Prediction of Gleason score in prostate cancer
3.3.2 Prediction of histopathological grade in bladder cancer
3.3.3 Prediction of histopathological grade in cervical cancer
3.3.4 Identification of pathological subtype of lung ground-glass nodules
3.3.5 Identification of histologic subtype in non-small cell lung cancer
3.4 Application of radiomics in prediction of cancer gene mutation and molecular subtype
3.4.1 Prediction of somatic mutations in lung cancer
3.4.2 Prediction of gene mutations in gliomas
3.4.3 Prediction of KRAS/NRAS/BRAF mutations in colorectal cancer
3.4.4 Prediction of molecular subtypes in breast cancer
3.5 Application of radiomics in other diseases
3.5.1 Diagnosis of COVID-19
3.5.2 Staging of liver fibrosis
3.5.3 Diagnosis of portal hypertension
3.5.4 Diagnosis of cardiovascular plaques
3.5.5 Identification of coronary plaques with napkin-ring sign
References
4. Treatment evaluation and prognosis prediction using radiomics in clinical practice
4.1 Radiomics and its application in treatment evaluation
4.1.1 Evaluation of radiotherapy
4.1.1.1 Application 1 of radiomics for radiotherapy effect evaluation: association of radiomic data extracted from static respirato ...
4.1.1.2 Application 2 of radiomics for radiotherapy effect evaluation: comparison between radiological experts and radiomics for pr ...
4.1.1.3 Application 3 of radiomics for radiotherapy effect evaluation: texture analysis on parametric maps derived from dynamic con ...
4.1.2 Evaluation of response to targeted therapy
4.1.2.1 Application 1 of radiomics on the evaluation of the response to targeted therapy: evaluation of the treatment response of g ...
4.1.2.2 Application 2 of radiomics in the evaluation of the response to targeted therapy: CT-based radiomics analysis for the evalu ...
4.1.2.3 Application 3 of radiomics on the evaluation of the response to targeted therapy: PET/MRI-based radiomics analysis on the e ...
4.1.2.4 Application 4 of radiomics on the evaluation of response to targeted therapy: radiomics analysis on the prediction of progr ...
4.1.2.5 Application 5 of radiomics on the evaluation of response to targeted therapy: radiomics analysis on the prediction of treat ...
4.1.3 Application of radiogenomics in efficacy evaluation
4.1.3.1 Application 1 of radiogenomics in efficacy evaluation: non–small cell lung cancer radiogenomics map identifies relationship ...
4.1.3.2 Application 2 of radiogenomics in efficacy evaluation: magnetic resonance perfusion image features uncover an angiogenic su ...
4.1.3.3 Application 3 of radiogenomics in efficacy evaluation: relationships between computer-extracted mammographic texture patter ...
4.1.3.4 Application 4 of radiogenomics in efficacy evaluation: a radiogenomics signature for predicting the clinical outcome of bla ...
4.2 Radiomics-based prognosis analysis
4.2.1 Lung cancer
4.2.1.1 Prediction of recurrence/metastasis
4.2.1.1.1 CT imaging
4.2.1.1.2 PET/CT imaging
4.2.1.2 Prediction of survival
4.2.1.3 Surgery
4.2.1.4 Radiation therapy/chemotherapy/concurrent chemoradiotherapy
4.2.1.4.1 CT imaging
4.2.1.4.2 PET/CT imaging
4.2.1.5 Targeted therapy
4.2.1.5.1 CT imaging
4.2.1.5.2 PET/CT imaging
4.2.1.6 Immunotherapy
4.2.1.6.1 CT imaging
4.2.1.6.2 PET/CT imaging
4.2.2 Breast cancer
4.2.2.1 Prediction of survival
4.2.2.1.1 MRI
4.2.2.1.2 PET/CT imaging
4.2.3 Prostate cancer
4.2.3.1 Prediction of recurrence
4.2.4 Colorectal cancer
4.2.4.1 Prediction of recurrence/metastasis
4.2.4.2 Prediction of survival
4.2.4.2.1 MRI
4.2.4.2.2 PET/CT imaging
4.2.4.2.3 CT imaging
4.2.5 Esophageal and gastric cancers
4.2.5.1 Esophageal/esophago-gastric cancer
4.2.5.1.1 CT imaging
4.2.5.1.2 PET/CT imaging
4.2.5.2 Gastric cancer
4.2.5.2.1 CT imaging
4.2.5.2.2 PET/CT imaging
4.2.6 Liver cancer
4.2.6.1 Prediction of recurrence/metastasis
4.2.6.1.1 CT imaging
4.2.6.1.2 MRI
4.2.6.2 Prediction of survival
4.2.6.2.1 CT imaging
4.2.6.2.2 PET/CT imaging
4.2.7 Pancreatic cancer
4.2.7.1 Prediction of recurrence/metastasis
4.2.7.2 Prediction of survival
4.2.7.2.1 CT imaging
4.2.7.2.2 PET/CT imaging
4.2.8 Cervix cancer
4.2.8.1 Prediction of recurrence/metastasis
4.2.8.1.1 PET/CT imaging
4.2.8.1.2 MRI
4.2.8.1.3 Combined PET/CT imaging and MRI
4.2.8.2 Prediction of survival
4.2.8.2.1 PET/CT imaging
4.2.8.2.2 MRI
4.2.8.3 Combined PET/CT imaging and MRI
4.2.9 Central nervous system cancers
4.2.9.1 Prediction of recurrence/metastasis
4.2.9.2 Prediction of survival
4.2.9.2.1 MRI
4.2.9.2.2 PET/CT imaging
4.2.10 Other solid cancers
4.2.10.1 Blood cancers
4.2.10.1.1 Lymphoma
4.2.10.1.2 PET/CT imaging
4.2.10.1.3 MRI
4.2.10.1.4 CT imaging
4.2.10.1.5 Myeloma
References
5. Summary and prospects
5.1 Summary
5.2 Prospect
5.2.1 Prospective clinical application of radiomics
5.2.1.1 Radiogenomics
5.2.1.2 Immunotherapy
5.2.1.3 Virtual biopsy
5.2.1.4 Delta radiomics
5.2.2 Formulate the research norms
5.2.3 Fundamentals of medical big data
5.2.4 Lesion segmentation algorithms
5.2.5 Reproducibility of the experiment
5.2.6 Influence of machine parameters
5.2.7 Integration of radiomics and multi-omics
5.2.8 Prospective study
5.2.9 Distributed learning in medical research
5.2.10 Interpretability of radiomics
5.2.11 Advancement in clinical guidelines
5.3 Conclusion
References
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
P
Q
R
S
T
V
W
X