PRELIMS.pdf
Preface
Acknowledgements
Editor Biographies
Ayman El-Baz
Jasjit S Suri
Contributors
CH001.pdf
Chapter 1 Early diagnosis system for lung nodules based on the integration of a higher-order MGRF appearance feature model and 3D-CNN
1.1 Introduction
1.2 Methods
1.2.1 Appearance features using MGRF energy
Algorithm 1. Learning the seventh-order MGRF appearance model
1.2.2 Local feature extraction using a 3D-CNN
1.2.3 Nodule classification using autoencoders
1.3 Experimental results
1.4 Conclusion
References
CH002.pdf
Chapter 2 Capsule networks for lung cancer screening
2.1 Introduction
2.2 Capsule network
2.2.1 Capsule computation
2.2.2 Dynamic routing
2.2.3 Capsule network architecture
2.3 Fast capsule network
2.3.1 Consistent dynamic routing
2.3.2 Convolutional decoder
2.3.3 Loss function
2.4 Dataset
2.5 Experiments
2.5.1 2D capsule network
2.5.2 3D capsule network
2.6 Results and discussion
2.7 Conclusions
References
CH003.pdf
Chapter 3 Quantitative malignancy recognition of lung cancer using non-invasive image modalities
3.1 Introduction
3.2 Materials and methods
3.2.1 Patient information
3.2.2 Multichannel features
3.2.3 Result
3.2.4 Discussion
3.3 Conclusion
References
CH004.pdf
Chapter 4 Epidemiology of lung cancer
4.1 Descriptive epidemiology of lung cancer
4.2 Risk factors of lung cancer
4.2.1 Tobacco
4.2.2 Second-hand smoke
4.2.3 Diet, obesity, and physical activity
4.2.4 Occupational exposures
4.2.5 Ionizing radiation
4.2.6 Environmental air pollution
4.2.7 Infection
4.2.8 Genetic factors
4.3 Lung cancer in never-smokers
4.4 Screening
4.5 Conclusion
References
CH005.pdf
Chapter 5 Use of biomarkers in lung cancer diagnosis, prognosis, and treatment
5.1 Introduction
5.2 Histological subtypes and respective personalized medicine
5.3 Available screening assays to detect molecular alterations and genetic rearrangements
5.3.1 Screening assays for EFGR
5.3.2 Targeted assays
5.4 Molecular methods used to detect mutations
5.4.1 Fluorescence in situ hybridization (FISH)
5.4.2 Immunohistochemistry (IHC)
5.4.3 Reverse transcriptase polymerase reaction (RTPCR)
5.5 Genomic markers
5.5.1 EFGR-TKI
5.5.2 Analplastic lymphoma kinase
5.5.3 KRAS
5.5.4 Antibody mediated treatment
5.6 Proteomic markers
5.7 Metabolic markers
5.7.1 Metabolomics
5.7.2 Metabolomic techniques used to analyze metabolites
5.7.3 Lipidomics
5.7.4 Classification of diagnostic metabolic biomarkers
5.7.5 Body fluids based metabolic biomarkers
5.7.6 Alterations of metabolites in healthy individuals versus cancer patients
5.7.7 Metabolite alterations in lung cancer stages and their significance as disease progression indicators and overall survival
5.8 Immunotherapy markers
5.8.1 Immunotherapy in NSCLC and SCLC
5.8.2 PD-L1
5.8.3 Immune checkpoint inhibitors
5.8.4 Circulating tumor DNA
5.8.5 Tumor mutational burden (TMB)
5.8.6 Immunotherapy side effects
5.9 The emerging role of microRNAs
5.10 Clinical trials with targetable oncogenic drivers
5.11 Conclusion
References
CH006.pdf
Chapter 6 Radiomics and lung cancer: promising news for early detection of nodules
6.1 Introduction
6.2 Interpretation of small lung nodules
6.2.1 Incidentally detected nodules
6.2.2 CT screening and Lung-RADS
6.3 Computer-aided detection/diagnosis (CAD)
6.4 Radiomics
6.4.1 What radiomics is
6.4.2 Radiomics models and lung nodules
6.5 Conclusion
References
CH007.pdf
Chapter 7 Photodynamic diagnosis and treatment of lung cancer
7.1 Introduction
7.2 Cancer
7.2.1 Lung cancer
7.2.2 Diagnosis and staging of lung cancer
7.2.3 Conventional treatments used in lung cancer
7.3 Photodynamic diagnosis
7.3.1 Photodynamic diagnosis
7.3.2 Photosensitizers used in PDD
7.3.3 Photodynamic diagnosis and lung cancer
7.4 Photodynamic therapy
7.4.1 Photodynamic therapy
7.4.2 Photosensitizers used in PDT
7.4.3 Photodynamic therapy and lung cancer
7.5 Conclusion
Acknowledgements
References
CH008.pdf
Chapter 8 Cold atmospheric plasma and iron oxide based magnetic nanoparticles for synergetic lung cancer therapy
8.1 Introduction
8.2 Therapeutic effect of cold atmospheric plasma in lung cancer
8.2.1 Cold atmospheric plasma device structure, methods, and carrier gas types
8.2.2 The mechanism of cold atmospheric plasma treatment of lung cancer
8.2.3 The impact of cold atmospheric plasma on EMT
8.3 The therapeutic effect of magnetic iron oxide nanoparticles in lung cancer
8.3.1 Advantages of magnetic iron oxide nanoparticles in the treatment of lung cancer
8.3.2 The mechanism of magnetic iron oxide nanoparticle treatment of lung cancer
8.3.3 Magnetic iron oxide nanoparticles for the detection of lung cancer in magnetic resonance imaging
8.4 Synergistic therapeutic effects of cold atmospheric plasma and magnetic iron oxide nanoparticles in lung cancer
8.4.1 Limitations of cold atmospheric plasma
8.4.2 Cold atmospheric plasma science and nanotechnology
8.4.3 Synergies between plasma and nanoparticles in the treatment of lung cancer
8.5 The synergistic therapeutic effect of cold atmospheric plasma and drug-loaded magnetic nanoparticles in lung cancer
8.5.1 Methods for preparing magnetic nanoparticles loaded with drugs
8.5.2 Synergistic effect between cold atmospheric plasma and magnetic nanoparticles loaded with drugs in the treatment of lung cancer
8.6 Conclusions
References
CH009.pdf
Chapter 9 Exploiting exhaled aerosol fingerprints to detect lung cancers and obstructive respiratory diseases
9.1 Introduction
9.2 Methods and materials
9.2.1 Healthy and diseased airway models
9.2.2 Acquisition of exhaled aerosol images
9.2.3 Feature extraction of exhaled aerosol images
9.2.4 Image classification of exhaled aerosol images
9.3 Results
9.3.1 AFP differences in healthy and diseased airways
9.3.2 Growing bronchial tumor
9.3.3 Asthmatic bronchioles in small airways
9.3.4 Dynamic mode decomposition (DMD) to catch disease growth
9.4 Discussion
9.5 Conclusion
References
CH010.pdf
Chapter 10 A study of ground-glass opacity (GGO) nodules in the automated detection of lung cancer
10.1 Introduction
10.2 Ground-glass opacity (GGO) nodules
10.2.1 Clinical and radiological characteristics of GGO nodules
10.2.2 The importance of GGO nodules: the correlation between malignancy and GGO nodules
10.2.3 Image analysis of GGO nodules from CT scans
10.3 Computer-aided detection of GGO nodules
10.3.1 Challenges of GGOs in automated detection
10.4 Different ways to handle GGOs in automated detection
10.4.1 Markov random field-based GGO segmentation
10.4.2 Template matching based GGO segmentation
10.4.3 Multi-level learning-based GGO segmentation
10.4.4 Deep learning-based GGO segmentation
10.5 Conclusion
References
CH011.pdf
Chapter 11 Electromagnetic imaging and lung ablation
11.1 Introduction
11.2 Electrical impedance tomography
11.2.1 Working principle
11.2.2 Imaging methods
11.2.3 Measurement system
11.2.4 Clinical trails
11.3 Magnetic induction tomography
11.3.1 Working principle
11.3.2 Measurement system
11.3.3 HMI imaging algorithm
11.4 Microwave imaging
11.4.1 Imaging methods
11.4.2 Measurement systems
11.5 Lung ablation
11.6 Current trends and future perspectives
11.7 Conclusion
Acknowledgments
References