Detection Systems in Lung Cancer and Imaging

PRELIMS.pdf
	Preface
	Acknowledgement
	Editor biographies
		Ayman El-Baz
		Jasjit S Suri
	List of contributors
CH001.pdf
	Chapter 1 Lung cancer classification using wavelet recurrent neural network
		1.1 Introduction
		1.2 Lung cancer and lung image
			1.2.1 Lung cancer
			1.2.2 Lung image
			1.2.3 Image processing
		1.3 Classification process
			1.3.1 Classification
			1.3.2 Features extraction
			1.3.3 Wavelet
			1.3.4 Machine learning
			1.3.5 Neural network
			1.3.6 Recurrent neural network
			1.3.7 Mean square error
			1.3.8 Sensitivity, specificity, and accuracy
		1.4 Dataset
		1.5 Modeling wavelet recurrent neural network for lung cancer nodule classification
			1.5.1 Image denoising using wavelet
			1.5.2 Wavelet recurrent neural network for lung cancer classification
		1.6 Results and discussion
		1.7 Conclusion
		References
CH002.pdf
	Chapter 2 Diagnosis of diffusion-weighted magnetic resonance imaging (DWI) for lung cancer
		2.1 Introduction
		2.2 Diagnosis of lung cancer and the pulmonary nodules and masses (figures 2.1–2.6, table 2.1)
		2.3 Diagnostic capability of nodal involvement in lung cancer (figures 2.7–2.9)
		2.4 Recurrence or metastasis from lung cancer (figure 2.11)
		2.5 Diagnosis of lung cancer by whole-body DWI
		2.6 Response evaluation to chemotherapy and/or radiotherapy (figure 2.11, table 2.2)
		2.7 ADC and pathology
		2.8 Medical cost of examinations
		2.9 Advantage and disadvantage of MRI
		2.10 Future plans
		2.11 Conclusion
		References
CH003.pdf
	Chapter 3 Computer assisted detection of low/high grade nodule from lung CT scan slices using handcrafted features
		3.1 Introduction
		3.2 Computer assisted detection system
			3.2.1 Image collection
			3.2.2 3D to 2D conversion
			3.2.3 Threshold filter implementation
			3.2.4 Nodule segmentation
			3.2.5 Feature extraction
			3.2.6 Feature selection
			3.2.7 Classifier implementation
			3.2.8 Validation of the CAD system
		3.3 Results and discussions
		3.4 Conclusion
		References
CH004.pdf
	Chapter 4 Computer-aided lung cancer screening in computed tomography: state-of the-art and future perspectives
		4.1 Introduction
			4.1.1 Computer-aided lung cancer screening
		4.2 Computer-aided lung nodule detection
		4.3 Computer-aided lung nodule segmentation
		4.4 Computer-aided lung nodule characterization
			4.4.1 Malignancy characterization
			4.4.2 Other nodule features
		4.5 Computer-aided lung cancer patient diagnosis/management
			4.5.1 Lung cancer patient diagnosis
			4.5.2 Patient follow-up recommendation
		4.6 Available datasets
			4.6.1 ANODE09 dataset
			4.6.2 Lung image database consortium image collection dataset
			4.6.3 Luna16 dataset
			4.6.4 National lung screening trial dataset
			4.6.5 Kaggle data Science Bowl 2017 dataset
			4.6.6 LNDB dataset
		4.7 Conclusion and future perspectives
		References
CH005.pdf
	Chapter 5 Radiation therapy in lung cancer treatment
		References
CH006.pdf
	Chapter 6 Application of visual sensing technology in lung cancer screening
		6.1 Introduction
		6.2 Section 1: detection of lung cancer-related markers in exhaled breath through the visual sensing technology
			6.2.1 Definition of VOCs in exhaled breath
			6.2.2 Production mechanism of lung cancer-related VOCs in exhaled breath
			6.2.3 Preparation method of sensor chip
			6.2.4 Collection of lung cancer-related VOCs in exhaled breath
			6.2.5 Analysis of VOC patterns
			6.2.6 Computational formulas of the relative standard deviation (RSD or %RSD) and concentrations of saturated vapor (Cs)
			6.2.7 Cross-response mechanism of the visual sensor
		6.3 Section 2: detection of clinical exhaled breath of lung cancer through the visual sensing technique
			6.3.1 Institutional review board approval
			6.3.2 Exhaled breath collection and analysis
			6.3.3 Basic data collection
			6.3.4 Statistical analysis of data
		6.4 Section 3: detection of metabolic volatile products of lung cancer cells through the visual sensing technique
		6.5 Section 4: development space of the visual sensing technology
		6.6 Section 5: important role and application prospect of visual sensing technology in lung cancer screening
		References
CH007.pdf
	Chapter 7 Precision molecular imaging can perhaps be enhanced for lung cancer management via integrated analysis of general parameters such as age, gender, genetics, and lifestyle11Correspondence: juneindia@gmail.com.
		7.1 Multiple molecular imaging modalities have been beneficial in the clinical management of various cancer types, including different types of lung cancer
		7.2 There have been constant efforts for development of new probes for molecular imaging
		7.3 Identifying novel biological molecules as targets for molecular imaging: research often starts with an exploration of gene expression patterns and mutations in the DNA, particularly those within the genes
		7.4 Combinatorial analysis of metadata on age, gender, lifestyle, etc, has not received enough attention even though this approach has great potential to enhance precision medicine in the area of molecular imaging
		7.5 Gender as a special case of a general parameter that has the potential to contribute to precision medicine in the area of molecular imaging
		7.6 Conclusion
		7.7 Funding and conflict of interest
		References
CH008.pdf
	Chapter 8 Computed tomography ventilation imaging in lung cancer: theory, validation and application
		8.1 Introduction
		8.2 Theory
			8.2.1 Overview
			8.2.2 Intensity metric
			8.2.3 Jacobian metric
			8.2.4 Specific gas volume
			8.2.5 Other metrics
			8.2.6 Discussion
		8.3 Validation
			8.3.1 Comparisons with global measures
			8.3.2 Comparisons with nuclear medicine imaging
			8.3.3 Comparisons with xenon-enhanced CT
			8.3.4 Comparisons with hyperpolarised gas MRI
			8.3.5 Reproducibility
		8.4 Clinical applications in lung cancer
			8.4.1 Functional lung avoidance radiotherapy treatment planning
			8.4.2 Post-radiotherapy functional lung assessment
		8.5 Discussion and future research directions
			8.5.1 Multi-institutional validation studies
			8.5.2 Alveolar ventilation versus lung expansion
			8.5.3 Ventilation versus perfusion
		8.6 Conclusion
		References
CH009.pdf
	Chapter 9 Novel non-invasive methods used in the early detection of lung cancer: from biomarkers to nanosystems
		9.1 Introduction
		9.2 Diagnostic methods for lung cancer
			9.2.1 Invasive diagnostic methods
			9.2.2 Non-invasive diagnostic methods
		9.3 Conclusion
		References
CH010.pdf
	Chapter 10 Heat shock proteins as biomarkers for early-stage diagnosis of lung cancer
		10.1 Introduction
			10.1.1 Risk factors and symptoms associated with lung cancer
		10.2 Conventional diagnostic methods of lung cancer
			10.2.1 Chest radiography
			10.2.2 CT scan
			10.2.3 MRI (magnetic resonance imaging)
			10.2.4 PET (positron emission tomography) scan
		10.3 Biomarkers as tools for early cancer detection
			10.3.1 The chaperoning activity of HSPs
			10.3.2 Role of HSPs in cancer
			10.3.3 HSPs as biomarkers for lung cancer
		10.4 Conclusion
		Declarations of interest: none
		Acknowledgments
		References