Within this third volume dealing with breast and bladder cancer, the editors and authors will detail the latest research related to the application of AI to cancer diagnosis and prognosis and summarize its advantages. It's the editors and authors intention to explore how AI assists in these activities, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. Ways will also be demonstrated as to how these methods in AI are advancing the field.
There have been thousands of papers written between 1995 and 2019 related to AI for cancer diagnosis and prognosis. However, to this date (and unknown to the Editors) there has not yet been published a comprehensive overview of the latest findings pertaining to these AI technologies, within a single book project(s). Therefore, the purpose of this three volume work and particularly for this third volume dealing with brain and bladder cancer, is to present a compendium of these findings related to these two pervasive cancers. Within this coverage it's our hope that scientists, researchers and clinicians can successfully incorporate these techniques into other significant cancers such as pancreatic, esophageal, leukemia, melanoma, etc.
Author(s): Ayman El-Baz, Jasjit S. Suri
Series: IPEM–IOP Series in Physics and Engineering in Medicine and Biology
Publisher: IOP Publishing
Year: 2022
Language: English
Pages: 303
City: Boca Raton
PRELIMS.pdf
Preface
Acknowledgements
Editor biographies
Ayman El-Baz
Jasjit S Suri
List of contributors
Outline placeholder
Adeel Ahmed Abbasi
Nahla B Abdel-Hamid
H Arafat Ali
Sarah M Ayyad
Samir Kumar Bandyopadhyay
Gustavo M Callico
Daniel U Campos-Delgado
Inés Alejandro Cruz-Guerrero
Dimitrios E Diamantis
Shawni Dutta
Mohamed Abou El-Ghar
Moumen El-Melegy
Himar Fabelo
Davide Fontanarosa
Matthew Foote
Mohamed Ghazal
Preetam Ghosh
Vishal Goyalis
Cheng-Yeh Hsieh
Lal Hussain
Jiwoong Jason Jeong
Rishabh Kapoor
Ali Keles
Ayturk Keles
Labib M Labib
Rui Li
Tian Liu
Zecheng Liu
Chung-Ming Lo
Yeh-Chi Lo
Ali Mahmoud
Hui Mao
Aldo Rodrigo Mejia-Rodríguez
Akash Mehta
Serafeim Moustakidis
Charis Ntakolia
Samuel Ortega
Jatinder R Palta
Elpiniki I Papageorgiou
Nikolaos Papandrianos
Ben Perrett
Mark Pinkham
Prabhakar Ramachandran
Venkatakrishnan Seshadri
Ahmed Shalaby
Mohamed Shehata
Ren-Dih Sheu
William C Sleeman IV
Sriram Srinivasan
Richard Stock
James Tam
Zhen Tian
Tzu-Chi Tseng
Jia Wei
Lei Yang
Xiaofeng Yang
Wenguang Yuan
Yading Yuan
CH001.pdf
Chapter 1 Artificial Intelligence in prostate cancer treatment with image-guided radiation therapy
1.1 Introduction
1.1.1 External radiation therapy for prostate cancer
1.1.2 Brachytherapy for prostate cancer: radioactive seed implants
1.2 Deep contouring: automated multiple organ segmentation using dilated U-Net with generalized Jaccard distance
1.2.1 Introduction
1.2.2 Methodology
1.2.3 Experiments
1.2.4 Summary
1.3 Deep planning: fully 3D-knowledge-based treatment planning
1.3.1 Introduction
1.3.2 Methodology
1.3.3 Experiments
1.3.4 Summary
1.4 Conclusions
References
CH002.pdf
Chapter 2 Artificial-intelligence-based diagnosis of brain tumor diseases
2.1 Introduction
2.2 Related works
2.3 Current methods used to collect images
2.3.1 Ultrasound (USG)
2.3.2 Projection radiography (x-rays)
2.3.3 Computed tomography
2.3.4 Magnetic resonance imaging
2.3.5 Positron emission tomography
2.4 Background
2.4.1 Artificial intelligence and machine learning
2.4.2 Performance evaluation metrics
2.5 Datasets of brain tumors
2.6 Proposed methodologies for disease detection
2.6.1 Brain tumor detection methodology
2.7 Experimental results
2.8 Conclusions
References
CH003.pdf
Chapter 3 Multisite brain tumor segmentation using a unified generative adversarial network
3.1 Introduction
3.2 UGAN
3.2.1 Method overview
3.2.2 Loss function
3.3 Experiments
3.3.1 Datasets
3.3.2 Training settings
3.3.3 Segmentation performances
3.4 Conclusions
References and further reading
CH004.pdf
Chapter 4 Role of artificial intelligence in automatic segmentation of brain metastases for radiotherapy
4.1 Introduction
4.1.1 Brain metastasis treatment options
4.2 Manual segmentation of tumors
4.2.1 Limitations of manual segmentation
4.3 Automatic segmentation
4.3.1 Automatic segmentation techniques
4.3.2 U-Net
4.3.3 Identification of small lesions
4.3.4 Post-treatment volumetric assessment
4.3.5 Post-treatment response prediction
4.3.6 Post-treatment radionecrosis
4.4 Summary
References and further reading
CH005.pdf
Chapter 5 Applications of artificial intelligence in the fields of brain and prostate cancer
Abbreviations
5.1 Introduction
5.2 AI applications in brain cancer
5.2.1 Brain tumor segmentation
5.2.2 Survival prognosis
5.2.3 Surgical performance
5.3 AI applications in prostate cancer
5.3.1 Analyzing histopathological images
5.3.2 PCa segmentation
5.3.3 Robotic surgery
5.3.4 PCa treatment
5.4 Conclusions
Acknowledgments
References
CH006.pdf
Chapter 6 AI-based non-deep learning and deep learning techniques used to accurately predict prostate cancer
6.1 Introduction
6.2 Study data
6.2.1 Dataset
6.3 AI-based non-deep-learning prediction methods
6.3.1 Handcrafted features
6.3.2 Classification algorithms
6.4 AI-based deep learning prediction methods
6.4.1 Convolutional neural network (CNN) overview
6.4.2 CNN methods
6.4.3 CNN layers
6.4.4 Training/testing data formulation
6.4.5 Performance evaluation measures
6.4.6 Receiver operating characteristic curve
6.5 Results and discussion
6.6 Conclusions and future recommendations
References
CH007.pdf
Chapter 7 Intelligent brain tumor classification using deep convolutional neural networks with transfer learning
7.1 Introduction
7.2 Materials and methods
7.2.1 MR images
7.2.2 Image analysis
7.2.3 Transfer learning
7.2.4 Data augmentation
7.2.5 Results
7.2.6 Discussion
7.3 Conclusions
References
CH008.pdf
Chapter 8 Big data applications in radiation oncology: challenges and opportunities
8.1 Introduction
8.2 Methods for structure set standardization
8.2.1 Overview
8.2.2 DICOM structure set standardization methods
8.2.3 Results
8.3 The use of natural language processing with medical texts
8.3.1 NLP feature extraction and models
8.3.2 NLP implementation results
8.3.3 Challenges for NLP in understanding free text
8.4 Standardization through structured templates
8.4.1 Manual data extraction
8.4.2 Analytic dashboard
8.4.3 Limitations of automated data extraction
8.4.4 Health Information Gateway Exchange (HINGE)
8.5 Future directions in data standardization and aggregation
8.5.1 Retrospective data
8.5.2 Transfer learning
8.5.3 Federated learning
8.6 Conclusions
References
CH009.pdf
Chapter 9 A hybrid approach to the hyperspectral classification of in vivo brain tissue: linear unmixing with spatial coherence and machine learning
9.1 Introduction
9.2 Intraoperative HS acquisition system and HS dataset
9.2.1 Data preprocessing
9.3 Processing framework based on linear unmixing with spatial coherence and machine learning
9.3.1 Abundances estimation
9.3.2 End-members estimation
9.3.3 Internal abundances estimation
9.3.4 Machine learning for classification
9.4 Hybrid classification methodology
9.5 Experimental results and discussion
9.5.1 Evaluation of the hybrid classification methodology
9.5.2 Comparison with other related works
9.5.3 Limitations
9.6 Conclusions
References
CH010.pdf
Chapter 10 Application and post-hoc explainability of deep convolutional neural networks for bone cancer metastasis classification in prostate patients
10.1 Introduction
10.2 Computer-aided diagnosis (CAD) system
10.2.1 Study population
10.2.2 Explainable deep learning pipeline for diagnosis
10.3 Results
10.3.1 Bone metastasis classification results
10.3.2 Post-hoc explainability results
10.4 Discussion
10.5 Conclusions
References
CH011.pdf
Chapter 11 Prostate cancer detection using histopathology image analysis
11.1 Introduction
11.2 Histopathological images
11.3 Handcrafted feature-based CAD
11.4 Deep learning-based CAD
11.5 Conclusions
Acknowledgments
References
CH012.pdf
Chapter 12 Machine learning of gliomas in 3D dynamic contrast enhanced MRI: automatic segmentation and classification
12.1 Introduction
12.2 Segmentation and classification methods
12.2.1 Segmentation method
12.2.2 Automatic classification system
12.3 Results
12.3.1 Segmentation results
12.3.2 Classification results
12.4 Discussion
12.4.1 Comparison of segmentation methods
12.4.2 Correlation thresholds and feature lists
12.4.3 Classification results using positive features
12.4.4 Significant radiomics features
12.4.5 Limitations
12.5 Conclusions
References
