Artificial Intelligence in Cancer Diagnosis and Prognosis, Volume 3: Brain and prostate cancer

304 Pages·2022·34.451 MB·English

by  Ayman El-Baz, Jasjit S. Suri

Artificial Intelligence in
Cancer Diagnosis and Prognosis,
Volume 3
Brain and prostate cancer
Online at: https://doi.org/10.1088/978-0-7503-3603-1
IPEM–IOP Series in Physics and Engineering in Medicine and Biology
Editorial Advisory Board Members
Frank Verhaegen Kwan Hoong Ng
Maastro Clinic, The Netherlands University of Malaya, Malaysia
Carmel Caruana John Hossack
University of Malta, Malta University of Virginia, USA
Penelope Allisy-Roberts Tingting Zhu
formerly of BIPM, Sèvres, France University of Oxford, UK
Rory Cooper Dennis Schaart
University of Pittsburgh, PA, USA TU Delft, The Netherlands
Alicia El Haj Indra J Das
University of Birmingham, UK NorthwesternUniversityFeinbergSchool
of Medicine, USA
About the Series
TheSeriesinPhysicsandEngineeringinMedicineandBiologywillallowtheInstitute
of Physics and Engineering in Medicine (IPEM) to enhance its mission to ‘advance
physics and engineering applied to medicine and biology for the public good’.
It focuses on key areas, including, but not limited to:
(cid:129) clinical engineering
(cid:129) diagnostic radiology
(cid:129) informatics and computing
(cid:129) magnetic resonance imaging
(cid:129) nuclear medicine
(cid:129) physiological measurement
(cid:129) radiation protection
(cid:129) radiotherapy
(cid:129) rehabilitation engineering
(cid:129) ultrasound and non-ionizing radiation.
A number of IPEM–IOP titles are being published as part of the EUTEMPE
Network Series for Medical Physics Experts.
A full list of titles published in this series can be found here: https://iopscience.iop.
org/bookListInfo/physics-engineering-medicine-biology-series.
Artificial Intelligence in
Cancer Diagnosis and Prognosis,
Volume 3
Brain and prostate cancer
Edited by
Ayman El-Baz
University of Louisville, Louisville, KY, USA
and
University of Louisville at AlAlamein International University (UofL-AIU),
New Alamein City, Egypt
Jasjit S Suri
AtheroPoint LLC, Roseville, CA, USA
IOP Publishing, Bristol, UK
ªIOPPublishingLtd2022
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[email protected].
AymanEl-BazandJasjitSSurihaveassertedtheirrighttobeidentifiedastheeditorsofthiswork
inaccordancewithsections77and78oftheCopyright,DesignsandPatentsAct1988.
ISBN 978-0-7503-3603-1(ebook)
ISBN 978-0-7503-3601-7(print)
ISBN 978-0-7503-3604-8(myPrint)
ISBN 978-0-7503-3602-4(mobi)
DOI 10.1088/978-0-7503-3603-1
Version:20221001
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Withloveandaffectiontomymotherandfather,whoselovingspiritsustainsmestill.
—Ayman El-Baz
To my late loving parents, immediate family, and children.
—Jasjit S Suri

Contents
Preface xiii
Acknowledgements xiv
Editor biographies xv
List of contributors xvi
1 Artificial Intelligence in prostate cancer treatment with 1-1
image-guided radiation therapy
Yading Yuan, Ren-Dih Sheu, Tzu-Chi Tseng, James Tam,
Yeh-Chi Lo and Richard Stock
1.1 Introduction 1-1
1.1.1 External radiation therapy for prostate cancer 1-2
1.1.2 Brachytherapy for prostate cancer: radioactive seed implants 1-4
1.2 Deep contouring: automated multiple organ segmentation using 1-7
dilated U-Net with generalized Jaccard distance
1.2.1 Introduction 1-7
1.2.2 Methodology 1-9
1.2.3 Experiments 1-12
1.2.4 Summary 1-15
1.3 Deep planning: fully 3D-knowledge-based treatment planning 1-16
1.3.1 Introduction 1-16
1.3.2 Methodology 1-18
1.3.3 Experiments 1-21
1.3.4 Summary 1-23
1.4 Conclusions 1-25
References 1-26
2 Artificial-intelligence-based diagnosis of brain tumor diseases 2-1
Samir Kumar Bandyopadhyay, Vishal Goyal and Shawni Dutta
2.1 Introduction 2-1
2.2 Related works 2-2
2.3 Current methods used to collect images 2-6
2.3.1 Ultrasound (USG) 2-7
2.3.2 Projection radiography (x-rays) 2-7
2.3.3 Computed tomography 2-7
2.3.4 Magnetic resonance imaging 2-7
2.3.5 Positron emission tomography 2-7
vii
ArtificialIntelligenceinCancerDiagnosisandPrognosis,Volume3
2.4 Background 2-8
2.4.1 Artificial intelligence and machine learning 2-8
2.4.2 Performance evaluation metrics 2-9
2.5 Datasets of brain tumors 2-9
2.6 Proposed methodologies for disease detection 2-10
2.6.1 Brain tumor detection methodology 2-10
2.7 Experimental results 2-11
2.8 Conclusions 2-13
References 2-13
3 Multisite brain tumor segmentation using a unified generative 3-1
adversarial network
Jia Wei, Zecheng Liu, Wenguang Yuan and Rui Li
3.1 Introduction 3-1
3.2 UGAN 3-3
3.2.1 Method overview 3-3
3.2.2 Loss function 3-4
3.3 Experiments 3-5
3.3.1 Datasets 3-5
3.3.2 Training settings 3-5
3.3.3 Segmentation performances 3-6
3.4 Conclusions 3-8
References and further reading 3-8
4 Role of artificial intelligence in automatic segmentation of 4-1
brain metastases for radiotherapy
Prabhakar Ramachandran, Venkatakrishnan Seshadri, Ben Perrett,
Akash Mehta, Davide Fontanarosa, Mark Pinkham and Matthew Foote
4.1 Introduction 4-1
4.1.1 Brain metastasis treatment options 4-2
4.2 Manual segmentation of tumors 4-4
4.2.1 Limitations of manual segmentation 4-5
4.3 Automatic segmentation 4-5
4.3.1 Automatic segmentation techniques 4-8
4.3.2 U-Net 4-11
4.3.3 Identification of small lesions 4-13
4.3.4 Post-treatment volumetric assessment 4-15
viii
ArtificialIntelligenceinCancerDiagnosisandPrognosis,Volume3
4.3.5 Post-treatment response prediction 4-17
4.3.6 Post-treatment radionecrosis 4-17
4.4 Summary 4-18
References and further reading 4-18
5 Applications of artificial intelligence in the fields of brain and 5-1
prostate cancer
Ayturk Keles and Ali Keles
Abbreviations 5-1
5.1 Introduction 5-3
5.2 AI applications in brain cancer 5-4
5.2.1 Brain tumor segmentation 5-13
5.2.2 Survival prognosis 5-14
5.2.3 Surgical performance 5-15
5.3 AI applications in prostate cancer 5-16
5.3.1 Analyzing histopathological images 5-17
5.3.2 PCa segmentation 5-18
5.3.3 Robotic surgery 5-18
5.3.4 PCa treatment 5-19
5.4 Conclusions 5-19
Acknowledgments 5-20
References 5-20
6 AI-based non-deep learning and deep learning techniques 6-1
used to accurately predict prostate cancer
Lal Hussain and Adeel Ahmed Abbasi
6.1 Introduction 6-1
6.2 Study data 6-3
6.2.1 Dataset 6-3
6.3 AI-based non-deep-learning prediction methods 6-3
6.3.1 Handcrafted features 6-3
6.3.2 Classification algorithms 6-8
6.4 AI-based deep learning prediction methods 6-12
6.4.1 Convolutional neural network (CNN) overview 6-12
6.4.2 CNN methods 6-14
6.4.3 CNN layers 6-17
6.4.4 Training/testing data formulation 6-20
6.4.5 Performance evaluation measures 6-20
ix