Artificial Intelligence in Cancer Diagnosis and Prognosis, Volume 1: Lung and kidney cancer

250 Pages·2022·40.955 MB·English

by  Ayman El-Baz, Jasjit S. Suri

Artificial Intelligence in Cancer
Diagnosis and Prognosis,
Volume 1
Lung and kidney cancer
Online at: https://doi.org/10.1088/978-0-7503-3595-9
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 is focused 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-ionising 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 1
Lung and kidney 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
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem
ortransmittedinanyformorbyanymeans,electronic,mechanical,photocopying,recording
orotherwise,withoutthepriorpermissionofthepublisher,orasexpresslypermittedbylawor
undertermsagreedwiththeappropriaterightsorganization.Multiplecopyingispermittedin
accordancewiththetermsoflicencesissuedbytheCopyrightLicensingAgency,theCopyright
ClearanceCentreandotherreproductionrightsorganizations.
PermissiontomakeuseofIOPPublishingcontentotherthanassetoutabovemaybesought
[email protected].
AymanEl-BazandJasjitSSurihaveassertedtheirrighttobeidentifiedastheeditorsofthiswork
inaccordancewithsections77and78oftheCopyright,DesignsandPatentsAct1988.
ISBN 978-0-7503-3595-9(ebook)
ISBN 978-0-7503-3593-5(print)
ISBN 978-0-7503-3596-6(myPrint)
ISBN 978-0-7503-3594-2(mobi)
DOI 10.1088/978-0-7503-3595-9
Version:20221001
IOPebooks
BritishLibraryCataloguing-in-PublicationData:Acataloguerecordforthisbookisavailable
fromtheBritishLibrary.
PublishedbyIOPPublishing,whollyownedbyTheInstituteofPhysics,London
IOPPublishing,No.2TheDistillery,Glassfields,AvonStreet,Bristol,BS20GR,UK
USOffice:IOPPublishing,Inc.,190NorthIndependenceMallWest,Suite601,Philadelphia,
PA19106,USA
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 American Joint Committee on Cancer staging of lung and renal 1-1
cancers using a recurrent deep neural network model
Dipanjan Moitra
1.1 Introduction 1-1
1.2 Background 1-2
1.2.1 Lung cancer 1-2
1.2.2 Renal cancer 1-3
1.2.3 Research scope 1-3
1.3 Methodology 1-4
1.3.1 AJCC staging 1-4
1.3.2 Database 1-5
1.3.3 The deep learning model 1-7
1.4 The experiment 1-9
1.5 Results and discussion 1-14
1.6 Conclusions 1-18
References 1-18
2 Neural-ensemble-based detection: a modern way to diagnose 2-1
lung cancer
Sharayu Govardhane, Sahil Gandhi and Pravin Shende
2.1 Introduction 2-2
2.1.1 Lung cancer epidemiology 2-2
2.1.2 Signs and symptoms of lung cancer 2-2
2.1.3 Staging of lung cancer 2-3
2.1.4 Classification of lung cancer 2-3
2.2 Different methods of lung cancer detection 2-4
2.2.1 Invasive methods 2-5
2.2.2 Non-invasive methods 2-8
vii
ArtificialIntelligenceinCancerDiagnosisandPrognosis,Volume1
2.3 Neural-ensemble-based detection 2-10
2.4 Conclusions 2-14
References and further reading 2-14
3 Computed tomography and magnetic resonance imaging 3-1
machine learning applications for renal cell carcinoma
Elvira Guerriero, Arnaldo Stanzione, Lorenzo Ugga and Renato Cuocolo
3.1 Background 3-1
3.2 Applications 3-5
3.2.1 Malignant versus benign discrimination 3-5
3.2.2 Malignancy subtyping 3-8
3.2.3 Biologic aggressiveness 3-10
3.2.4 Correlation with overall and progression-free survival 3-12
under treatment
3.2.5 Prediction of perioperative complications 3-14
3.3 Conclusions 3-15
References 3-15
4 Pulmonary nodule-based feature learning for automated lung 4-1
tumor grading using convolutional neural networks
Supriya Suresh and Subaji Mohan
4.1 Introduction 4-2
4.2 Literature review 4-3
4.2.1 Preprocessing 4-4
4.2.2 Candidate nodule segmentation 4-4
4.2.3 Feature extraction and classification 4-5
4.3 Methodology 4-7
4.3.1 Data acquisition 4-8
4.3.2 Preprocessing 4-8
4.3.3 NROI segmentation 4-10
4.3.4 GAN 4-10
4.3.5 Feature extraction 4-12
4.3.6 Classification 4-15
4.4 Results and discussion 4-16
4.5 Conclusions 4-30
References 4-30
viii
ArtificialIntelligenceinCancerDiagnosisandPrognosis,Volume1
5 Detection of lung contours using closed principal curves and 5-1
machine learning
Tao Peng, Yihuai Wang, Thomas Canhao Xu, Lianmin Shi,
Jianwu Jiang and Shilang Zhu
5.1 Introduction 5-2
5.2 Materials and methods 5-3
5.2.1 Principal curve 5-4
5.2.2 Machine learning 5-5
5.2.3 Proposed algorithm 5-6
5.2.4 Quantitative evaluation 5-9
5.3 Results and discussion 5-9
5.3.1 Detecting contours in the private dataset using different 5-10
learning rates
5.3.2 Detecting contours in the private dataset using different 5-12
numbers of neurons in the hidden layer
5.3.3 Detecting contours in the private dataset using different 5-12
numbers of epochs
5.3.4 Detecting contours in the private dataset using different 5-16
algorithms
5.3.5 Detecting contours in the public LIDC–IDRI dataset using 5-18
different algorithms
5.4 Conclusions 5-19
Acknowledgments 5-20
References 5-20
6 Bytes, pixels, and bases: machine learning in imaging–omics 6-1
for renal cell carcinoma
Ruchi Chauhan, C V Jawahar and P K Vinod
6.1 Introduction 6-1
6.1.1 The convergence of computers and cancer care 6-3
6.2 Imaging in renal cell carcinoma 6-5
6.2.1 Radiology 6-5
6.2.2 Pathology 6-6
6.3 Omics in renal cell carcinoma 6-7
6.3.1 Multiomics 6-9
6.4 Imaging–omics for kidney carcinoma 6-9
6.4.1 Radiomics 6-10
6.4.2 Pathomics 6-11
ix