Table Of ContentA Guide to Outcome Modeling
In Radiotherapy and Oncology
Listening to the Data
Series in Medical Physics and Biomedical Engineering
Series Editors: John G. Webster, E. Russell Ritenour, Slavik Tabakov,
and Kwan-Hoong Ng
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Series in Medical Physics and Biomedical Engineering
A Guide to Outcome Modeling
In Radiotherapy and Oncology
Listening to the Data
Issam El Naqa
Associate Professor, University of Michigan
Ann Arbor, Michigan
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Library of Congress Cataloging-in-Publication Data
Names: El Naqa, Issam, editor.
Title: A guide to outcome modeling in radiotherapy and oncology : listening
to the data / edited by Issam El Naqa.
Other titles: Series in medical physics and biomedical engineering.
Description: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018] |
Series: Series in medical physics and biomedical engineering | Includes
bibliographical references and index.
Identifiers: LCCN 2017036390 | ISBN 9781498768054 (hardback ; alk. paper) |
ISBN 1498768059 (hardback ; alk. paper) | ISBN 9781498768061 (e-book) |
ISBN 1498768067 (e-book)
Subjects: LCSH: Cancer--Radiotherapy--Data processing. | Oncology--Data
processing.
Classification: LCC RC271.R3 G85 2018 | DDC 616.99/40642--dc23
LC record available at https://lccn.loc.gov/2017036390
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To my parents (Rola & Mustafa), brothers (Rami & Rabie),
my wife Rana and daughters (Layla & Lamees).
Taylor & Francis
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Contents
Section I Multiple Sources of Data
hapter
C 1(cid:2) Introduction to data sources and outcome models 3
IssamElNaqaandRandallK.TenHaken
1.1 INTRODUCTIONTOOUTCOMEMODELING 4
1.2 MODELDEFINITION 4
1.3 TYPESOFOUTCOMEMODELS 6
1.3.1 Prognostic versus predictive models 6
1.3.2 Top-down versus bottom-up models 6
1.3.3 Analytical versus data-driven models 7
1.4 TYPESOFDATAUSEDINOUTCOMEMODELS 9
1.5 THEFIVESTEPSTOWARDSBUILDINGANOUTCOMEMODEL 9
1.6 CONCLUSIONS 11
hapter
C 2(cid:2) Clinical data in outcome models 13
NicholasJ.DeNunzio,MD,PhD,SarahL.Kerns,PhD,andMichaelTMilano,MD,PhD
2.1 INTRODUCTION 14
2.2 COLLAGENVASCULARDISEASE 16
2.3 GENETICSTUDIES 17
2.4 BIOLOGICALFACTORSIMPACTINGTOXICITYAFTERSBRT 19
2.4.1 Chest wall toxicity after SBRT 20
2.4.2 Radiation-induced lung toxicity (RILT) after SBRT 20
2.4.3 Radiation-induced liver damage (RILD) after SBRT 22
2.5 BIGDATA 23
2.6 CONCLUSIONS 23
hapter
C 3(cid:2) Imaging data (radiomics) 25
IssamElNaqa
3.1 INTRODUCTION 25
3.2 IMAGEFEATURESEXTRACTION 26
3.2.1 Static image features 26
3.2.2 Dynamic image features 27
3.3 RADIOMICSEXAMPLESFROMDIFFERENTCANCERSITES 28
3.3.1 Predicting local control in lung cancer using PET/CT 28
vii
viii (cid:2) Contents
3.3.2 Predicting distant metastasis in sarcoma using PET/MR 30
3.4 CONCLUSIONS 31
hapter
C 4(cid:2) Dosimetric data 33
IssamElNaqaandRandallK.TenHaken
4.1 INTRODUCTION 34
4.2 DOSEVOLUMEMETRICS 35
4.3 EQUIVALENTUNIFORMDOSE 36
4.4 DOSIMETRICMODELVARIABLESELECTION 38
4.4.1 Model order based on information theory 38
4.4.2 Model order based on resampling methods 38
4.5 ADOSIMETRICMODELINGEXAMPLE 39
4.5.1 Data set 39
4.5.2 Data exploration 40
4.5.3 Multivariate modeling with logistic regression 41
4.5.4 Multivariate modeling with machine learning 42
4.5.5 Comparison with other known models 43
4.6 SOFTWARETOOLSFORDOSIMETRICOUTCOMEMODELING 44
4.7 CONCLUSIONS 45
hapter
C 5(cid:2) Pre-clinical radiobiological insights to inform modelling of
radiotherapy outcome 47
PetervanLuijk,PhDandRobertP.Coppes,PhD
5.1 VARIABILITY IN RESPONSE TO HIGHLY STANDARDIZED RADIO-
THERAPY 48
5.2 VARIATIONINSENSITIVITYTORADIATION 49
5.3 UNDERSTANDINGDOSE-RESPONSEOFTISSUESANDORGANS 50
5.4 ANIMALMODELSTOSTUDYRADIATIONRESPONSE 50
5.5 PROCESSESGOVERNINGOUTCOME 51
5.6 PATIENT-INDIVIDUALFACTORS/CO-MORBIDITY 52
5.7 USEINMODELS 52
5.8 CONCLUSION 52
hapter
C 6(cid:2) Radiogenomics 53
IssamElNaqa,SarahL.Kerns,PhD,JamesCoates,PhD,YiLuo,PhD,CoreySpeers,
MDPhD,RandallK.TenHaken,CatharineM.L.West,PhD,andBarryS.Rosenstein,PhD
6.1 INTRODUCTION 54
6.2 BIOMARKERSANDTHEWORLDOF“-OMICS” 54
6.2.1 Structural variations 56
6.2.1.1 Single nucleotide polymorphisms (SNPs) 56
6.2.1.2 Copy number variations (CNVs) 56
6.2.2 Gene expression: mRNA, miRNA, lncRNA 57
Contents (cid:2) ix
6.2.3 Protein expression 57
6.2.4 Metabolites 59
6.3 RESOURCESFORBIOLOGICALDATA 59
6.4 EXAMPLESOFRADIOGENOMICMODELING 60
6.4.1 Prostate cancer 60
6.4.2 Breast cancer 61
6.4.3 Lung cancer 61
6.5 CONCLUSIONS 62
Section II Top-down Modeling Approaches
hapter
C 7(cid:2) Analytical and mechanistic modeling 65
VitaliMoiseenko,PhD,JimmGrimm,PhD,JamesD.Murphy,PhD,DavidJ.Carlson,
PhD,andIssamElNaqa,PhD
7.1 INTRODUCTION 66
7.2 TRACKSTRUCTUREANDDNADAMAGE 67
7.3 LINEAR-QUADRATICMODEL 69
7.4 KINETICREACTIONRATEMODELS 73
7.4.1 Repair-misrepair and lethal-potentially-lethal models 73
7.4.2 Refined models 74
7.4.3 The Giant LOop Binary LEsion (GLOBE) 75
7.4.4 Local Effect Model (LEM) 75
7.4.5 Microdosimetric-kinetic model (MKM) 76
7.4.6 The Repair-misrepair-fixation model 76
7.5 MECHANISTIC MODELING OF STEREOTACTIC RADIOSURGERY
(SRS)ANDSTEREOTACTICBODYRADIOTHERAPY(SBRT) 77
7.5.1 LQ limitations and alternative models 79
7.6 INCORPORATING BIOLOGICAL DATA TO DESCRIBE AND PREDICT
BIOLOGICALRESPONSE 81
7.7 CONCLUSIONS 82
hapter
C 8(cid:2) Data driven approaches I: conventional statistical inference
methods, including linear and logistic regression 85
TizianaRancatiandClaudioFiorino
8.1 WHATISAREGRESSION 86
8.2 LINEARREGRESSION 87
8.2.1 Mathematical formalism 88
8.2.2 Estimation of regression coefficients 88
8.2.3 Accuracy of coefficient estimates 89
8.2.4 Rejecting the null hypothesis 89
8.2.5 Accuracy of the model 90
8.2.6 Qualitative predictors 91
