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CONTRIBUTORS
EricO.Aboagye
ComprehensiveCancerImagingCentre,DepartmentofSurgery&Cancer,ImperialCollege
London,London,UnitedKingdom
SamuelAchilefu
DepartmentofRadiology,WashingtonUniversityinSt.Louis,St.Louis,Missouri,USA
BabakBehnamAzad
RussellH.MorganDepartmentofRadiologyandRadiologicalScience,JohnsHopkins
University,Baltimore,Maryland,USA
ZaverM.Bhujwalla
JHUICMICProgram,DivisionofCancerImagingResearch,TheRussellH.Morgan
DepartmentofRadiologyandRadiologicalScience,andSidneyKimmelComprehensive
CancerCenter,TheJohnsHopkinsUniversitySchoolofMedicine,Baltimore,Maryland,
USA
KannieW.Y.Chan
F.M.KirbyResearchCenterforFunctionalBrainImaging,KennedyKriegerInstitute;The
RussellH.MorganDepartmentofRadiologyandRadiologicalSciences,DivisionofMR
Research,andCellularImagingSectionandVascularBiologyProgram,InstituteforCell
Engineering,JohnsHopkinsUniversitySchoolofMedicine,Baltimore,Maryland,USA
SamitChatterjee
RussellH.MorganDepartmentofRadiologyandRadiologicalScience,JohnsHopkins
University,Baltimore,Maryland,USA
XiaoyuanChen
LaboratoryofMolecularImagingandNanomedicine,NationalInstituteofBiomedical
ImagingandBioengineering,NationalInstitutesofHealth,Bethesda,Maryland,USA
ZhihangChen
JHUICMICProgram,DivisionofCancerImagingResearch,TheRussellH.Morgan
DepartmentofRadiologyandRadiologicalScience,TheJohnsHopkinsUniversitySchool
ofMedicine,Baltimore,Maryland,USA
SwadeshK.Das
DepartmentofHumanandMolecularGenetics,andVCUInstituteofMolecularMedicine,
VirginiaCommonwealthUniversity,SchoolofMedicine,Richmond,Virginia,USA
SudeepDas
MolecularPharmacologyandChemistryProgram,MemorialSloan-KetteringCancer
Center,NewYork,NY
LuniEmdad
DepartmentofHumanandMolecularGenetics;VCUInstituteofMolecularMedicine,and
VCUMasseyCancerCenter,VirginiaCommonwealthUniversity,SchoolofMedicine,
Richmond,Virginia,USA
ix
x Contributors
PaulB.Fisher
DepartmentofHumanandMolecularGenetics;VCUInstituteofMolecularMedicine,and
VCUMasseyCancerCenter,VirginiaCommonwealthUniversity,SchoolofMedicine,
Richmond,Virginia,USA
SanjivS.Gambhir
DepartmentofBioengineering;MolecularImagingProgramatStanford,TheJamesHClark
Center,StanfordUniversity;DepartmentofRadiology,StanfordUniversitySchoolof
Medicine,andDepartmentofMaterialsScience&Engineering,StanfordUniversity,
Stanford,California,USA
ShengkuiGao
DepartmentofComputerScienceandEngineering,WashingtonUniversityinSt.Louis,
St.Louis,Missouri,USA
JanGrimm
MolecularPharmacologyandChemistryProgramandDepartmentofRadiology,Memorial
SloanKetteringCancerCenter,NewYork,NY
ViktorGruev
DepartmentofComputerScienceandEngineering,WashingtonUniversityinSt.Louis,
St.Louis,Missouri,USA
EdwardH.Herskovits
DepartmentofDiagnosticRadiologyandNuclearMedicine,UniversityofMaryland,
Baltimore,Maryland,USA
JiefuJin
JHUICMICProgram,DivisionofCancerImagingResearch,TheRussellH.Morgan
DepartmentofRadiologyandRadiologicalScience,TheJohnsHopkinsUniversitySchool
ofMedicine,Baltimore,Maryland,USA
LauraM.Kenny
ComprehensiveCancerImagingCentre,DepartmentofSurgery&Cancer,ImperialCollege
London,London,UnitedKingdom
BalajiKrishnamachary
JHUICMICProgram,DivisionofCancerImagingResearch,TheRussellH.Morgan
DepartmentofRadiologyandRadiologicalScience,TheJohnsHopkinsUniversitySchool
ofMedicine,Baltimore,Maryland,USA
DavidM.Kurtz
DivisionofOncology;DivisionofHematology,DepartmentofMedicine,StanfordCancer
Institute,andDepartmentofBioengineering,StanfordUniversity,Stanford,California,USA
RongguangLiang
CollegeofOpticalSciences,UniversityofArizona,Tucson,Arizona,USA
MichaelT.McMahon
F.M.KirbyResearchCenterforFunctionalBrainImaging,KennedyKriegerInstitute,and
TheRussellH.MorganDepartmentofRadiologyandRadiologicalSciences,Divisionof
MRResearch,JohnsHopkinsUniversitySchoolofMedicine,Baltimore,Maryland,USA
Contributors xi
MitchellE.Menezes
DepartmentofHumanandMolecularGenetics,Richmond,Virginia,USA
IlMinn
RussellH.MorganDepartmentofRadiologyandRadiologicalScience,JohnsHopkins
University,Baltimore,Maryland,USA
SumanB.Mondal
DepartmentofRadiology,andDepartmentofBiomedicalEngineering,Washington
UniversityinSt.Louis,St.Louis,Missouri,USA
SridharNimmagadda
RussellH.MorganDepartmentofRadiologyandRadiologicalScience,JohnsHopkins
University,Baltimore,Maryland,USA
Marie-FrancePenet
JHUICMICProgram,DivisionofCancerImagingResearch,TheRussellH.Morgan
DepartmentofRadiologyandRadiologicalScience,andSidneyKimmelComprehensive
CancerCenter,TheJohnsHopkinsUniversitySchoolofMedicine,Baltimore,Maryland,
USA
MartinG.Pomper
RussellH.MorganDepartmentofRadiologyandRadiologicalScience,andSidneyKimmel
ComprehensiveCancerCenter,JohnsHopkinsUniversity,Baltimore,Maryland,USA
DevanandSarkar
DepartmentofHumanandMolecularGenetics;VCUInstituteofMolecularMedicine,and
VCUMasseyCancerCenter,VirginiaCommonwealthUniversity,SchoolofMedicine,
Richmond,Virginia,USA
SiddikSarkar
DepartmentofHumanandMolecularGenetics,Richmond,Virginia,USA
AvinashSrivatsan
LaboratoryofMolecularImagingandNanomedicine,NationalInstituteofBiomedical
ImagingandBioengineering,NationalInstitutesofHealth,Bethesda,Maryland,USA
DanielL.J.Thorek
DivisionofNuclearMedicine,DepartmentofRadiologyandRadiologicalSciences,The
JohnsHopkinsSchoolofMedicine,Baltimore,MD,USA
KeerthiYarlagadda
RussellH.MorganDepartmentofRadiologyandRadiologicalScience,JohnsHopkins
University,Baltimore,Maryland,USA
NanZhu
CollegeofOpticalSciences,UniversityofArizona,Tucson,Arizona,USA
PREFACE
As molecular pathways in cancer succumb one by one to increasingly sen-
sitive methods of detection, we can begin to isolate the key drivers of
malignancy—with implications for management. Cancer researchers are
continually uncovering molecular subsets of what were until recently con-
sidered single pathologic entities. Because it is inherently noninvasive,
sensitive, and quantitative, molecular imaging enables measurement of
biochemistry within tissue and lends itself well to identification of disease
subsets and, by extension, precision medicine.
InthisvolumeofAdvancesinCancerResearch,westartbydiscussingquan-
titative radiology, and how to extract optimum value out of existing, pri-
marily anatomic clinical imaging studies. We focus on new technologies
(nanomedicine,fluorescence-guidedsurgery,Cerenkovimaging,andsmart
MR agents) as well as on new targets for detecting cancer directly or for
studying the biology of its supporting microenvironment (chemokine
receptor 4, hypoxia, pH, and the extracellular matrix). We also address
emerging clinical applications, including molecular-genetic imaging,
immunecelltracking,assessmentofimmunetherapies,andaspectsoftumor
metabolism.
A goal of this volume is to communicate the excitement in molecular
imagingresearchastheimagingtechnologiescontinuetoevolveandadapt
to new discoveries in cancer pathogenesis in ways that will enable more
precise management of patients suffering from this protean disease.
MARTIN G. POMPER
PAUL B. FISHER
xiii
CHAPTER ONE
Quantitative Radiology:
Applications to Oncology
Edward H. Herskovits1
DepartmentofDiagnosticRadiologyandNuclearMedicine,UniversityofMaryland,Baltimore,
Maryland,USA
1Correspondingauthor:e-mailaddress:[email protected]
Contents
1. Introduction 1
2. RadiologicalCharacterizationofTumors 2
2.1 Computedtomography 3
2.2 Magneticresonance 6
2.3 Positronemissiontomography 13
3. QuantitativeRadiology 15
3.1 Imageanalysis 15
3.2 Evaluation 20
3.3 Integration 21
4. FutureDirections 21
5. Conclusion 23
References 23
Abstract
Oncologists, clinician-scientists, and basic scientists collect computed tomography,
magneticresonance,andpositronemissiontomographyimagesintheprocessofcar-
ingforpatients,managingclinicaltrials,andinvestigatingcancerbiology.Aswehave
developedmoresophisticatedmeansfornoninvasivelydelineatingandcharacterizing
neoplasms,theseimagedatahavecometoplayacentralroleinoncology.Inparallel,
the increasing complexity and volume of these data have necessitated the develop-
mentofquantitativemethodsforassessingtumorburden,andbyproxy,disease-free
survival.
1. INTRODUCTION
Oncologists,clinician-scientists,andbasicscientistscollectaplethora
of data in the process of caring for patients, managing clinical trials, and
investigating cancer biology. As we have developed more sophisticated
AdvancesinCancerResearch,Volume124 #2014ElsevierInc. 1
ISSN0065-230X Allrightsreserved.
http://dx.doi.org/10.1016/B978-0-12-411638-2.00001-X
2 EdwardH.Herskovits
means for noninvasively delineating and interrogating neoplasms, the
resultingimagedatahavecometoplayacentralroleinoncology.Tounder-
standthecurrentimpactandlong-termpromiseofradiologywithrespectto
oncology,itmayhelptocharacterizethenatureoftheinformationsoughtas
we diagnose and treat cancer patients.
Theultimategoalofpatientcareinoncologyistomaximizedisease-free
survival (DFS)—or, barring that, progression-free survival (PFS)—while
minimizing the morbidity of treatment (i.e., to maximize quality-adjusted
life years). Ignoring intercurrent illnesses and treatment morbidity for the
sakeofthisdiscussion,wetakePFStobeafunctionoftumorburden,which
canbedecomposedintotwoindependentfactors:thenumberoftumorcells
and the malignant potential of each cell. For many years, the former—
extent—was determined via exploratory surgery and summarized as tumor
stage,andthelatter—grade—wasdeterminedbypathologistsfromwhatwas
hopedtobeabiologicallyrepresentativesampleobtainedduringthisoper-
ation.Advancesinradiologyfirstbecameevidentwithrespecttostaging,for
the simple reason that it is much easier to generate images that show mac-
roscopic groups of cells than it is to generate images that show how these
cellsarelikelytobehave.Onlyinthelastdecadehasradiologybeguntooffer
information regarding tumor biology, and such information still pales in
comparison with that obtained from histopathology and genetic analysis.
Inparallelwiththeincreasingcomplexityofimagedata,therehasbeen
steadyprogressinthequantificationofthesedata.Althoughclinicalradiol-
ogyreportsareunfortunatelyrepletewithverbiagesuchas“largemassinthe
right frontal lobe,” researchers have begun to deliver on the promise of
computer-based methods for quantification of tumor extent and have also
developed quantitative or semiquantitative methods for characterizing
tumor biology. The premise underlying such efforts is that quantitative—
rather than qualitative—indications of tumor extent and biology render
more precise prediction of DFS, thereby promising superior patient care
andassessmentoftherapy.HereinIexplorethearcofradiology’scontribu-
tionstooncology,bothintermsoftheinformationprovidedandeffortsto
quantify this information, with the expectation that such exploration will
shed light on future developments in oncology research and practice.
2. RADIOLOGICAL CHARACTERIZATION OF TUMORS
Theadventofcomputedtomography(CT)revolutionizedthestaging
ofsolidtumors;sincethen,thequalityandrangeofinformationprovidedto
QuantitativeRadiology:ApplicationstoOncology 3
oncologists via noninvasive radiological examinations have steadily
increased. The vast majority of this information relates to tumor extent;
however, magnetic resonance (MR), positron emission tomography
(PET),andnewermodalitieshaveofferedprogressivelymoredetailedinfor-
mationabouttumorphysiology(Fass,2008).Despiteimprovementsinthese
modalities,thereremainsignificantproblems.Forexample,itiswellknown
that,evenwithacombinationofadvancedMRsequencessuchasDTIand
perfusion, we cannot accurately delineate the extent of infiltrative tumors,
such as glioblastoma. Although there has been a striking expansion of
research modalities for characterizing tumors (Budde & Frank, 2009;
Cai & Chen, 2008; Desar et al., 2009; Fass, 2008; O’Connor et al., 2008;
Pfannenberg et al., 2007; van der Meel et al., 2010; Weissleder & Pittet,
2008), we focus here on those most widely applied in clinical research
and practice: CT, MR, and PET.
2.1. Computed tomography
TheprincipalformsofCTusedinoncologyarestructural(routine)CT,CT
perfusion, and dual-energy CT (DECT).
2.1.1 Structural (routine) CT
CT, usually following intravenous iodinated-contrast administration, has
been the workhorse of oncologists and researchers seeking to stage tumors
anddetermineresponsetotherapy.RelativetoMR,PET,andothermolec-
ularimagingtechniques,CTisinexpensive,fast,applicablethroughoutthe
body,andwidelyavailable,allofwhicharecriticalfeaturesofamodalitythat
would be used to establish internationally accepted response criteria for a
broad range of neoplasms. For many solid tumors—including some of the
most common, such as lung cancer and gastrointestinal malignancies—the
contrastbetweentumorandadjacentnormalstructures(i.e.,tissuecontrast)
issufficienttosupportdelineationoflesions(i.e.,toestimatestageortumor
burden). With the advent of helical CT (Van Hoe et al., 1997) and
multidetector CT, spatial resolution (particularly in the z-axis) increased,
allowing characterization of ever-smaller lesions.
2.1.2 CT perfusion
Although CT provides excellent anatomic information for most tumors, it
provides little physiologic information about tumors. CT perfusion, in
which intravenous contrast is administered as a bolus, and voxel-wise
time-attenuation curves are computed from repeated scans (including a
4 EdwardH.Herskovits
baselinenoncontrastscan),isoneofthemostcommonmeansforobtaining
information beyond precontrast or postcontrast attenuation values. The
widespread availability, first of helical scanners, and subsequently of
multidetector scanners, has promoted CT perfusion from a research tool
into a commonly used clinical tool, with applications across organ systems
and disease categories (Miles & Griffiths, 2003).
Thereareseveralcategoriesofmathematicalmodelsthathavebeenused
toinformthecalculationofperfusionparametersfromthetime-attenuation
curve, with varying assumptions about the interactions among the contrast
material (e.g., diffusibility; bolus contour), the patient’s physical state (e.g.,
cardiac output), and tissue characteristics (e.g., collateral flow; differences
between capillary and arterial hematocrit), among others. Virtually all per-
fusion analysis models ultimately invoke the Fick principle, which codifies
conservationofmass—inthiscase,blood—intheperfusionmodel.TheFick
principle models perfusion with a single (arterial) input that supplies a vol-
umeoftissue,whichinturndrainsintoasingle(venous)output.Underthis
model,allcontrastmustbeeitherinanartery,perfusingtissue,orinavein.
Twomajor groupsof methodsfor perfusionanalysis arethosebasedon
deconvolution,andeverythingelse.Deconvolutionmethodsareconsidered
tobemoreaccuratethanalternativeapproaches,butarealsomorecomplex.
Methods that do not employ deconvolution, such as the maximum-slope
method,oftenrelyonsimplifyingassumptions,suchastheassumptionthat
no venous outflow has occurred during the time interval of interest;
althoughsuchassumptionsareclearlynotvalidinmostCTperfusionacqui-
sitions, they may introduce only minimal parameter estimation errors.
Deconvolution approaches assume that the concentration of the contrast
agentintissueisalinearfunctionofflowtothetissueandtheconvolution
ofthearterialinputfunctionandtissue-specificcharacteristics(Nabavietal.,
1999); there are fewer simplifying assumptions than in nondeconvolution
approaches. Mathematical deconvolution methods, such as singular value
decomposition (Kudo et al., 2009; Ostergaard, Weisskoff, Chesler,
Gyldensted, & Rosen, 1996), yield blood flow (BF), blood volume (BV),
and other tissue-specific parameters. An additional parameter that is com-
monly employed in clinical practice is mean transit time (MTT), which is
computedfrom flow andvolumeviatherelation BF(cid:1)MTT¼BV.Perfu-
sionisexpressedasBFper100goftissue,whichcanbecomputedfromBF,
BV, and a tissue-density conversion factor.
Although CT perfusion has been utilized primarily in evaluating stroke
patients,ithasalsofoundanimportantroleintheevaluationofneoplasms.
QuantitativeRadiology:ApplicationstoOncology 5
Totheextentthatneovascularityreflectstumorgrade,featuresderivedfrom
CTperfusionwillproveprognosticallyuseful,particularlywhenevaluating
response to treatment with antiangiogenic agents such as bevacizumab and
cediranib(Kanetal.,2005).ResearchershaveappliedCTperfusiontochar-
acterizeoropharyngealsquamouscellcarcinoma(Gandhietal.,2006),lung
cancer (Ng et al., 2006), hepatocellular carcinoma (Ippolito, Capraro,
Casiraghi, Cestari, & Sironi, 2012; Jiang, Kambadakone, Kulkarni,
Zhu,&Sahani,2012;Sahani,Holalkere,Mueller,&Zhu,2007),rectalcan-
cer (Sahani et al., 2005), renal cell carcinoma (Fournier et al., 2010), and
pancreatic cancer (Park, Klotz, et al., 2009) and have correlated perfusion
parameters with tumor vascularity in a breast cancer animal model (Park,
Goo,etal.,2009).TheprimarylimitationtobroaderutilizationofCTper-
fusioninoncologyisnonuniformityoftechniquesandresults.Inparticular,
there are several sources of variability in CT perfusion, including different
CT perfusion analysis algorithms (Goh, Halligan, & Bartram, 2007), the
requirement for manual selection of thearterial inputfunction and manual
region of interest (ROI) placement (Goh, Halligan, Hugill, Bassett, &
Bartram,2005),CTtechnique(e.g.,bolustiming,spatialandtemporalres-
olution), and scanner characteristics. In addition, CT perfusion’s radiation
dose is greater than that for conventional CT (Mnyusiwalla, Aviv, &
Symons, 2009). These problems have hindered CT perfusions playing a
broader role in the standard radiological evaluation of neoplasms.
2.1.3 Dual-energy CT
DECT (Johnson et al., 2007) is based on the detection of two separate
energy profiles during acquisition; postprocessing yields monochromatic
images that can be used to differentiate among calcium, iodine, iron, and
other materials, based on their respective spectral profiles. For example,
iodine maps quantify voxel-wise enhancement, which may allow DECT
toplayarolesimilartothatofCTperfusioninevaluatingsubtleneoplasms,
while decreasing radiation dose relative to CT perfusion (De Cecco et al.,
2012).AnalogoustoobservationsforCTperfusion,Meyeretal.foundthat
DECT findings provided greater prognostic accuracy than RECIST or
ChoicriteriaforevaluatingGISTs(Meyeretal.,2013).Ofnote,preliminary
studieshavedisputedthenotionthatiodinemapscorrelatewithCTperfu-
sion or MR perfusion findings, at least for nononcologic applications
(Hansmann et al., 2013); this result is reasonable in light of the fact that
DECT is typically a single time-point acquisition, whereas CT perfusion
parameters are computed from time-attenuation curves. Although DECT
