Headandnecksquamouscarcinomasaffectallethnicitiesandagegroups, accountingforsignificant mortality and therapyrelated sideeffects. Over 50,000 new cases are diagnosed each year in the UnitedStates,leadingtolarge,richrepositoriesofpatientdata.Foreachofthesecases,oncologists needtoanticipatesurvival,oncologic,andsideeffectoutcomesassociatedwithtreatmentstrategiesin ordertoselectatreatmentwhichbalancesefficacyandtoxicity.However,despitethewealthofdata available,riskpredictionalgorithmsforcancersarerudimentaryandincorporateonlyahandfulof patientcharacteristics,largelyduetoalackofcomputationalmethodologies. We propose to develop a computational methodology that supports the construction of a validated precision model of patientspecific outcomes for head and neck cancers, based on demographics,toxicity,andcompleximagingdata.Ourensemblemethodologywillberevolutionaryin that it will be the first in the field to include both imaging and nonimaging data, while taking into accountlargescalebiologicalandclinicalcorrelates.Theapproachisinnovativethroughitsleverageof big data repositories and through its unique blend of computational modeling principles from bioengineering,statistics,andcomputerscience.Thesemethodsallowustoincorporatediversedata types,handlemissingdata,andmodelmultipleoutcomes. Fromaclinicalperspective,thisintegrativeapproachisnovelinthefieldofcancertherapy.The resultingmodelandwebbasedenvironmentwillmarkasignificantadvanceinbiomedicalcomputing becauseitwillbeabletoidentify,forthefirsttime,specificsubgroupswhoareatriskfordistinct oncologic,toxicity,andsurvivalprofiles.Furthermore,the?currentclinicalpracticeparadigmforhead andnecktreatmentisstagedriven.Withvalidation,thisnovelapproachcanidentifypatientsbasednot on clinical staging, but on precision modeling using cohort data and similar sets of patients. The methodologyisapplicabletoclinicalpracticeinvolvinghumansubjectsandhasthepotentialtochange thestandardofcareandoutcomesoftreatmentinthefield.

Public Health Relevance

Over50,000newcasesofheadandneckcancersarediagnosedeachyearintheUnitedStates,leading tolarge,richrepositoriesofpatientdata.Thisprojectdevelopsthecomputationalmethodologyneeded to transform these repositories into precise models which can predict patientspecific responses to cancertherapy.Theprojectwillleadtobetterinsightsintothebiological,therapeuticanddemographic factorsrelatedtocancersurvivalandtoxicity,allowingpersonalizationofcareattheindividuallevel, andlargescaleprecisionmedicinebasedonquantitativerisk.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA214825-01
Application #
9288680
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Ossandon, Miguel
Project Start
2017-03-03
Project End
2020-02-29
Budget Start
2017-03-03
Budget End
2018-02-28
Support Year
1
Fiscal Year
2017
Total Cost
$407,321
Indirect Cost
$81,876
Name
University of Illinois at Chicago
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
098987217
City
Chicago
State
IL
Country
United States
Zip Code
60612
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