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 #
5R01CA214825-03
Application #
9645039
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Ossandon, Miguel
Project Start
2017-03-03
Project End
2021-02-28
Budget Start
2019-03-01
Budget End
2021-02-28
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Illinois at Chicago
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
098987217
City
Chicago
State
IL
Country
United States
Zip Code
60612
Marai, G Elisabeta (2018) Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization. IEEE Trans Vis Comput Graph 24:913-922
Guzun, Gheorghi; Canahuate, Guadalupe (2018) Distributed query-aware quantization for high-dimensional similarity searches. Adv Database Technol 2018:373-384
Jurkovic, Ines-Ana; Kocak-Uzel, Esengul; Mohamed, Abdallah Sherif Radwan et al. (2018) Dosimetric and Radiobiological Evaluation of Patient Setup Accuracy in Head-and-neck Radiotherapy Using Daily Computed Tomography-on-rails-based Corrections. J Med Phys 43:28-40
M. D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group (2018) Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Sci Rep 8:1524
McKearnan, Shannon B; Wolfson, Julian; Vock, David M et al. (2018) Performance of the Net Reclassification Improvement for Nonnested Models and a Novel Percentile-Based Alternative. Am J Epidemiol 187:1327-1335
Pinnix, Chelsea C; Ng, Andrea K; Dabaja, Bouthaina S et al. (2018) Positron emission tomography-computed tomography predictors of progression after DA-R-EPOCH for PMBCL. Blood Adv 2:1334-1343
Kamal, Mona; Ng, Sweet Ping; Eraj, Salman A et al. (2018) Three-dimensional imaging assessment of anatomic invasion and volumetric considerations for chemo/radiotherapy-based laryngeal preservation in T3 larynx cancer. Oral Oncol 79:1-8
Grossberg, Aaron J; Mohamed, Abdallah S R; Elhalawani, Hesham et al. (2018) Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy. Sci Data 5:180173
Marai, G Elisabeta; Ma, Chihua; Burks, Andrew et al. (2018) Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots. IEEE Trans Vis Comput Graph :
Multidisciplinary Larynx Cancer Working Group; Mulcahy, Collin F; Mohamed, Abdallah S R et al. (2018) Age-adjusted comorbidity and survival in locally advanced laryngeal cancer. Head Neck 40:2060-2069

Showing the most recent 10 out of 31 publications