2023
Liver cancer risk quantification through an artificial neural network based on personal health data
Ataei A, Deng J, Muhammad W. Liver cancer risk quantification through an artificial neural network based on personal health data. Acta Oncologica 2023, 62: 495-502. PMID: 37211681, DOI: 10.1080/0284186x.2023.2213445.Peer-Reviewed Original ResearchConceptsNational Health Interview SurveyLiver cancer riskHealth dataCancer riskHealth Interview SurveyHepatocellular carcinomaPersonal health dataHigh-risk populationLiver cancerInterview SurveyReceiver operating characteristic curveArea under the receiver operating characteristic curveCancer-related deathsPrimary liver cancerHealthOvarian cancerTherapeutic optionsMalignant diseaseTest cohortEarly detectionAggressive progressionRiskCancerCharacteristic curveLiverStatistical biopsy: An emerging screening approach for early detection of cancers
Hart G, Yan V, Nartowt B, Roffman D, Stark G, Muhammad W, Deng J. Statistical biopsy: An emerging screening approach for early detection of cancers. Frontiers In Artificial Intelligence 2023, 5: 1059093. PMID: 36744110, PMCID: PMC9895959, DOI: 10.3389/frai.2022.1059093.Peer-Reviewed Original ResearchCancer riskDifferent cancer typesCancer typesStatistical modelRisk of complicationsIndividual cancer riskPersonal health dataHealth dataGeneral populationMultiple cancer risksBiopsyCancerContinuous outputMost cancersTraditional biopsyEarly detectionRiskBinary outputCancer detectionNeural networkMachine learningTraditional methodsMorbidityComplicationsModel
2022
Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation
Stahlberg E, Abdel-Rahman M, Aguilar B, Asadpoure A, Beckman R, Borkon L, Bryan J, Cebulla C, Chang Y, Chatterjee A, Deng J, Dolatshahi S, Gevaert O, Greenspan E, Hao W, Hernandez-Boussard T, Jackson P, Kuijjer M, Lee A, Macklin P, Madhavan S, McCoy M, Mirzaei N, Razzaghi T, Rocha H, Shahriyari L, Shmulevich I, Stover D, Sun Y, Syeda-Mahmood T, Wang J, Wang Q, Zervantonakis I. Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. Frontiers In Digital Health 2022, 4: 1007784. PMID: 36274654, PMCID: PMC9586248, DOI: 10.3389/fdgth.2022.1007784.Peer-Reviewed Original ResearchMonitoring treatment responsePatient digital twinsUS National Cancer InstituteNational Cancer InstituteTreatment responsePlanning treatmentEarly progressionCancer preventionDigital twin approachIndividual patientsPersonalized treatmentPilot projectCancer InstituteCancer typesCancerDigital twinDeep phenotypingCancer researchPatients
2019
A Model of Risk of Colorectal Cancer Tested between Studies: Building Robust Machine Learning Models for Colorectal Cancer Risk Prediction
Nartowt B, Hart G, Muhammad W, Liang Y, Deng J. A Model of Risk of Colorectal Cancer Tested between Studies: Building Robust Machine Learning Models for Colorectal Cancer Risk Prediction. International Journal Of Radiation Oncology • Biology • Physics 2019, 105: e132. DOI: 10.1016/j.ijrobp.2019.06.2265.Peer-Reviewed Original ResearchScoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data
Nartowt BJ, Hart GR, Roffman DA, Llor X, Ali I, Muhammad W, Liang Y, Deng J. Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data. PLOS ONE 2019, 14: e0221421. PMID: 31437221, PMCID: PMC6705772, DOI: 10.1371/journal.pone.0221421.Peer-Reviewed Original ResearchConceptsNational Health Interview SurveyUnited States Preventative Services Task ForceColorectal cancerPredictive valueDiagnosis of CRCColorectal cancer riskHealth Interview SurveyHigh-risk categoryNegative predictive valuePositive predictive valueMultivariable prediction modelHealth dataUSPSTF guidelinesRisk score methodCRC riskFamily historyCancer riskHigh riskAge 50Individual prognosisLower riskPersonal health dataClinical applicabilityInterview SurveyCancerStratifying Ovarian Cancer Risk Using Personal Health Data
Hart GR, Nartowt BJ, Muhammad W, Liang Y, Huang GS, Deng J. Stratifying Ovarian Cancer Risk Using Personal Health Data. Frontiers In Big Data 2019, 2: 24. PMID: 33693347, PMCID: PMC7931902, DOI: 10.3389/fdata.2019.00024.Peer-Reviewed Original ResearchOvarian cancer riskCancer riskOvarian Cancer Screening TrialNational Health Interview SurveyCancer Screening TrialHigh-risk populationHealth Interview SurveyHealth dataOvarian cancer detectionDifferent risk categoriesPublic health organizationsOvarian cancerScreening TrialGeneral populationLower riskPersonal health dataTargeted screeningGenetic testingRisk categoriesHealth OrganizationInterview SurveyCancerCharacteristic curveNon-invasive wayCancer detectionPancreatic Cancer Prediction Through an Artificial Neural Network
Muhammad W, Hart GR, Nartowt B, Farrell JJ, Johung K, Liang Y, Deng J. Pancreatic Cancer Prediction Through an Artificial Neural Network. Frontiers In Artificial Intelligence 2019, 2: 2. PMID: 33733091, PMCID: PMC7861334, DOI: 10.3389/frai.2019.00002.Peer-Reviewed Original ResearchNational Health Interview SurveyPancreatic cancer riskPancreatic cancerCancer riskHigh-risk patientsCancer-specific symptomsHealth Interview SurveyReliable screening toolHigh riskTesting cohortAdvanced stagePatientsScreening toolEarly detectionInterview SurveyCancerCharacteristic curveHigh discriminatory powerHealth dataRiskOvarian cancer datasetDiscriminatory powerCancer predictionArtificial neural networkColorectal
2018
Imaging Dose, Cancer Risk and Cost Analysis in Image-guided Radiotherapy of Cancers
Zhou L, Bai S, Zhang Y, Ming X, Zhang Y, Deng J. Imaging Dose, Cancer Risk and Cost Analysis in Image-guided Radiotherapy of Cancers. Scientific Reports 2018, 8: 10076. PMID: 29973695, PMCID: PMC6031630, DOI: 10.1038/s41598-018-28431-9.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overBone Marrow CellsBrainChildChild, PreschoolCone-Beam Computed TomographyCost-Benefit AnalysisFemaleHumansInfantLungMaleMiddle AgedMonte Carlo MethodNeoplasmsPhantoms, ImagingRadiation DosageRadiotherapy DosageRadiotherapy, Image-GuidedRisk FactorsThoraxYoung AdultConceptsCancer riskAssociated cancer riskImage-guided radiotherapyImaging proceduresLifetime attributable riskImaging dosesAverage lifetime attributable riskRadiological imaging proceduresRed bone marrowRetrospective studyCancer patientsLung cancerAttributable riskCancer incidenceBilling codesIndividual patientsBone marrowBrain cancerImage guidance proceduresPelvic scanPatientsCancerOrgan dosesRadiotherapyDosesPredicting non-melanoma skin cancer via a multi-parameterized artificial neural network
Roffman D, Hart G, Girardi M, Ko CJ, Deng J. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Scientific Reports 2018, 8: 1701. PMID: 29374196, PMCID: PMC5786038, DOI: 10.1038/s41598-018-19907-9.Peer-Reviewed Original ResearchConceptsNon-melanoma skin cancerFamily historySkin cancerUVR exposureHistory of strokePotential predictive parametersNon-cancer casesFamily history informationUltraviolet radiation exposureDiabetic statusSmoking statusHeart diseaseRisk factorsExercise habitsHispanic ethnicityPredictive parametersAdult survey dataEarly detectionRadiation exposureROC curveHealth informationPersonal health informationNovel associationsCancerExposure
2009
Impact of Kilo-Voltage Cone Beam Computed Tomography on Image-Guided Radiotherapy of Prostate Cancer
Deng J, Chen Z, Nath R. Impact of Kilo-Voltage Cone Beam Computed Tomography on Image-Guided Radiotherapy of Prostate Cancer. IFMBE Proceedings 2009, 25/1: 17-20. DOI: 10.1007/978-3-642-03474-9_5.Peer-Reviewed Original ResearchKilo-voltage cone beamProstate cancerImage-guided radiotherapyDosevolume histogramsExcessive dosesCone beamFemur headAdjacent critical structuresPatient anatomyTesticular doseCBCT dosesTesticular dosesImage-guided radiation therapyRadiation therapyMost dosesSuperior-inferior directionDVH analysisCritical organsCancerDosesIGRT treatmentReal patient anatomyDosimetric impactScanning protocolPosterior side
2003
Dose correlation for thoracic motion in radiation therapy of breast cancer
Ding M, Li J, Deng J, Fourkal E, Ma C. Dose correlation for thoracic motion in radiation therapy of breast cancer. Medical Physics 2003, 30: 2520-2529. PMID: 14528974, DOI: 10.1118/1.1603744.Peer-Reviewed Original ResearchMeSH KeywordsArtifactsBreast NeoplasmsHumansMammographyMotionMovementOnline SystemsPhantoms, ImagingRadiographic Image Interpretation, Computer-AssistedRadiography, ThoracicRadiometryRadiotherapy DosageRadiotherapy Planning, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsRespirationSensitivity and SpecificityStatistics as TopicThoraxTomography, X-Ray ComputedConceptsBreathing patternDose correlationBreast cancerChest wall motionChest wall movementPatient's breathing patternDose dataRadiation therapyThoracic motionRadiotherapy treatmentTreatment planningExpiration phaseDifferent breathing phasesFinal dose distributionCancerBreathing phasesPatient anatomyWall motionTreatmentDose distributionCT dataBreathing levelTherapyDosePatient geometry
2000
Modulated Electron Beams for Treatment of Breast Cancer
Ma C, Pawlicki T, Lee M, Jiang S, Li J, Deng J, Yi B, Mok E, Boyer A. Modulated Electron Beams for Treatment of Breast Cancer. 2000, 173-175. DOI: 10.1007/978-3-642-59758-9_64.Peer-Reviewed Original ResearchContralateral breastBreast cancer treatmentChest wall treatmentSuch conventional treatmentHigh-dose volumeTangential photon fieldsTumor locationBreast cancerSecondary cancersEffective modalityRadiation therapyLow doseConventional treatmentNormal tissuesCancer treatmentPatient sizeMajor causeLungDoseTreatmentNormal structureCancerBreastHeartScatter dose