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
2020
Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence
Hart GR, Yan V, Huang GS, Liang Y, Nartowt BJ, Muhammad W, Deng J. Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence. Frontiers In Artificial Intelligence 2020, 3: 539879. PMID: 33733200, PMCID: PMC7861326, DOI: 10.3389/frai.2020.539879.Peer-Reviewed Original ResearchAverage-risk womenEndometrial cancerRisk womenOvarian Cancer Screening TrialEndometrial cancer riskCancer Screening TrialPrimary care physiciansPopulation-based screeningCancer risk predictionHealth dataCare physiciansGynecologic oncologistsRisk stratificationDisease onsetPositive rateIndividual patientsCancer riskInvasive proceduresScreening TrialPersonal health dataEarly cancer detectionMortality rateEarly screeningFalse positive ratePrevious risk models
2019
Predicting breast cancer risk using personal health data and machine learning models
Stark GF, Hart GR, Nartowt BJ, Deng J. Predicting breast cancer risk using personal health data and machine learning models. PLOS ONE 2019, 14: e0226765. PMID: 31881042, PMCID: PMC6934281, DOI: 10.1371/journal.pone.0226765.Peer-Reviewed Original ResearchConceptsBreast cancer riskBreast cancer risk predictionCancer riskCancer risk predictionGail modelBreast cancer risk prediction toolsCancer risk prediction toolsPositive breast cancer casesHormone replacement therapyRisk stratification toolRisk predictionRisk prediction toolsHealth dataBreast cancer casesPersonal health dataStratification toolReplacement therapyLeading causeBreast cancerCancer casesInvasive proceduresBCRATDeLong testLogistic regressionEarly breast cancer detectionScoring 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
Development and Validation of a Multiparameterized Artificial Neural Network for Prostate Cancer Risk Prediction and Stratification
Roffman DA, Hart GR, Leapman MS, Yu JB, Guo FL, Ali I, Deng J. Development and Validation of a Multiparameterized Artificial Neural Network for Prostate Cancer Risk Prediction and Stratification. JCO Clinical Cancer Informatics 2018, 2: 1-10. PMID: 30652591, PMCID: PMC6873987, DOI: 10.1200/cci.17.00119.Peer-Reviewed Original ResearchConceptsProstate Cancer Risk PredictionCancer risk predictionPositive predictive valueProstate cancerRisk predictionPredictive valueHistory of strokeHigh-risk subgroupsBody mass indexCancer risk stratificationHealth informationPersonal health informationPrimary prostate cancerYears of ageDiabetes statusRisk stratificationSmoking statusMass indexCancer populationHeart diseaseExercise habitsCancer riskHigh riskCutoff valueAdult survey dataA multi-parameterized artificial neural network for lung cancer risk prediction
Hart GR, Roffman DA, Decker R, Deng J. A multi-parameterized artificial neural network for lung cancer risk prediction. PLOS ONE 2018, 13: e0205264. PMID: 30356283, PMCID: PMC6200229, DOI: 10.1371/journal.pone.0205264.Peer-Reviewed Original ResearchConceptsLung cancer risk predictionHistory of strokeNon-invasive clinical toolLung cancer riskHealth informationPersonal health informationNon-cancer casesCancer risk predictionRisk stratificationSmoking statusHeart diseaseExercise habitsHispanic ethnicityLung cancer detectionCancer riskClinical toolAdult dataRisk predictionModest sensitivityCancer detectionAUCHigh specificitySpecificityHypertensionAsthmaImaging 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 dosesRadiotherapyDoses
2015
Concomitant Imaging Dose and Cancer Risk in Image Guided Thoracic Radiation Therapy
Zhang Y, Wu H, Chen Z, Knisely JP, Nath R, Feng Z, Bao S, Deng J. Concomitant Imaging Dose and Cancer Risk in Image Guided Thoracic Radiation Therapy. International Journal Of Radiation Oncology • Biology • Physics 2015, 93: 523-531. PMID: 26460994, DOI: 10.1016/j.ijrobp.2015.06.034.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge FactorsAgedAged, 80 and overAorta, ThoracicBody SizeBreastChildChild, PreschoolCone-Beam Computed TomographyFemaleHeartHumansLungMaleMiddle AgedMonte Carlo MethodOrgans at RiskPhantoms, ImagingPhotonsPrecision MedicineProtonsRadiation DosageRadiography, ThoracicRadiotherapy, Image-GuidedRisk AssessmentSex FactorsSpinal CordThoracic WallThoraxConceptsConcomitant imaging doseThoracic radiation therapyCancer riskRadiation therapyMean dosesCardiac substructuresKilovoltage cone-beamImaging doseAdverse eventsPediatric patientsMedian dosesCancer patientsRight ventricleExtra radiation doseSpinal cordHigh dosesPatientsCone beamPlanning CT imagesChest dimensionsDosesPatient sizeImaging guidanceTherapyDose
2014
SU‐D‐9A‐07: Imaging Dose and Cancer Risk in Image‐Guided Radiotherapy of Cancers
Zhou L, Bai S, Zhang Y, Ming X, Zhang Y, Deng J. SU‐D‐9A‐07: Imaging Dose and Cancer Risk in Image‐Guided Radiotherapy of Cancers. Medical Physics 2014, 41: 123-123. DOI: 10.1118/1.4887923.Peer-Reviewed Original ResearchExcess relative riskCancer riskRed bone marrowImage-guided radiotherapyImaging proceduresBone marrowRadiation doseRelative cancer riskSpecific imaging proceduresClinical benefitRetrospective studyTreatment courseWhole cohortRadiotherapy courseCancer patientsRelative riskLarge dosesPatientsHigh dosesPersonalized imagingDosesAbdominal regionDoseCT simulationTreatment planning
2012
Personalized estimation of dose to red bone marrow and the associated leukaemia risk attributable to pelvic kilo-voltage cone beam computed tomography scans in image-guided radiotherapy
Zhang Y, Yan Y, Nath R, Bao S, Deng J. Personalized estimation of dose to red bone marrow and the associated leukaemia risk attributable to pelvic kilo-voltage cone beam computed tomography scans in image-guided radiotherapy. Physics In Medicine And Biology 2012, 57: 4599-4612. PMID: 22750636, DOI: 10.1088/0031-9155/57/14/4599.Peer-Reviewed Original ResearchConceptsKilo-voltage cone beamRed bone marrowLeukemia riskRBM doseImage-guided radiation therapyTomography scanBone marrowCone beamRadiogenic cancer riskRadiation therapy courseCancer patientsBone densityImage-guided radiotherapyRadiation therapyCancer riskHigh riskPersonalized estimationAnthropometric variablesTherapy courseDoseSubject groupsDosesBone dosesScansPatients