Featured Publications
Artificial Intelligence in Breast Cancer Screening
Potnis K, Ross J, Aneja S, Gross C, Richman I. Artificial Intelligence in Breast Cancer Screening. JAMA Internal Medicine 2022, 182: 1306-1312. PMID: 36342705, PMCID: PMC10623674, DOI: 10.1001/jamainternmed.2022.4969.Peer-Reviewed Original ResearchPrevalence of Missing Data in the National Cancer Database and Association With Overall Survival
Yang DX, Khera R, Miccio JA, Jairam V, Chang E, Yu JB, Park HS, Krumholz HM, Aneja S. Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival. JAMA Network Open 2021, 4: e211793. PMID: 33755165, PMCID: PMC7988369, DOI: 10.1001/jamanetworkopen.2021.1793.Peer-Reviewed Original ResearchConceptsNational Cancer DatabaseNon-small cell lung cancerOverall survivalCell lung cancerCancer DatabaseMedical recordsLung cancerProstate cancerBreast cancerPatient recordsComplete dataRetrospective cohort studyCohort studyCancer RegistryCommon cancerVariables of interestHigh prevalenceMAIN OUTCOMEPatientsClinical advancementReal-world data sourcesCancerPrevalenceSurvivalHeterogeneous differencesComparison of radiomic feature aggregation methods for patients with multiple tumors
Chang E, Joel MZ, Chang HY, Du J, Khanna O, Omuro A, Chiang V, Aneja S. Comparison of radiomic feature aggregation methods for patients with multiple tumors. Scientific Reports 2021, 11: 9758. PMID: 33963236, PMCID: PMC8105371, DOI: 10.1038/s41598-021-89114-6.Peer-Reviewed Original ResearchConceptsCox proportional hazards modelCox proportional hazardsProportional hazards modelBrain metastasesRadiomic featuresHazards modelProportional hazardsStandard Cox proportional hazards modelMultifocal brain metastasesMultiple brain metastasesNumber of patientsPatient-level outcomesHigher concordance indexRadiomic feature analysisRandom survival forest modelSurvival modelsDifferent tumor volumesMultifocal tumorsCancer outcomesMultiple tumorsMetastatic cancerConcordance indexTumor volumePatientsTumor types
2018
Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks
Kann BH, Aneja S, Loganadane GV, Kelly JR, Smith SM, Decker RH, Yu JB, Park HS, Yarbrough WG, Malhotra A, Burtness BA, Husain ZA. Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks. Scientific Reports 2018, 8: 14036. PMID: 30232350, PMCID: PMC6145900, DOI: 10.1038/s41598-018-32441-y.Peer-Reviewed Original ResearchConceptsExtranodal extensionNodal metastasisPatient managementNeck cancer patient managementBlinded test setClinical decision-making toolCancer patient managementNeck cancer managementPostoperative pathologyPretreatment identificationCancer managementMetastasisRadiographic identificationCharacteristic curveCliniciansConvolutional neural networkHuman cliniciansNeural networkHeadDeep learning convolutional neural networkLymphDeep learning neural network
2012
Radiation therapy in the management of unilesional primary cutaneous T‐cell lymphomas
Chan D, Aneja S, Honda K, Carlson S, Yao M, Katcher J, Cooper K. Radiation therapy in the management of unilesional primary cutaneous T‐cell lymphomas. British Journal Of Dermatology 2012, 166: 1134-1137. PMID: 22059744, DOI: 10.1111/j.1365-2133.2011.10728.x.Peer-Reviewed Original Research