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 ResearchPerspectives of Patients About Artificial Intelligence in Health Care
Khullar D, Casalino LP, Qian Y, Lu Y, Krumholz HM, Aneja S. Perspectives of Patients About Artificial Intelligence in Health Care. JAMA Network Open 2022, 5: e2210309. PMID: 35507346, PMCID: PMC9069257, DOI: 10.1001/jamanetworkopen.2022.10309.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 differencesPublic vs physician views of liability for artificial intelligence in health care
Khullar D, Casalino LP, Qian Y, Lu Y, Chang E, Aneja S. Public vs physician views of liability for artificial intelligence in health care. Journal Of The American Medical Informatics Association 2021, 28: 1574-1577. PMID: 33871009, PMCID: PMC8279784, DOI: 10.1093/jamia/ocab055.Peer-Reviewed Original ResearchComparison 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 typesUsing Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology
Joel MZ, Umrao S, Chang E, Choi R, Yang DX, Duncan JS, Omuro A, Herbst R, Krumholz HM, Aneja S. Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology. JCO Clinical Cancer Informatics 2022, 6: e2100170. PMID: 35271304, PMCID: PMC8932490, DOI: 10.1200/cci.21.00170.Peer-Reviewed Original ResearchPremetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinoma
Fisk JN, Mahal AR, Dornburg A, Gaffney SG, Aneja S, Contessa JN, Rimm D, Yu JB, Townsend JP. Premetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinoma. Cancer Letters 2021, 526: 346-351. PMID: 34780851, PMCID: PMC8702484, DOI: 10.1016/j.canlet.2021.11.011.Peer-Reviewed Original ResearchConceptsMutational processesSingle ancestral lineageAncestral lineageProgression of cancerMetastatic lineagesPhylogenetic analysisGenetic resistanceEvolutionary processesExogenous mutational processesCancer evolutionConsolidative therapyMutational signature analysisEGFR-positive non-small cell lung cancerNon-small cell lung cancerKey eventsLineagesCell populationsTherapeutic resistanceLocal consolidative therapyClinical time courseCell lung cancerDisease etiologyTherapeutic decision makingCisplatin therapyLung cancer3D Capsule Networks for Brain Image Segmentation
Avesta A, Hui Y, Aboian M, Duncan J, Krumholz H, Aneja S. 3D Capsule Networks for Brain Image Segmentation. American Journal Of Neuroradiology 2023, 44: 562-568. PMID: 37080721, PMCID: PMC10171390, DOI: 10.3174/ajnr.a7845.Peer-Reviewed Original Research
2024
A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information
Ramakrishnan D, Jekel L, Chadha S, Janas A, Moy H, Maleki N, Sala M, Kaur M, Petersen G, Merkaj S, von Reppert M, Baid U, Bakas S, Kirsch C, Davis M, Bousabarah K, Holler W, Lin M, Westerhoff M, Aneja S, Memon F, Aboian M. A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. Scientific Data 2024, 11: 254. PMID: 38424079, PMCID: PMC10904366, DOI: 10.1038/s41597-024-03021-9.Peer-Reviewed Original ResearchConceptsWhole-brain radiotherapyStereotactic radiosurgeryT1 post-contrastBrain metastasesPost-contrastSide effectsImage informationArtificial intelligenceAssociated with cognitive side effectsContrast-enhancing lesionsQuality of datasetsCognitive side effectsFLAIR MR imagesValidation of AI modelsBrain radiotherapyLimitations of algorithmsStandard treatmentAI modelsMR imagingAI networksContrast enhancementClinical settingSegmentation workflowDatasetClinical adoption
2023
Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas
Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay E, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo I, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Scientific Reports 2023, 13: 22942. PMID: 38135704, PMCID: PMC10746716, DOI: 10.1038/s41598-023-48918-4.Peer-Reviewed Original ResearchConceptsInformatics platformDeep learning algorithmsImaging featuresCDKN2A alterationsLearning algorithmHeterozygous lossHomozygous deletionLarge datasetsDeep white matter invasionGBM molecular subtypesNew informaticsQualitative imaging biomarkersWhole-exome sequencingQualitative imaging featuresGBM resectionRadiographic evidenceWorse prognosisPACSMolecular subtypesPial invasionImaging biomarkersCDKN2A mutationsAllele statusNoninvasive identificationMagnetic resonance imagesSystematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction
Lost J, Verma T, Jekel L, von Reppert M, Tillmanns N, Merkaj S, Petersen G, Bahar R, Gordem A, Haider M, Subramanian H, Brim W, Ikuta I, Omuro A, Conte G, Marquez-Nostra B, Avesta A, Bousabarah K, Nabavizadeh A, Kazerooni A, Aneja S, Bakas S, Lin M, Sabel M, Aboian M. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction. American Journal Of Neuroradiology 2023, 44: 1126-1134. PMID: 37770204, PMCID: PMC10549943, DOI: 10.3174/ajnr.a8000.Peer-Reviewed Original ResearchClinical Informatics Approaches to Facilitate Cancer Data Sharing
Aneja S, Avesta A, Xu H, Machado L. Clinical Informatics Approaches to Facilitate Cancer Data Sharing. Yearbook Of Medical Informatics 2023, 32: 104-110. PMID: 37414028, PMCID: PMC10751108, DOI: 10.1055/s-0043-1768721.Peer-Reviewed Original ResearchConceptsCommon data modelHomomorphic encryptionData modelData sharingClinical informaticsMachine learning techniquesInformatics studiesDiagnostic image analysisLearning techniquesElectronic health recordsEncryptionClinical dataAnalyticsHealth recordsPromising solutionHealth dataInformaticsSharingSmall settingsData availabilityImage analysisPromising resultsHealth informationRepresentative datasetCancer dataScreening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial
Kann B, Likitlersuang J, Bontempi D, Ye Z, Aneja S, Bakst R, Kelly H, Juliano A, Payabvash S, Guenette J, Uppaluri R, Margalit D, Schoenfeld J, Tishler R, Haddad R, Aerts H, Garcia J, Flamand Y, Subramaniam R, Burtness B, Ferris R. Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial. The Lancet Digital Health 2023, 5: e360-e369. PMID: 37087370, PMCID: PMC10245380, DOI: 10.1016/s2589-7500(23)00046-8.Peer-Reviewed Original ResearchConceptsExtranodal extensionOropharyngeal carcinomaShort-axis diameterChallenging cohortPathology reportsECOG-ACRIN Cancer Research GroupDe-escalation trialsCancer Research GroupDe-escalation strategiesSurgical pathology reportsNational Cancer InstituteInter-reader agreementLargest short-axis diameterPostoperative chemoradiationProtocol exclusionsConcurrent chemoradiationPrimary endpointMulticentre trialPretreatment CTAdjuvant strategiesHuman papillomavirusTreatment selection toolUS National InstitutesPretreatment identificationStudy protocolDeveloping Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study
Bikdeli B, Lo Y, Khairani C, Bejjani A, Jimenez D, Barco S, Mahajan S, Caraballo C, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Wang Y, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Aneja S, Khera R, Goldhaber S, Zhou L, Monreal M, Krumholz H, Piazza G. Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study. Thrombosis And Haemostasis 2023, 123: 649-662. PMID: 36809777, PMCID: PMC11200175, DOI: 10.1055/a-2039-3222.Peer-Reviewed Original ResearchConceptsElectronic health recordsNLP algorithmNatural language processing toolsLanguage processing toolsPrincipal discharge diagnosisICD-10 codesDischarge diagnosisNLP toolsChart reviewHealth systemProcessing toolsYale New Haven Health SystemPatient identificationElectronic databasesHealth recordsData validationHigh-risk PEPulmonary Embolism ResearchSecondary discharge diagnosisIdentification of patientsManual chart reviewNegative predictive valueCodeRadiology reportsAlgorithmUsing ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information
Johnson S, King A, Warner E, Aneja S, Kann B, Bylund C. Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information. JNCI Cancer Spectrum 2023, 7: pkad015. PMID: 36929393, PMCID: PMC10020140, DOI: 10.1093/jncics/pkad015.Peer-Reviewed Original ResearchConceptsNumber of wordsBladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence
Moore N, McWilliam A, Aneja S. Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Seminars In Radiation Oncology 2023, 33: 70-75. PMID: 36517196, DOI: 10.1016/j.semradonc.2022.10.009.Peer-Reviewed Original ResearchConceptsArtificial intelligenceMachine learningReliability of algorithmAccurate predictive modelsEfficient creationIntelligenceBladder cancer patientsRadiation oncology patientsAlgorithmPrognostic modellingRoutine clinical useClinical outcomesOncology patientsClinical recordsCancer patientsBladder cancerPredictive modelTreatment planClinical useMultiple treatment plansClinical implementationNext stepRadiation oncologyTreatment planningInterpretability
2021
Opportunities for integration of artificial intelligence into stereotactic radiosurgery practice
Kotecha R, Aneja S. Opportunities for integration of artificial intelligence into stereotactic radiosurgery practice. Neuro-Oncology 2021, 23: 1629-1630. PMID: 34244803, PMCID: PMC8485447, DOI: 10.1093/neuonc/noab169.Peer-Reviewed Original Research
2020
Provider Engagement in Radiation Oncology Data Science: Workshop Report.
Jain AK, Aneja S, Fuller CD, Dicker AP, Chung C, Kim E, Kirby JS, Quon H, Lam CJK, Louv WC, Ahern C, Xiao Y, McNutt TR, Housri N, Ennis RD, Kang J, Tang Y, Higley H, Berny-Lang MA, Camphausen KA. Provider Engagement in Radiation Oncology Data Science: Workshop Report. JCO Clinical Cancer Informatics 2020, 4: 700-710. PMID: 32755458, PMCID: PMC7469584, DOI: 10.1200/cci.20.00051.Peer-Reviewed Original Research
2019
Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.
Kann BH, Hicks DF, Payabvash S, Mahajan A, Du J, Gupta V, Park HS, Yu JB, Yarbrough WG, Burtness BA, Husain ZA, Aneja S. Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma. Journal Of Clinical Oncology 2019, 38: 1304-1311. PMID: 31815574, DOI: 10.1200/jco.19.02031.Peer-Reviewed Original ResearchConceptsNeck squamous cell carcinomaExtranodal extensionSquamous cell carcinomaLymph nodesCell carcinomaContrast-enhanced CT scanDiagnostic abilityBoard-certified neuroradiologistsTreatment escalationCancer Genome AtlasPathologic confirmationPretreatment identificationDiagnostic challengeExternal validation data setsPathology resultsPretreatment imagingPoor prognosticatorClinical utilityCT scanPatientsClinical decisionHNSCCDiagnostic accuracyInstitutional ValidationGenome AtlasApplications of artificial intelligence in neuro-oncology.
Aneja S, Chang E, Omuro A. Applications of artificial intelligence in neuro-oncology. Current Opinion In Neurology 2019, 32: 850-856. PMID: 31609739, DOI: 10.1097/wco.0000000000000761.Peer-Reviewed Original ResearchConceptsArtificial intelligenceArtificial intelligence algorithmsNatural language processingAmount of dataIntelligence algorithmsLanguage processingIntelligenceNeuro-oncologyImage analysisApplicationsAlgorithmRisk stratificationFuture innovationsTreatment responseBrain tumorsClinical practiceClassificationRecent applicationsProcessingSignificant promiseChallengesDetection