Featured Publications
Using 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 ResearchMeSH KeywordsBreastDeep LearningHumansMagnetic Resonance ImagingMammographyTomography, X-Ray Computed
2023
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
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 ResearchMeSH KeywordsAlgorithmsCarcinomaDeep LearningExtranodal ExtensionHuman Papillomavirus VirusesHumansOropharyngeal NeoplasmsPapillomavirus InfectionsRetrospective StudiesTomography, X-Ray ComputedConceptsExtranodal 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 protocol
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 Atlas
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