2022
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 Research
2020
The value of interventional radiology in clinical trial teams: experience from the BATTLE lung cancer trials
Tam AL, Papadimitrakopoulou V, Wistuba II, Lee JJ, Ensor JE, Kim ES, Kalhor N, Blumenschein GR, Tsao AS, Heymach JV, Herbst RS, Hicks ME, Hong WK, Gupta S. The value of interventional radiology in clinical trial teams: experience from the BATTLE lung cancer trials. Clinical Radiology 2020, 76: 155.e25-155.e34. PMID: 33268083, DOI: 10.1016/j.crad.2020.09.024.Peer-Reviewed Original ResearchConceptsInterventional radiologistsClinical trial teamsTrial teamPercutaneous image-guided biopsyLung Cancer EliminationLung cancer trialsEvidence of viabilityImage-guided biopsyLesion scoring systemBiomarker analysisCancer trialsTargeted therapyCancer eliminationScore's abilityInterventional radiologyMultivariate analysisLesionsBiopsySolid massFollowing criteriaModerate agreementMultidisciplinary approachTrialsScoresBiomarkers