Mena Shenouda
Staff Affiliate - YNHHCards
About
Research
Publications
2026
Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study
Lipman K, Wittenberg R, de Oliveira Taveira M, Smesseim I, Schmitz A, Boellaard T, Chupetlovska K, Abdelatty M, Petrychenko L, Jain N, Arico F, Pugliese V, Cornelissen R, Mulders T, Zwierenga F, Hiddinga B, Shenouda M, Armato S, Popat S, Peters S, Roux S, Zellweger C, Fontecedro A, Frauenfelder T, Nguyen-Kim T, Beets-Tan R, Baas P, Dungey M, Bajaj A, Fennell D, Tissier R, Burgers J, de Gooijer C, Trebeschi S. Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study. The Lancet Oncology 2026, 27: 879-892. PMID: 42309108, DOI: 10.1016/s1470-2045(26)00084-7.Peer-Reviewed Original Research
2024
Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs
Shenouda M, Whitney HM, Giger ML, Armato SG III, Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs, J. Med. Imag. 11(6), 064503 (2024), doi: 10.1117/1.JMI.11.6.064503.Peer-Reviewed Original ResearchRadiomics for differentiation of somatic BAP1 mutation on CT scans of patients with pleural mesothelioma
Shenouda M, Shaikh A, Deutsch I, Mitchell O, Kindler HL, Armato SG III, Radiomics for differentiation of somatic BAP1 mutation on CT scans of patients with pleural mesothelioma, J. Med. Imag. 11(6), 064501 (2024), doi: 10.1117/1.JMI.11.6.064501.Peer-Reviewed Original ResearchRadiomics for differentiation of somatic BAP1 mutation on CT scans of patients with pleural mesothelioma
Shenouda M, Shaikh A, Deutsch I, Mitchell O, Kindler H, Armato S. Radiomics for differentiation of somatic BAP1 mutation on CT scans of patients with pleural mesothelioma. Journal Of Medical Imaging 2024, 11: 064501-064501. PMID: 39669009, PMCID: PMC11633667, DOI: 10.1117/1.jmi.11.6.064501.Peer-Reviewed Original ResearchImpact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs
Shenouda M, Whitney H, Giger M, Armato S. Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs. Journal Of Medical Imaging 2024, 11: 064503-064503. PMID: 39734609, PMCID: PMC11670362, DOI: 10.1117/1.jmi.11.6.064503.Peer-Reviewed Original ResearchConvolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance)
Shenouda M, Gudmundsson E, Li F, Straus C, Kindler H, Dudek A, Stinchcombe T, Wang X, Starkey A, Armato III S. Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance). Journal Of Imaging Informatics In Medicine 2024, 38: 967-978. PMID: 39266911, PMCID: PMC11950581, DOI: 10.1007/s10278-024-01092-z.Peer-Reviewed Original ResearchThe use of radiomics on computed tomography scans for differentiation of somatic BAP1 mutation status for patients with pleural mesothelioma
Shenouda M, Shaikh A, Deutsch I, Mitchell O, Kindler H, Armato S. The use of radiomics on computed tomography scans for differentiation of somatic BAP1 mutation status for patients with pleural mesothelioma. Progress In Biomedical Optics And Imaging 2024, 12927: 1292732-1292732-9. DOI: 10.1117/12.3000085.Peer-Reviewed Original ResearchAssessing radiomic feature robustness using agreement over image perturbation
Shaikh A, Deutsch I, Shenouda M, Mitchell O, Kindler H, Armato S. Assessing radiomic feature robustness using agreement over image perturbation. Progress In Biomedical Optics And Imaging 2024, 12927: 129272z-129272z-7. DOI: 10.1117/12.3006291.Peer-Reviewed Original Research
2023
Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors
Shenouda M, Flerlage I, Kaveti A, Giger M, Armato S. Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors. Journal Of Medical Imaging 2023, 10: 064504-064504. PMID: 38162317, PMCID: PMC10753846, DOI: 10.1117/1.jmi.10.6.064504.Peer-Reviewed Original ResearchAssessing robustness of a deep-learning model for COVID-19 classification on chest radiographs
Shenouda M, Kaveti A, Flerlage I, Kalpathy-Cramer J, Giger M, Armato S. Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs. Progress In Biomedical Optics And Imaging 2023, 12465: 124650f-124650f-6. DOI: 10.1117/12.2652106.Peer-Reviewed Original Research