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EJNMMI Research|July 29, 2022
Combatting the effect of image reconstruction settings on lymphoma [<sup>18</sup>F]FDG PET metabolic tumor volume assessment using various segmentation methodsMaria C Ferrández, Jakoba J Eertink, Sandeep S V Golla, et al.European Journal of Nuclear Medicine and Molecular Imaging|September 27, 2022
Reproducibility of [18F]FDG PET/CT liver SUV as reference or normalisation factorGerben J C Zwezerijnen, Jakoba J Eertink, Maria C Ferrández, et al.EJNMMI Research|September 27, 2023
Sensitivity of an AI method for [<sup>18</sup>F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocolsMaria C Ferrández, Sandeep S V Golla, Jakoba J Eertink, et al.Scientific Reports|August 12, 2023
An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patientsMaria C Ferrández, Sandeep S V Golla, Jakoba J Eertink, et al.Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine|October 3, 2024
Validation of an Artificial Intelligence-Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell LymphomaMaria C Ferrández, Sandeep S V Golla, Jakoba J Eertink, et al.EJNMMI Research|November 28, 2025
Predicting 2-year time to progression in diffuse large B cell lymphoma using 3D CNNs on whole-body PET/CT scansMaria C Ferrández, Sanne E Wiegers, Gerben J C Zwezerijnen, et al.Pageof 1