1. “Stability of Radiomic Features across Different Region of Interest Sizes—A CT and MR Phantom Study”
by Laura J. Jensen, Damon Kim, Thomas Elgeti, Ingo G. Steffen, Bernd Hamm and Sebastian N. Nagel
Tomography 2021, 7(2), 238–252; https://doi.org/10.3390/tomography7020022
Available online: https://www.mdpi.com/2379-139X/7/2/22

2. “EDNC: Ensemble Deep Neural Network for COVID-19 Recognition”
by Lin Yang, Shui-Hua Wang and Yu-Dong Zhang
Tomography 2022, 8(2), 869–890; https://doi.org/10.3390/tomography8020071
Available online: https://www.mdpi.com/2379-139X/8/2/71

3. “Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning”
by Matthew D. Holbrook, Darin P. Clark, Rutulkumar Patel, Yi Qi, Alex M. Bassil, Yvonne M. Mowery and Cristian T. Badea
Tomography 2021, 7(3), 358–372; https://doi.org/10.3390/tomography7030032
Available online: https://www.mdpi.com/2379-139X/7/3/32

4. “A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC”
by Silvia Moreno, Mario Bonfante, Eduardo Zurek, Dmitry Cherezov, Dmitry Goldgof, Lawrence Hall and Matthew Schabath
Tomography 2021, 7(2), 154–168; https://doi.org/10.3390/tomography7020014
Available online: https://www.mdpi.com/2379-139X/7/2/14

5. “Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans”
by Annette Erle, Sobhan Moazemi, Susanne Lütje, Markus Essler, Thomas Schultz and Ralph A. Bundschuh
Tomography 2021, 7(3), 301–312; https://doi.org/10.3390/tomography7030027
Available online: https://www.mdpi.com/2379-139X/7/3/27

6. “Honorary Authorship: Is There Any Chance to Stop It? Analysis of the Literature and a Personal Opinion”
by Emilio Quaia and Filippo Crimi’
Tomography 2021, 7(4), 801–803; https://doi.org/10.3390/tomography7040067
Available online: https://www.mdpi.com/2379-139X/7/4/67

7. “Evaluation of Regional Lung Function in Pulmonary Fibrosis with Xenon-129 MRI”
by Jaime Mata, Steven Guan, Kun Qing, Nicholas Tustison, Yun Shim, John P. Mugler III, Talissa Altes, Jhosep Huaromo and Borna Mehrad
Tomography 2021, 7(3), 452–465; https://doi.org/10.3390/tomography7030039
Available online: https://www.mdpi.com/2379-139X/7/3/39

8. “Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer”
by Kanae Takahashi, Tomoyuki Fujioka, Jun Oyama, Mio Mori, Emi Yamaga, Yuka Yashima, Tomoki Imokawa, Atsushi Hayashi, Yu Kujiraoka, Junichi Tsuchiya et al.
Tomography 2022, 8(1), 131–141; https://doi.org/10.3390/tomography8010011
Available online: https://www.mdpi.com/2379-139X/8/1/11

9. “Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer”
by Ankush Jajodia, Ayushi Gupta, Helmut Prosch, Marius Mayerhoefer, Swarupa Mitra, Sunil Pasricha, Anurag Mehta, Sunil Puri and Arvind Chaturvedi
Tomography 2021, 7(3), 344–357; https://doi.org/10.3390/tomography7030031
Available online: https://www.mdpi.com/2379-139X/7/3/31

10. “Systematic Assessment of the Impact of DTI Methodology on Fractional Anisotropy Measures in Alzheimer’s Disease”
by Maurizio Bergamino, Elizabeth G. Keeling, Ryan R. Walsh and Ashley M. Stokes
Tomography 2021, 7(1), 20–38; https://doi.org/10.3390/tomography7010003
Available online: https://www.mdpi.com/2379-139X/7/1/3