Announcements

1. “Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets”
by McNitt-Gray, M.; Napel, S.; Jaggi, A.; Mattonen, S. A.; Hadjiiski, L.; Muzi, M.; Goldgof, D.; Balagurunathan, Y.; Pierce, L.A.; Kinahan, P. E. et al.
Tomography 2020, 6(2), 118-128. https://doi.org/10.18383/j.tom.2019.00031
Available online: https://www.mdpi.com/2379-139X/6/2/118

2. “A Fully Automated Deep Learning Network for Brain Tumor Segmentation”
by Yogananda, C. G. B.; Shah, B. R.; Vejdani-Jahromi, M.; Nalawade, S. S.; Murugesan, G. K.; Yu, F. F.; Pinho, M. C.; Wagner, B. C.; Emblem, K. E.; Bjørnerud, A. et al.
Tomography 2020, 6(2), 186-193; https://doi.org/10.18383/j.tom.2019.00026
Available online: https://www.mdpi.com/2379-139X/6/2/186

3. “A Perspective on Cell Tracking with Magnetic Particle Imaging”
by Sehl, O. C.; Gevaert, J. J.; Melo, K. P.; Knier, N. N. and Foster, P. J.
Tomography 2020, 6(4), 315-324; https://doi.org/10.18383/j.tom.2020.00043
Available online: https://www.mdpi.com/2379-139X/6/4/315

4. “Radiomics Prediction of EGFR Status in Lung Cancer—Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data”
by Lu, L.; Sun, S.H.; Yang, H.; E, L.; Guo, P.; Schwartz, L. H. and Zhao, B.
Tomography 2020, 6(2), 223-230; https://doi.org/10.18383/j.tom.2020.00017
Available online: https://www.mdpi.com/2379-139X/6/2/223

5. “Radiomic Features of Multiparametric MRI Present Stable Associations with Analogous Histological Features in Patients with Brain Cancer”
by Bobholz, S. A.; Lowman, A. K.; Barrington, A.; Brehler, M.; McGarry, S.; Cochran, E. J.; Connelly, J.; Mueller, W. M.; Agarwal, M.; O'Neill, D. et al.
Tomography 2020, 6(2), 160-169; https://doi.org/10.18383/j.tom.2019.00029
Available online: https://www.mdpi.com/2379-139X/6/2/160

6. “MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice”
by Holbrook, M. D.; Blocker, S. J.; Mowery, Y. M.; Badea, A.; Qi, Y.; Xu, E. S.; Kirsch, D. G.; Johnson, G. A. and Badea, C. T.
Tomography 2020, 6(1), 23-33; https://doi.org/10.18383/j.tom.2019.00021
Available online: https://www.mdpi.com/2379-139X/6/1/23

7. “Assessment of the Prognostic Value of Radiomic Features in 18F-FMISO PET Imaging of Hypoxia in Postsurgery Brain Cancer Patients: Secondary Analysis of Imaging Data from a Single-Center Study and the Multicenter ACRIN 6684 Trial”
by Muzi, M.; Wolsztynski, E.; Fink, J. R.; O’Sullivan, J. N.; O’Sullivan, F.; Krohn, K. A. and Mankoff, D. A.
Tomography 2020, 6(1), 14-22; https://doi.org/10.18383/j.tom.2019.00023
Available online: https://www.mdpi.com/2379-139X/6/1/14

8. “Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support”
by Hadjiiski, L. M.; Cha, K. H.; Cohan, R. H.; Chan, H.-P.; Caoili, E. M.; Davenport, M. S.; Samala, R. K.; Weizer, A. Z.; Alva, A.; Kirova-Nedyalkova, G. et al.
Tomography 2020, 6(2), 194-202; https://doi.org/10.18383/j.tom.2020.00013
Available online: https://www.mdpi.com/2379-139X/6/2/194

9. “Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features”
by Jaggi, A.; Mattonen, S. A.; McNitt-Gray, M. and Napel, S.
Tomography 2020, 6(2), 111-117; https://doi.org/10.18383/j.tom.2019.00030
Available online: https://www.mdpi.com/2379-139X/6/2/111

10. “Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis”
by LoCastro, E.; Paudyal, R.; Mazaheri, Y.; Hatzoglou, V.; Oh, J. H.; Lu, Y.; Konar, A. S.; Eigen, K.v.; Ho, A.; Ewing, J. R. et al.
Tomography 2020, 6(2), 129-138; https://doi.org/10.18383/j.tom.2020.00005
Available online: https://www.mdpi.com/2379-139X/6/2/129