Tomography | Top 10 Cited Papers in 2021
The 2021 top 10 cited papers from the journal Tomography (ISSN: 2379-139X) are as follows:
1. “A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC”
by Moreno, S.; Bonfante, M.; Zurek, E.; Cherezov, D.; Goldgof, D.; Hall, L.; Schabath, M.
Tomography 2021, 7(2), 154-168; https://doi.org/10.3390/tomography7020014
Full text available online: https://www.mdpi.com/2379-139X/7/2/14
2. “Non-Hemorrhagic Adrenal Infarction during Pregnancy: The Diagnostic Imaging Keys”
by Chagué, P.; Marchi, A.; Fechner, A.; Hindawi, G.; Tranchart, H.; Carrara, J.; Vivanti, A.J.; Rocher, L.
Tomography 2021, 7(4), 533-544; https://doi.org/10.3390/tomography7040046
Full text available online: https://www.mdpi.com/2379-139X/7/4/46
3. “Is It Worth Considering Multicentric High-Grade Glioma a Surgical Disease? Analysis of Our Clinical Experience and Literature Review”
by Guerrini, F.; Mazzeo, L.A.; Rossi, G.; Verlotta, M.; Del Maestro, M.; Rampini, A.D.; Pesce, A.; Viganò, M.; Luzzi, S.; Galzio, R.J.; Salmaggi, A.; Spena, G.
Tomography 2021, 7(4), 523-532; https://doi.org/10.3390/tomography7040045
Full text available online: https://www.mdpi.com/2379-139X/7/4/45
4. “Identification of Non-Traumatic Bone Marrow Oedema: The Pearls and Pitfalls of Dual-Energy CT (DECT)”
by Foti, G.; Serra, G.; Iacono, V.; Marocco, S.; Bertoli, G.; Gori, S.; Zorzi, C.
Tomography 2021, 7(3), 387-396; https://doi.org/10.3390/tomography7030034
Full text available online: https://www.mdpi.com/2379-139X/7/3/34
5. “Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer”
by Jajodia, A.; Gupta, A.; Prosch, H.; Mayerhoefer, M.; Mitra, S.; Pasricha, S.; Mehta, A.; Puri, S.; Chaturvedi, A.
Tomography 2021, 7(3), 344-357; https://doi.org/10.3390/tomography7030031
Full text available online: https://www.mdpi.com/2379-139X/7/3/31
6. “Systematic Assessment of the Impact of DTI Methodology on Fractional Anisotropy Measures in Alzheimer’s Disease”
by Bergamino, M.; Keeling, E.G.; Walsh, R.R.; Stokes, A.M.
Tomography 2021, 7(1), 20-38; https://doi.org/10.3390/tomography7010003
Full text available online: https://www.mdpi.com/2379-139X/7/1/3
7. “Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features”
by Kim, D.; Jensen, L.J.; Elgeti, T.; Steffen, I.G.; Hamm, B.; Nagel, S.N.
Tomography 2021, 7(3), 477-487; https://doi.org/10.3390/tomography7030041
Full text available online: https://www.mdpi.com/2379-139X/7/3/41
8. “Identifying Robust Radiomics Features for Lung Cancer by Using In Vivo and Phantom Lung Lesions”
by Lu, L.; Sun, S.H.; Afran, A.; Yang, H.; Lu, Z.F.; So, J.; Schwartz, L.H.; Zhao, B.
Tomography 2021, 7(1), 55-64; https://doi.org/10.3390/tomography7010005
Full text available online: https://www.mdpi.com/2379-139X/7/1/5
9. “Prediction of Disease-Free Survival in Laryngeal and Hypopharyngeal Cancers Using CT Perfusion and Radiomic Features: A Pilot Study”
by Woolen, S.; Virkud, A.; Hadjiiski, L.; Cha, K.; Chan, H.-P.; Swiecicki, P.; Worden, F.; Srinivasan, A.
Tomography 2021, 7(1), 10-19; https://doi.org/10.3390/tomography7010002
Full text available online: https://www.mdpi.com/2379-139X/7/1/2
10. “Stability of Radiomic Features across Different Region of Interest Sizes—A CT and MR Phantom Study”
by Jensen, L.J.; Kim, D.; Elgeti, T.; Steffen, I.G.; Hamm, B.; Nagel, S.N.
Tomography 2021, 7(2), 238-252; https://doi.org/10.3390/tomography7020022
Full text available online: https://www.mdpi.com/2379-139X/7/2/22