Article Versions Notes

Tabari A, D’Amore B, Cox M, Brito S, Gee MS, Wehrenberg-Klee E, Uppot RN, Daye D. Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant. Cancers. 2023; 15(7):2058. https://doi.org/10.3390/cancers15072058

Tabari, Azadeh, Brian D’Amore, Meredith Cox, Sebastian Brito, Michael S. Gee, Eric Wehrenberg-Klee, Raul N. Uppot, and Dania Daye. 2023. "Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant" Cancers 15, no. 7: 2058. https://doi.org/10.3390/cancers15072058

Tabari, A., D’Amore, B., Cox, M., Brito, S., Gee, M. S., Wehrenberg-Klee, E., Uppot, R. N., & Daye, D. (2023). Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant. Cancers, 15(7), 2058. https://doi.org/10.3390/cancers15072058