Article Versions Notes

Renzulli M, Mottola M, Coppola F, Cocozza MA, Malavasi S, Cattabriga A, Vara G, Ravaioli M, Cescon M, Vasuri F, et al. Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT). Cancers. 2022; 14(7):1816. https://doi.org/10.3390/cancers14071816

Renzulli, Matteo, Margherita Mottola, Francesca Coppola, Maria Adriana Cocozza, Silvia Malavasi, Arrigo Cattabriga, Giulio Vara, Matteo Ravaioli, Matteo Cescon, Francesco Vasuri, and et al. 2022. "Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT)" Cancers 14, no. 7: 1816. https://doi.org/10.3390/cancers14071816

Renzulli, M., Mottola, M., Coppola, F., Cocozza, M. A., Malavasi, S., Cattabriga, A., Vara, G., Ravaioli, M., Cescon, M., Vasuri, F., Golfieri, R., & Bevilacqua, A. (2022). Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT). Cancers, 14(7), 1816. https://doi.org/10.3390/cancers14071816