Development and validation of an artificial intelligence model for predicting post-transplant hepatocellular cancer recurrence.
(2023) Cancer communications (London, England) — Vol. 43, n° 12, p. 1381-1385 (2023)
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Lai, Quirino 
- Abstract
- DEAR EDITOR, In recent years, criteria based on the combination of morphology and biology have been proposed for improving the selection of hepatocellular cancer (HCC) patients waiting for liver transplantation (LT). Since all the proposed models showed suboptimal results in predicting the risk of post-LT recurrence, a prediction model constructed using artificial intelligence (AI) could be an attractive way to surpass this limit. Therefore, the Time_Radiological-response_Alpha-fetoproteIN_Artificial-Intelligence (TRAIN-AI) model was developed, combining morphology and biology tumor variables. [...]
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Citations
Lai, Q., De Stefano, C., Emond, J., Bhangui, P., Ikegami, T., Schaefer, B., Hoppe-Lotichius, M., Mrzljak, A., Ito, T., Vivarelli, M., Tisone, G., Agnes, S., Ettorre, G. M., Rossi, M., Tsochatzis, E., Lo, C. M., Chen, C.-L., Cillo, U., Ravaioli, M., & Lerut, J. P. (2023). Development and validation of an artificial intelligence model for predicting post-transplant hepatocellular cancer recurrence. Cancer communications (London, England), 43(12), 1381-1385. https://doi.org/10.1002/cac2.12468 (Original work published 2023)