A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)
Abstract
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Garnier, C.; Ferrer, L.; Vargas, J.; Gallinato, O.; Jambon, E.; Le Bras, Y.; Bernhard, J.-C.; Colin, T.; Grenier, N.; Marcelin, C. A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics 2023, 13, 2548. https://doi.org/10.3390/diagnostics13152548
Garnier C, Ferrer L, Vargas J, Gallinato O, Jambon E, Le Bras Y, Bernhard J-C, Colin T, Grenier N, Marcelin C. A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics. 2023; 13(15):2548. https://doi.org/10.3390/diagnostics13152548
Chicago/Turabian StyleGarnier, Cassandre, Loïc Ferrer, Jennifer Vargas, Olivier Gallinato, Eva Jambon, Yann Le Bras, Jean-Christophe Bernhard, Thierry Colin, Nicolas Grenier, and Clément Marcelin. 2023. "A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)" Diagnostics 13, no. 15: 2548. https://doi.org/10.3390/diagnostics13152548
APA StyleGarnier, C., Ferrer, L., Vargas, J., Gallinato, O., Jambon, E., Le Bras, Y., Bernhard, J.-C., Colin, T., Grenier, N., & Marcelin, C. (2023). A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics, 13(15), 2548. https://doi.org/10.3390/diagnostics13152548