Gómez, O.V.; Herraiz, J.L.; UdÃas, J.M.; Haug, A.; Papp, L.; Cioni, D.; Neri, E.
Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions. Cancers 2022, 14, 2922.
https://doi.org/10.3390/cancers14122922
AMA Style
Gómez OV, Herraiz JL, UdÃas JM, Haug A, Papp L, Cioni D, Neri E.
Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions. Cancers. 2022; 14(12):2922.
https://doi.org/10.3390/cancers14122922
Chicago/Turabian Style
Gómez, Ober Van, Joaquin L. Herraiz, José Manuel UdÃas, Alexander Haug, Laszlo Papp, Dania Cioni, and Emanuele Neri.
2022. "Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions" Cancers 14, no. 12: 2922.
https://doi.org/10.3390/cancers14122922
APA Style
Gómez, O. V., Herraiz, J. L., UdÃas, J. M., Haug, A., Papp, L., Cioni, D., & Neri, E.
(2022). Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions. Cancers, 14(12), 2922.
https://doi.org/10.3390/cancers14122922