Ni, B.; Huang, G.; Huang, H.; Wang, T.; Han, X.; Shen, L.; Chen, Y.; Hou, J.
Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study. J. Clin. Med. 2023, 12, 2280.
https://doi.org/10.3390/jcm12062280
AMA Style
Ni B, Huang G, Huang H, Wang T, Han X, Shen L, Chen Y, Hou J.
Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study. Journal of Clinical Medicine. 2023; 12(6):2280.
https://doi.org/10.3390/jcm12062280
Chicago/Turabian Style
Ni, Beiwen, Gan Huang, Honghui Huang, Ting Wang, Xiaofeng Han, Lijing Shen, Yumei Chen, and Jian Hou.
2023. "Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study" Journal of Clinical Medicine 12, no. 6: 2280.
https://doi.org/10.3390/jcm12062280
APA Style
Ni, B., Huang, G., Huang, H., Wang, T., Han, X., Shen, L., Chen, Y., & Hou, J.
(2023). Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study. Journal of Clinical Medicine, 12(6), 2280.
https://doi.org/10.3390/jcm12062280