An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening

MLA

Yong Li, et al. “An Ordinal Radiomic Model to Predict the Differentiation Grade of Invasive Non-Mucinous Pulmonary Adenocarcinoma Based on Low-Dose Computed Tomography in Lung Cancer Screening.” European Radiology, vol. 33, Feb. 2023, pp. 3072–82. EBSCOhost, https://doi.org/10.1007/s00330-023-09453-y.

APA

Yong Li, Jieke Liu, Xi Yang, Ai Wang, Chi Zang, Lu Wang, Changjiu He, Libo Lin, Haomiao Qing, Jing Ren, & Peng Zhou. (2023). An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening. European Radiology, 33, 3072–3082. https://doi.org/10.1007/s00330-023-09453-y

Chicago

Yong Li, Jieke Liu, Xi Yang, Ai Wang, Chi Zang, Lu Wang, Changjiu He, et al. 2023. “An Ordinal Radiomic Model to Predict the Differentiation Grade of Invasive Non-Mucinous Pulmonary Adenocarcinoma Based on Low-Dose Computed Tomography in Lung Cancer Screening.” European Radiology 33 (February): 3072–82. doi:10.1007/s00330-023-09453-y.