Cite
Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling.
MLA
Jia, Tian-Ying, et al. “Identifying EGFR Mutations in Lung Adenocarcinoma by Noninvasive Imaging Using Radiomics Features and Random Forest Modeling.” European Radiology, vol. 29, no. 9, Sept. 2019, pp. 4742–50. EBSCOhost, https://doi.org/10.1007/s00330-019-06024-y.
APA
Jia, T.-Y., Xiong, J.-F., Li, X.-Y., Yu, W., Xu, Z.-Y., Cai, X.-W., Ma, J.-C., Ren, Y.-C., Larsson, R., Zhang, J., Zhao, J., & Fu, X.-L. (2019). Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. European Radiology, 29(9), 4742–4750. https://doi.org/10.1007/s00330-019-06024-y
Chicago
Jia, Tian-Ying, Jun-Feng Xiong, Xiao-Yang Li, Wen Yu, Zhi-Yong Xu, Xu-Wei Cai, Jing-Chen Ma, et al. 2019. “Identifying EGFR Mutations in Lung Adenocarcinoma by Noninvasive Imaging Using Radiomics Features and Random Forest Modeling.” European Radiology 29 (9): 4742–50. doi:10.1007/s00330-019-06024-y.