Zhang, R.; Shi, K.; Hohenforst-Schmidt, W.; Steppert, C.; Sziklavari, Z.; Schmidkonz, C.; Atzinger, A.; Hartmann, A.; Vieth, M.; Förster, S.
Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma. Cancers 2023, 15, 3684.
https://doi.org/10.3390/cancers15143684
AMA Style
Zhang R, Shi K, Hohenforst-Schmidt W, Steppert C, Sziklavari Z, Schmidkonz C, Atzinger A, Hartmann A, Vieth M, Förster S.
Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma. Cancers. 2023; 15(14):3684.
https://doi.org/10.3390/cancers15143684
Chicago/Turabian Style
Zhang, Ruiyun, Kuangyu Shi, Wolfgang Hohenforst-Schmidt, Claus Steppert, Zsolt Sziklavari, Christian Schmidkonz, Armin Atzinger, Arndt Hartmann, Michael Vieth, and Stefan Förster.
2023. "Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma" Cancers 15, no. 14: 3684.
https://doi.org/10.3390/cancers15143684
APA Style
Zhang, R., Shi, K., Hohenforst-Schmidt, W., Steppert, C., Sziklavari, Z., Schmidkonz, C., Atzinger, A., Hartmann, A., Vieth, M., & Förster, S.
(2023). Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma. Cancers, 15(14), 3684.
https://doi.org/10.3390/cancers15143684
