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Predicting pathological highly invasive lung cancer from pr..:
Onozato, Yuki
;
Iwata, Takekazu
;
Uematsu, Yasufumi
...
European Journal of Nuclear Medicine and Molecular Imaging. 50 (2022) 3 - p. 715-726 , 2022
Link:
https://doi.org/10.1007/s00259-022-06038-7
RT Journal T1 <Title>Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.1007_s00259-022-06038-7&Exemplar=1&LAN=DE A1 Onozato, Yuki A1 Iwata, Takekazu A1 Uematsu, Yasufumi A1 Shimizu, Daiki A1 Yamamoto, Takayoshi A1 Matsui, Yukiko A1 Ogawa, Kazuyuki A1 Kuyama, Junpei A1 Sakairi, Yuichi A1 Kawakami, Eiryo A1 Iizasa, Toshihiko A1 Yoshino, Ichiro PB Springer Science and Business Media LLC YR 2022 SN 1619-7070 SN 1619-7089 JF European Journal of Nuclear Medicine and Molecular Imaging VO 50 IS 3 SP 715 OP 726 LK http://dx.doi.org/https://doi.org/10.1007/s00259-022-06038-7 DO https://doi.org/10.1007/s00259-022-06038-7 SF ELIB - SuUB Bremen
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