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1 Ergebnisse
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CT Radiomics and Machine-Learning Models for Predicting Tum..:
Yinghao Meng
;
Hao Zhang
;
Qi Li
...
https://www.frontiersin.org/articles/10.3389/fonc.2021.707288/full. , 2021
Link:
https://doi.org/10.3389/fonc.2021.707288
RT Journal T1 <Title>CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:97a9274c27c04a598c8643a72ca74334&Exemplar=1&LAN=DE A1 Yinghao Meng A1 Hao Zhang A1 Qi Li A1 Fang Liu A1 Xu Fang A1 Jing Li A1 Jieyu Yu A1 Xiaochen Feng A1 Mengmeng Zhu A1 Na Li A1 Guodong Jing A1 Li Wang A1 Chao Ma A1 Jianping Lu A1 Yun Bian A1 Chengwei Shao PB Frontiers Media S.A. YR 2021 K1 pancreatic neoplasm K1 carcinoma K1 prognosis K1 tumor-stroma ratio K1 multidetector computed tomography K1 radiomics K1 Neoplasms. Tumors. Oncology. Including cancer and carcinogens K1 RC254-282 JF https://www.frontiersin.org/articles/10.3389/fonc.2021.707288/full LK http://dx.doi.org/https://doi.org/10.3389/fonc.2021.707288 DO https://doi.org/10.3389/fonc.2021.707288 SF ELIB - SuUB Bremen
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