Definition and validation of a radiomics signature for loco-regional tumour control in patients with locally advanced head and neck squamous cell carcinoma

Autor: Rabasco Meneghetti, Asier, Zwanenburg, Alex, Leger, Stefan, Leger, Karoline, Troost, Esther G.C., Linge, Annett, Lohaus, Fabian, Schreiber, Andreas, Kalinauskaite, Goda, Tinhofer, Inge, Guberina, Nika, Guberina, Maja, Balermpas, Panagiotis, von der Grün, Jens, Ganswindt, Ute, Belka, Claus, Peeken, Jan C., Combs, Stephanie E., Böke, Simon, Zips, Daniel, Krause, Mechthild, Baumann, Michael, Löck, Steffen

Publikováno v: In Clinical and Translational Radiation Oncology January 2021 26:62-70

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2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma

Autor: Starke, Sebastian, Leger, Stefan, Zwanenburg, Alex, Leger, Karoline, Lohaus, Fabian, Linge, Annett, Schreiber, Andreas, Kalinauskaite, Goda, Tinhofer, Inge, Guberina, Nika, Guberina, Maja, Balermpas, Panagiotis, von der Grün, Jens, Ganswindt, Ute, Belka, Claus, Peeken, Jan C, Combs, Stephanie E, Boeke, Simon, Zips, Daniel, Richter, Christian, Troost, Esther G C, Krause, Mechthild, Baumann, Michael, Löck, Steffen

Publikováno v: Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
Sci. Rep. 10:15625 (2020)
Scientific Reports 10(2020)1, 15625
Publication date: 2020-02-27 Open accessDOI: 10.14278/rodare.254Versions: 10.14278/rodare.255License: CC-BY-NC-4.0

For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::5449af28dd00114049650500197d4976
https://pubmed.ncbi.nlm.nih.gov/32973220

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