14P Deep learning-based quantification of immune infiltrate for predicting response to pembrolizumab from pre-treatment biopsies of metastatic non-small cell lung cancer: A study on the PEMBRO-RT phase II trial. (December 2022)

Record Type:: Journal Article
Title:: 14P Deep learning-based quantification of immune infiltrate for predicting response to pembrolizumab from pre-treatment biopsies of metastatic non-small cell lung cancer: A study on the PEMBRO-RT phase II trial. (December 2022)
Main Title:: 14P Deep learning-based quantification of immune infiltrate for predicting response to pembrolizumab from pre-treatment biopsies of metastatic non-small cell lung cancer: A study on the PEMBRO-RT phase II trial
Authors:: Spronck, J.
Eekelen, L.V.
Tessier, L.
Bogaerts, J.
van der Woude, L.
van den Heuvel, M.
Theelen, W.
Ciompi, F.
Abstract:
Is Part Of:: Immuno-oncology technology. Volume 16(2022)Supplement 1
Journal:: Immuno-oncology technology
Issue:: Volume 16(2022)Supplement 1
Issue Display:: Volume 16, Issue 1 (2022)
Year:: 2022
Volume:: 16
Issue:: 1
Issue Sort Value:: 2022-0016-0001-0000
Page Start:
Page End:
Publication Date:: 2022-12
Subjects:: Cancer -- Immunotherapy -- Periodicals
616.994061
Journal URLs:: https://www.esmoiotech.org/issues ↗
http://www.sciencedirect.com/ ↗
DOI:: 10.1016/j.iotech.2022.100119 ↗
Languages:: English
ISSNs:: 2590-0188
Deposit Type:: Legaldeposit
View Content:: Available online (eLD content is only available in our Reading Rooms) ↗
Physical Locations:: British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store
Ingest File:: 24685.xml