TY  - JOUR
AU  - Echle, Amelie
AU  - Grabsch, Heike Irmgard
AU  - Quirke, Philip
AU  - van den Brandt, Piet A
AU  - West, Nicholas P
AU  - Hutchins, Gordon G A
AU  - Heij, Lara R
AU  - Tan, Xiuxiang
AU  - Richman, Susan D
AU  - Krause, Jeremias
AU  - Alwers, Elizabeth
AU  - Jenniskens, Josien
AU  - Offermans, Kelly
AU  - Gray, Richard
AU  - Brenner, Hermann
AU  - Chang-Claude, Jenny
AU  - Trautwein, Christian
AU  - Pearson, Alexander T
AU  - Boor, Peter
AU  - Luedde, Tom
AU  - Gaisa, Nadine Therese
AU  - Hoffmeister, Michael
AU  - Nikolas Kather, Jakob
TI  - Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.
JO  - Gastroenterology
VL  - 159
IS  - 4
SN  - 0016-5085
CY  - Philadelphia, Pa. [u.a.]
PB  - Saunders
M1  - DKFZ-2020-01144
SP  - 1406-1416.e11
PY  - 2020
N1  - 2020 Oct;159(4):1406-1416.e11
AB  - Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and cheaper than molecular assays. But clinical application of this technology requires high performance and multisite validation, which have not yet been performed.We collected hematoxylin and eosin-stained slides, and findings from molecular analyses for MSI and dMMR, from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (n=6406 specimens) and validated in an external cohort (n=771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC).The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound 0.91, upper bound 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67
LB  - PUB:(DE-HGF)16
C6  - pmid:32562722
DO  - DOI:10.1053/j.gastro.2020.06.021
UR  - https://inrepo02.dkfz.de/record/156827
ER  -