Development an validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

Hannah Sophie Muti; Larai Rosaline Heij; Gisela Keller; Meike Kohlruss; Rupert Langer; B. Dislich; Jae-Ho Cheong; Young-Woo Kim; Hyunki Kim; Myeong-Cherl Kook; David Cunningham; William H. Allum; Ruth E. Langley; Matthew G. Nankivell; Philip Quirke; Jeremy D. Hayden; Nicholas P. West; Andrew J. Invine; Takaka Yoshikawa; Takashi Oshima; R. Huss; Bianca Grosser; Franco Roviello; Alessia d`Ignazio; Alexander Quass; Hakan Alakus; Tan Xiuxiang; Alexander T. Pearson; Tom Luedde; Matthias P. Ebert; Dirk Jäger; Christian Trautwein; Nadine Therese Gaisa; Heike I. Grabsch; Jakob Nikolas Kather

doi:10.1016/S2589-7500(21)00133-3

Development an validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

Hannah Sophie Muti, Larai Rosaline Heij, Gisela Keller, Meike Kohlruss, Rupert Langer, B. Dislich, Jae-Ho Cheong, Young-Woo Kim, Hyunki Kim, Myeong-Cherl Kook, David Cunningham, William H. Allum, Ruth E. Langley, Matthew G. Nankivell, Philip Quirke, Jeremy D. Hayden, Nicholas P. West, Andrew J. Invine, Takaka Yoshikawa, Takashi OshimaR. Huss, Bianca Grosser, Franco Roviello, Alessia d`Ignazio, Alexander Quass, Hakan Alakus, Tan Xiuxiang, Alexander T. Pearson, Tom Luedde, Matthias P. Ebert, Dirk Jäger, Christian Trautwein, Nadine Therese Gaisa, Heike I. Grabsch, Jakob Nikolas Kather

Department of Pathology and Molecular Pathology

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	654-664
Number of pages	10
Journal	The Lancet Digital Health
Volume	3
Issue number	10
DOIs	https://doi.org/10.1016/S2589-7500(21)00133-3
Publication status	Published - 2021

Fields of science

301101 General pathology
301108 Molecular pathology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/S2589-7500(21)00133-3

Cite this

Muti, H. S., Heij, L. R., Keller, G., Kohlruss, M., Langer, R., Dislich, B., Cheong, J.-H., Kim, Y.-W., Kim, H., Kook, M.-C., Cunningham, D., Allum, W. H., Langley, R. E., Nankivell, M. G., Quirke, P., Hayden, J. D., West, N. P., Invine, A. J., Yoshikawa, T., ... Kather, J. N. (2021). Development an validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. The Lancet Digital Health, 3(10), 654-664. https://doi.org/10.1016/S2589-7500(21)00133-3

@article{06d910dbb0e5444e8f1bf89fac39ce48,

title = "Development an validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study",

author = "Muti, {Hannah Sophie} and Heij, {Larai Rosaline} and Gisela Keller and Meike Kohlruss and Rupert Langer and B. Dislich and Jae-Ho Cheong and Young-Woo Kim and Hyunki Kim and Myeong-Cherl Kook and David Cunningham and Allum, {William H.} and Langley, {Ruth E.} and Nankivell, {Matthew G.} and Philip Quirke and Hayden, {Jeremy D.} and West, {Nicholas P.} and Invine, {Andrew J.} and Takaka Yoshikawa and Takashi Oshima and R. Huss and Bianca Grosser and Franco Roviello and Alessia d`Ignazio and Alexander Quass and Hakan Alakus and Tan Xiuxiang and Pearson, {Alexander T.} and Tom Luedde and Ebert, {Matthias P.} and Dirk J{\"a}ger and Christian Trautwein and Gaisa, {Nadine Therese} and Grabsch, {Heike I.} and Kather, {Jakob Nikolas}",

year = "2021",

doi = "10.1016/S2589-7500(21)00133-3",

language = "English",

volume = "3",

pages = "654--664",

journal = "The Lancet Digital Health",

issn = "2589-7500",

publisher = "Elsevier Ltd.",

number = "10",

}

Muti, HS, Heij, LR, Keller, G, Kohlruss, M, Langer, R, Dislich, B, Cheong, J-H, Kim, Y-W, Kim, H, Kook, M-C, Cunningham, D, Allum, WH, Langley, RE, Nankivell, MG, Quirke, P, Hayden, JD, West, NP, Invine, AJ, Yoshikawa, T, Oshima, T, Huss, R, Grosser, B, Roviello, F, d`Ignazio, A, Quass, A, Alakus, H, Xiuxiang, T, Pearson, AT, Luedde, T, Ebert, MP, Jäger, D, Trautwein, C, Gaisa, NT, Grabsch, HI & Kather, JN 2021, 'Development an validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study', The Lancet Digital Health, vol. 3, no. 10, pp. 654-664. https://doi.org/10.1016/S2589-7500(21)00133-3

Development an validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. / Muti, Hannah Sophie; Heij, Larai Rosaline; Keller, Gisela et al.
In: The Lancet Digital Health, Vol. 3, No. 10, 2021, p. 654-664.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Development an validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

AU - Muti, Hannah Sophie

AU - Heij, Larai Rosaline

AU - Keller, Gisela

AU - Kohlruss, Meike

AU - Langer, Rupert

AU - Dislich, B.

AU - Cheong, Jae-Ho

AU - Kim, Young-Woo

AU - Kim, Hyunki

AU - Kook, Myeong-Cherl

AU - Cunningham, David

AU - Allum, William H.

AU - Langley, Ruth E.

AU - Nankivell, Matthew G.

AU - Quirke, Philip

AU - Hayden, Jeremy D.

AU - West, Nicholas P.

AU - Invine, Andrew J.

AU - Yoshikawa, Takaka

AU - Oshima, Takashi

AU - Huss, R.

AU - Grosser, Bianca

AU - Roviello, Franco

AU - d`Ignazio, Alessia

AU - Quass, Alexander

AU - Alakus, Hakan

AU - Xiuxiang, Tan

AU - Pearson, Alexander T.

AU - Luedde, Tom

AU - Ebert, Matthias P.

AU - Jäger, Dirk

AU - Trautwein, Christian

AU - Gaisa, Nadine Therese

AU - Grabsch, Heike I.

AU - Kather, Jakob Nikolas

PY - 2021

Y1 - 2021

U2 - 10.1016/S2589-7500(21)00133-3

DO - 10.1016/S2589-7500(21)00133-3

M3 - Article

SN - 2589-7500

VL - 3

SP - 654

EP - 664

JO - The Lancet Digital Health

JF - The Lancet Digital Health

IS - 10

ER -