Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Author:

Wagner Sophia J.,Reisenbüchler Daniel,West Nicholas P.,Niehues Jan Moritz,Zhu Jiefu,Foersch Sebastian,Veldhuizen Gregory Patrick,Quirke Philip,Grabsch Heike I.,van den Brandt Piet A.,Hutchins Gordon G.A.,Richman Susan D.,Yuan Tanwei,Langer Rupert,Jenniskens Josien C.A.,Offermans Kelly,Mueller Wolfram,Gray Richard,Gruber Stephen B.,Greenson Joel K.,Rennert Gad,Bonner Joseph D.,Schmolze Daniel,Jonnagaddala Jitendra,Hawkins Nicholas J.,Ward Robyn L.,Morton Dion,Seymour Matthew,Magill Laura,Nowak Marta,Hay Jennifer,Koelzer Viktor H.,Church David N.,Matek Christian,Geppert Carol,Peng Chaolong,Zhi Cheng,Ouyang Xiaoming,James Jacqueline A.,Loughrey Maurice B.,Salto-Tellez Manuel,Brenner Hermann,Hoffmeister Michael,Truhn Daniel,Schnabel Julia A.,Boxberg Melanie,Peng Tingying,Kather Jakob Nikolas,Church David,Domingo Enric,Edwards Joanne,Glimelius Bengt,Gogenur Ismail,Harkin Andrea,Hay Jen,Iveson Timothy,Jaeger Emma,Kelly Caroline,Kerr Rachel,Maka Noori,Morgan Hannah,Oien Karin,Orange Clare,Palles Claire,Roxburgh Campbell,Sansom Owen,Saunders Mark,Tomlinson Ian

Publisher

Elsevier BV

Subject

Cancer Research,Oncology

Reference71 articles.

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2. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer;Cao;Theranostics,2020

3. Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning;Echle;Gastroenterology,2020

4. Bilal, M., Ahmed Raza, S.E., Azam, A., Graham, S., Ilyas, M., Cree, I.A., Snead, D., Minhas, F., and Rajpoot, N.M. Novel Deep Learning Algorithm Predicts the Status of Molecular Pathways and Key Mutations in Colorectal Cancer from Routine Histology Images. 10.1101/2021.01.19.21250122

5. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer;Lee;Int. J. Cancer,2021

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