Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning.

Saldanha, Oliver Lester; Muti, Hannah Sophie; Grabsch, Heike I; Langer, Rupert; Dislich, Bastian; Kohlruss, Meike; Keller, Gisela; van Treeck, Marko; Hewitt, Katherine Jane; Kolbinger, Fiona R; Veldhuizen, Gregory Patrick; Boor, Peter; Foersch, Sebastian; Truhn, Daniel; Kather, Jakob Nikolas (2022). Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning. (In Press). Gastric cancer Springer 10.1007/s10120-022-01347-0

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BACKGROUND

Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL).

METHODS

Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer.

RESULTS

On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance.

CONCLUSIONS

Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Service Sector > Institute of Pathology

UniBE Contributor:

Langer, Rupert and Dislich, Bastian

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1436-3291

Publisher:

Springer

Language:

English

Submitter:

Pubmed Import

Date Deposited:

21 Oct 2022 10:20

Last Modified:

05 Dec 2022 16:26

Publisher DOI:

10.1007/s10120-022-01347-0

PubMed ID:

36264524

Uncontrolled Keywords:

Artificial intelligence Biomarker Blockchain Gastric cancer Pathology Swarm learning

BORIS DOI:

10.48350/173975

URI:

https://boris.unibe.ch/id/eprint/173975

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