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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

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.

Details

OriginalspracheEnglisch
Seiten (von - bis)264-274
Seitenumfang11
FachzeitschriftGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Jahrgang26 (2023)
Ausgabenummer2
PublikationsstatusVeröffentlicht - 20 Okt. 2022
Peer-Review-StatusJa

Externe IDs

PubMedCentral PMC9950158
Scopus 85140332811
WOS 000870673500001
Mendeley 904acdad-9a3c-3bf1-bbf5-e16e42275f55
ORCID /0000-0003-2265-4809/work/149798322
PubMed 36264524

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Artificial intelligence, Biomarker, Blockchain, Gastric cancer, Pathology, Swarm learning

Bibliotheksschlagworte