- Titel
- Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology
- Untertitel
- a retrospective multicentric study
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-974906
- Quellenangabe
- European journal of cancer
Erscheinungsjahr: 2023
Jahrgang: 194
E-ISSN: 1879-0852
Artikelnummer: 113335 - Erstveröffentlichung
- 2023
- Abstract (EN)
- Aim Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL). Methods Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin–stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. Results The aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests. Conclusion GC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der Zeitschrift „European journal of cancer” im Verlag Elsevier erschienen ist.
DOI: 10.1016/j.ejca.2023.113335 - Verweis
- Ergänzendes Material ist unter folgenden Links zu finden.
Link: https://www.sciencedirect.com/science/article/pii/S0959804923006378#sec0105 - Freie Schlagwörter (DE)
- Gastric cancer, Digital pathology, Deep learning, Precision oncology, Artificial intelligence
- Klassifikation (DDC)
- 610
- Verlag
- Elsevier, Amsterdam
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-974906
- Veröffentlichungsdatum Qucosa
- 06.11.2025
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0