PuSH - Publikationsserver des Helmholtz Zentrums München: Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

Informationen

Hinweise zu Qualitätskriterien von Zeitschriften

Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016

CC Licencences

Metriken für Publikationen

Navigation

Startseite

English

EVA: Veröffentlichen

Neuer EVA Antrag

Recherche

Erweiterte Suche

Durchblättern nach ...

... HMGU-Autoren/Konsortien

... Organisationsstruktur

... Zeitschriften

... Publikationstypen

... Forschungsdaten

... Arbeitsgruppen

... Erscheinungsjahr

Publikationen im Überblick

Statistik

HGF Fortschrittsbericht

OA Publikationen

Eintragen

Neue Publikation eintragen

Neue Publikation holen aus...

...EVA

Fehlende Publikation melden

Highlights

Suche

Hilfe & Kontakt

Ansprechpartner

Hilfe

Datenschutz

Helmholtz Open Science

Bibliometrische Indikatoren

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Barros, V.* ; Abdallah, N.* ; Ozery-Flato, M.* ; Dekel, A.* ; Raboh, M.* ; Heller, N.* ; Rabinovici-Cohen, S.* ; Golts, A.* ; Gentili, A.* ; Lang, D.M. ; Chaudhary, S.* ; Satish, V.* ; Tejpaul, R.* ; Eggel, I.* ; Guez, I.* ; Barkan, E.* ; Müller, H.* ; Hexter, E.* ; Rosen-Zvi, M.* ; Weight, C.*

Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

PLoS ONE 20:e0323240 (2025)

Verlagsversion

Forschungsdaten

DOI

PMC

	Open Access Gold

Abstract
Metriken
Zusatzinfos

We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of adjuvant therapy based on the mass appearance poses two major challenges: first, morphologic patterns may sometimes overlap across subtypes of varying risks. Second, interobserver variability is large. These complexities encourage the use of computational models as accurate noninvasive tools to find relevant relationships between individual perioperative renal mass characteristics and patient risk. In addition, recent evidence highlights the importance of clinical context as a promising direction to inform treatment decisions post-nephrectomy. In this work, we aim to identify relevant clinical markers that can be predictive of renal cancer prognosis. As a starting point, we perform a clinical feature ablation study by training a logistic regression baseline model to predict renal cancer patients' eligibility for adjuvant therapy. The training dataset consisted of medical records of 300 individuals with renal tumors who underwent partial or radical nephrectomy between 2011 and 2020. In addition, we evaluate the same task using a transformer-based model pretrained on a much larger dataset of over 300,000 clinical records of individuals from the UK Biobank. Our findings demonstrate the pretrained model's efficacy in knowledge transfer across different populations, with radiographic data from preoperative cross-sectional imaging playing an important role in informing renal risk and treatment decisions.

Impact Factor

Scopus SNIP

Altmetric

2.600

0.000