PuSH - Publication Server of Helmholtz Zentrum München: Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

EVA: Electronic Publishing

New EVA Application

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

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)

Publ. Version/Full Text

Research data

DOI

PMC

	Open Access Gold

Abstract
Metrics
Extra information

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