Evidence map›Paper›PMID 42064968›Full record

ArticleTechnical innovations & patient support in radiation oncology2026

Do AI-based contouring algorithms influence physicians in the online adaptive radiotherapy of patients with bladder cancer?

Nika Guberina, Aymane Khouya, Christian Hoffmann, Gerrit Fischedick, Yasemin Alberti, Julian Hlouschek, Fabian Freisleben, Andreas Herz, Mike That Troung Ton, Lars Oliver Kiwitt and 6 more

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Article in Technical innovations & patient support in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Nika GuberinaDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Aymane KhouyaDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Christian HoffmannDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Gerrit FischedickDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Yasemin AlbertiDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Julian HlouschekDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Fabian FreislebenDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Andreas HerzDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Mike That Troung TonDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Lars Oliver KiwittDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Ezgi Ceren SahinDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Alina Santiago GarciaDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Thomas GaulerDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Christoph PöttgenDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Maja GuberinaDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.
Martin StuschkeDepartment of Radiotherapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: In this prospective cross-over study, the precision of manual correction of the clinical target volume (CTV) during online-adaptive radiotherapy (ART) of patients with bladder cancer was investigated in dependence on the input of applied computational algorithms. Methods and materials: Online-adaptive-radiation therapy (ART) fractions were analysed from a prospective registry as a component of a trimodality treatment for bladder cancer. The CBCT-guided adaptive radiation therapy (CBCTgART) workflow uses a CBCT acquired at the beginning of a treatment session (CBCT1) to update the target volume contours of the planning CT (pCT) on the current anatomy of the day. The study comprises radiotherapy fractions with target (CTV) segments showing clinically relevant deviations (>5 mm) when mapped between pre-treatment (CBCT1) and post-adaptation (CBCT2) images. CTV Results: In 151/169 pairwise comparisons, mean-distance-of-segments-to-ground-truth (MDA Conclusions: The present study results reveal that the quality of AI-generated contours is of crucial importance for a proper online onboard ART, as errors may bias clinical workflow and contouring. The accuracy of supervised structures strongly depends on the accuracy of the AI generated contours before supervision.

Indexed as

Artificial intelligenceHuman machine interaction

Identifiers

PMID42064968
PMCPMC13127320

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