Evidence map›Paper›PMID 41821468›Full record

ArticleRadiation protection dosimetry2026

Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study.

Eirini Polymeri, Åse A Johnsson, Olof Enqvist, Johannes Ulén, Jon Kindblom, Karin Braide, Hans-Jurgen Wiltz, Margareta Tanyasiová, Elin Trägårdh, Lars Edenbrandt and 2 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiation protection dosimetry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Eirini PolymeriDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 3, Medicinareberget, 413 90, Gothenburg, Region Västra Götaland, Sweden.ORCID 0000-0003-3138-9330
Åse A JohnssonDepartment of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 3, Medicinareberget, 413 90, Gothenburg, Region Västra Götaland, Sweden.
Olof EnqvistDepartment of Electrical Engineering, Chalmers University of Technology, Hörsalsvägen 9-11, Region Västra Götaland, 412 96, Gothenburg, Sweden.
Johannes UlénEigenvision AB, Bredgatan 4, 211 30, Region Skåne, Malmö, Sweden.
Jon KindblomDepartment of Oncology, Sahlgrenska University Hospital, Blå Stråket 2, Region Västra Götaland, 413 45, Gothenburg, Sweden.
Karin BraideDepartment of Oncology, Institute of Clinical Sciences, University of Gothenburg, Medicinaregatan 3, Medicinareberget, 413 90, Gothenburg, Region Västra Götaland, Sweden.
Hans-Jurgen WiltzDepartment of Oncology and Radiotherapy, Region Kronoberg, Central lasarett Växjö, Strandvägen 8, 351 85, Växjö, Sweden.
Margareta TanyasiováDepartment of Oncology, Sahlgrenska University Hospital, Blå Stråket 2, Region Västra Götaland, 413 45, Gothenburg, Sweden.
Elin TrägårdhClinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, 221 85, Malmö, Region Skåne, Sweden.
Lars EdenbrandtDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Blå Stråket 5, 413 45, Region Västra Götaland, Gothenburg, Sweden.
Henrik KjölhedeDepartment of Urology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 5, Medicinareberget, 413 90, Region Västra Götaland, Gothenburg, Sweden.
Angelica SvalkvistDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 5, Medicinareberget, 413 90, Region Västra Götaland, Gothenburg, Sweden.

Funding

Göteborgs LäkarsällskapUniversity Hospital of Sahlgrenska
6 · The paper itself

Abstract

This study aimed to evaluate the clinical acceptability of artificial intelligence (AI)-based organ segmentations on pretreatment CT images of prostate cancer patients using manual organ delineations as a reference. Paired AI-based segmentations and manual delineations of the prostate, urinary bladder, and rectum were evaluated by three observers, according to a 4-grade Likert-scale, based on quality criteria, developed through a Delphi process. Visual grading characteristics (VGC) analysis was performed. When comparing the ratings of AI-based (n = 360) and manual delineations (n = 360), the area under the VGC-curve (AUCVGC) was 0.36 (95% CI 0.27-0.44), 0.35 (95% CI 0.28-0.41), and 0.3 (95% CI 0.22-0.40) for the prostate, urinary bladder, and rectum, respectively, indicating inferior ratings for the algorithm. Few AI segmentations (8%) were considered clinically unacceptable, while in 67% no or minor changes were needed. Despite superior ratings for manual delineations, most AI-segmentations needed no or minor changes, indicating clinical acceptability.

Indexed as

Artificial IntelligenceProstatic NeoplasmsRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedAged, 80 and overAlgorithmsHumansMaleMiddle AgedNeoplasm GradingProstateRectumUrinary Bladder

Identifiers

PMID41821468
PMCPMC13046062

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.