Evidence map›Paper›PMID 41965936›Full record

ArticleScientific reports2026

Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer.

Ralf Schmidt, Dion Bajerski, Alicia S Bicu, Victor Siefert, Miriam Eckl, Marvin Willam, Matthias F Froelich, Stefan O Schoenberg, Michael Ehmann, Daniel Buergy and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Quantifying manual editing time in autocontouring via audit log analysis.Physics and imaging in radiation oncology · 2026
    Article
  2. HyperSightRadiation oncology (London, England) · 2026
    Article
  3. 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

15 authors.

Ralf SchmidtDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Dion BajerskiDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Alicia S BicuDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Victor SiefertDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Miriam EcklDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Marvin WillamDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Matthias F FroelichDepartment of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Stefan O SchoenbergDepartment of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Michael EhmannDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Daniel BuergyDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Florian StielerDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Jens FleckensteinDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Frank A GiordanoDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Judit Boda-HeggemannDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Constantin DreherDepartment of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. constantin.dreher@umm.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluated segmentation accuracy, efficiency, and radiomic feature stability for manual (MD), artificial intelligence-based (AI), and hybrid (MD + AI) contouring of pelvic organs on planning CT (pCT) and HyperSight cone-beam CT (hCBCT) for adaptive radiotherapy. Dice similarity and 95th percentile Hausdorff distance (HD95) quantified segmentation agreement, while radiomic feature stability was assessed using the concordance correlation coefficient (CCC). Agreement between segmentation approaches was highest for bladder and femora (median Dice 0.95–0.96; HD95 1.88–2.17 mm), intermediate for prostate and rectum (median Dice 0.92; HD95 2.22–2.62 mm), and lowest for seminal vesicles and penile bulb (median Dice 0.76–0.83; HD95 3.01–3.41 mm). AI and MD + AI reduced contouring times by about 90% and 60% compared to MD. Radiomic feature stability differed significantly between segmentation modes (all padj ≤ 0.05). GLRLM features exhibited significantly higher stability than other features, whereas morphological features showed lower stability. Median radiomic feature stability was highest for bladder and femora, and intermediate for prostate and rectum. In conclusion, AI-based and hybrid contouring achieved high accuracy and substantial time savings, while texture- and intensity-based radiomic features showed robustness with AI segmentation. This study demonstrated feasibility of extracting distinct, reliable quantitative parameters based on AI-only contouring of pelvic structures.

Indexed as

Artificial IntelligenceCone-Beam Computed TomographyProstatic NeoplasmsRadiotherapy Planning, Computer-AssistedFeasibility StudiesHumansMaleProstateRadiomicsAdaptive radiotherapyAutocontouringEthosHyperSight-CBCTProstate cancerRadiomics

Identifiers

PMID41965936
PMCPMC13076649

What OpenQuestion holds

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Registered trials

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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.