Evidence map›Paper›PMID 39559487›Full record

ArticlePhysics and imaging in radiation oncology2024

Evaluation of deep learning-based target auto-segmentation for Magnetic Resonance Imaging-guided cervix brachytherapy.

Rita Simões, Eva C Rijkmans, Eva E Schaake, Marlies E Nowee, Sandra van der Velden, Tomas Janssen

Abstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Trends in brachytherapy utilization in Canada from 2011 to 2020.Physics and imaging in radiation oncology · 2026
    Article
  4. Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026
    Review
  5. Breakthroughs in modern brachytherapy are transforming cancer care.Physics and imaging in radiation oncology · 2025
    Article
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

6 authors.

Rita SimõesDepartment of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands (the).
Eva C RijkmansDepartment of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands (the).
Eva E SchaakeDepartment of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands (the).
Marlies E NoweeDepartment of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands (the).
Sandra van der VeldenDepartment of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands (the).
Tomas JanssenDepartment of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands (the).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: The target structures for cervix brachytherapy are segmented by radiation oncologists using imaging and clinical information. At the first fraction, this is performed manually from scratch. For subsequent fractions the first fraction segmentations are rigidly propagated and edited manually. This process is time-consuming while patients wait immobilized. In this work, we evaluate the potential clinical impact of using population-based and patient-specific auto-segmentations as a starting point for target segmentation of the second fraction. Materials and method: For twenty-eight patients with locally advanced cervical cancer, treated with MRI-guided brachytherapy, auto-segmentations were retrospectively generated for the second fraction image using two approaches: 1) population-based model, 2) patient-specific models fine-tuned on first fraction information. A radiation oncologist manually edited the auto-segmentations to assess model-induced bias. Pairwise geometric and dosimetric comparisons were performed for the automatic, edited and clinical structures. The time spent editing the auto-segmentations was compared to the current clinical workflow. Results: The edited structures were more similar to the automatic than to the clinical structures. The geometric and dosimetric differences between the edited and the clinical structures were comparable to the inter-observer variability investigated in literature. Editing the auto-segmentations was faster than the manual segmentation performed during our clinical workflow. Patient-specific auto-segmentations required less edits than population-based structures. Conclusions: Auto-segmentation introduces a bias in the manual delineations but this bias is clinically irrelevant. Auto-segmentation, particularly patient-specific fine-tuning, is a time-saving tool that can improve treatment logistics and therefore reduce patient burden during the second fraction of cervix brachytherapy.

Indexed as

Automatic target segmentationBrachytherapyCervical cancerPatient-specific fine-tuning

Identifiers

PMID39559487
PMCPMC11570852

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.