Evidence map›Paper›PMID 39472608›Full record

ArticleScientific reports2024

A student trained convolutional neural network competing with a commercial AI software and experts in organ at risk segmentation.

Sophia L Bürkle, Dejan Kuhn, Tobias Fechter, Gianluca Radicioni, Nanna Hartong, Martin T Freitag, Xuefeng Qiu, Efstratios Karagiannis, Anca-Ligia Grosu, Dimos Baltas and 2 more

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

Article in Scientific reports, 2024. 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. Article
  2. Review
  3. 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

12 authors.

Sophia L BürkleDepartment of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. sophia.buerkle@uniklinik-freiburg.de.
Dejan KuhnDivision of Medical Physics, Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Tobias FechterDivision of Medical Physics, Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Gianluca RadicioniDepartment of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Nanna HartongDepartment of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Martin T FreitagDepartment of Nuclear Medicine, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Xuefeng QiuDepartment of Urology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
Efstratios KaragiannisGerman Oncology Center (GOC), European University of Cyprus, Limassol, Cyprus.
Anca-Ligia GrosuDepartment of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Dimos BaltasDivision of Medical Physics, Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Constantinos ZamboglouDepartment of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Simon K B SpohnDepartment of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This retrospective, multi-centered study aimed to improve high-quality radiation treatment (RT) planning workflows by training and testing a Convolutional Neural Network (CNN) to perform auto segmentations of organs at risk (OAR) for prostate cancer (PCa) patients, specifically the bladder and rectum. The objective of this project was to develop a clinically applicable and robust artificial intelligence (AI) system to assist radiation oncologists in OAR segmentation. The CNN was trained using manual contours in CT-datasets from diagnostic

Indexed as

Neural Networks, ComputerOrgans at RiskProstatic NeoplasmsSoftwareArtificial IntelligenceHumansMalePositron Emission Tomography Computed TomographyRadiotherapy Planning, Computer-AssistedRectumRetrospective StudiesUrinary BladderArtificial intelligenceAuto segmentationConvolutional neural networkProstate cancerRadiation treatment planningTuring test

Identifiers

PMID39472608
PMCPMC11522297

What OpenQuestion holds

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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.