Evidence map›Paper›PMID 39253729›Full record

ArticlePhysics and imaging in radiation oncology2024

Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications.

Hasan Cavus, Philippe Bulens, Koen Tournel, Marc Orlandini, Alexandra Jankelevitch, Wouter Crijns, Brigitte Reniers

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 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. Article
  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

7 authors.

Hasan CavusDepartment of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium.
Philippe BulensDepartment of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium.
Koen TournelDepartment of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium.
Marc OrlandiniDepartment of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium.
Alexandra JankelevitchDepartment of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium.
Wouter CrijnsDepartment Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium.
Brigitte ReniersFaculty of Engineering Technology, Hasselt University, B-3590 Diepenbeek, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancements in radiotherapy auto-segmentation necessitate reliable and efficient workflows. Therefore, a standardized fully automatic workflow was developed for three commercially available deep learning-based auto-segmentation applications and compared to a manual workflow for safety and efficiency. The workflow underwent safety evaluation with failure mode and effects analysis. Notably, eight failure modes were reduced, including seven with severity factors ≥7, indicating the effect on patients, and two with Risk Priority Number value >125, which assesses relative risk level. Efficiency, measured by mouse clicks, showed zero clicks with the automatic workflow. This automation illustrated improvement in both safety and efficiency of workflow.

Indexed as

AutomationDeep learningESAPISegmentation

Identifiers

PMID39253729
PMCPMC11381787

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.