Evidence map›Paper›PMID 38369990›Full record

ArticlePhysics and imaging in radiation oncology2024

The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy.

Ethan Wang, Allen Yen, Brian Hrycushko, Siqiu Wang, Jingyin Lin, Xinran Zhong, Michael Dohopolski, Chika Nwachukwu, Zohaib Iqbal, Kevin Albuquerque

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

10 authors.

Ethan WangUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Allen YenUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Brian HrycushkoUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Siqiu WangUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Jingyin LinUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Xinran ZhongUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Michael DohopolskiUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Chika NwachukwuUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Zohaib IqbalUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.
Kevin AlbuquerqueUniversity of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Purpose: Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. Materials and Methods: From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%. Results: Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans. Conclusion: In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.

Indexed as

Adaptive RadiotherapyArtificial IntelligenceCone-beam CTDeformable Image Registration

Identifiers

PMID38369990
PMCPMC10869256

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.