Evidence map›Paper›PMID 42016352›Full record

ArticlePhysics and imaging in radiation oncology2026

Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.

Bao Ngoc Huynh, Cecilia Marie Futsaether, Oliver Tomic, Kristian Hovde Liland, Manish Kakar, Taran Paulsen Hellebust, Eirik Malinen, Einar Dale, Karsten Rydén-Eilertsen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Bao Ngoc HuynhOslo University Hospital, Department of Medical Physics, Oslo, Norway.
Cecilia Marie FutsaetherNorwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway.
Oliver TomicNorwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway.
Kristian Hovde LilandNorwegian University of Life Sciences, Faculty of Science and Technology, Ås, Norway.
Manish KakarOslo University Hospital, Department of Radiation Biology, Institute of Cancer Research, Oslo, Norway.
Taran Paulsen HellebustOslo University Hospital, Department of Medical Physics, Oslo, Norway.
Eirik MalinenOslo University Hospital, Department of Radiation Biology, Institute of Cancer Research, Oslo, Norway.
Einar DaleOslo University Hospital, Department of Oncology, Oslo, Norway.
Karsten Rydén-EilertsenOslo University Hospital, Department of Medical Physics, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Manual Gross Tumor Volume (GTV) delineation for radiotherapy is time‑consuming and prone to inter‑observer variability. Deep Learning (DL)-based auto‑segmentation offers faster and more consistent solutions, but translation into clinical practice remains limited. This study aimed to bridge this gap by integrating an in‑house GTV auto‑segmentation DL model into a radiotherapy Treatment Planning System (TPS). To demonstrate the integration process, a head‑and‑neck GTV segmentation model was successfully deployed and operated seamlessly within the TPS. In this integration, DL‑generated GTV contours were produced in under three minutes, demonstrating the feasibility of DL‑based GTV segmentation in the TPS without disrupting the radiotherapy workflow.

Indexed as

Artificial intelligenceAutomationDeep learningHead and neck cancerMachine learning

Identifiers

PMID42016352
PMCPMC13092611

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.