ArticleLife (Basel, Switzerland)2022
Implementation of a Commercial Deep Learning-Based Auto Segmentation Software in Radiotherapy: Evaluation of Effectiveness and Impact on Workflow.
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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.
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.
Who cites it
29 citing papers in PubMed.
- Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study.JCO global oncology · 2024Trial
- Clinical evaluation of three deep learning contouring models for head and neck organs at risk and lymph node levels.Technical innovations & patient support in radiation oncology · 2026Article
- A multi-centre real-world evaluation of AI-assisted organ at risk contouring on radiotherapy treatment planning workflows.The British journal of radiology · 2026Article
- Personalised medicine through AI-enhanced integration of diagnostic imaging and radiation therapy.European radiology experimental · 2025Review
- Geometric Accuracy Assessment of AI-Based Auto-contouring Using OncoStudio for Prostate Cancer Treatment Planning.Cureus · 2025Article
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking.International journal of radiation oncology, biology, physics · 2025Article
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- New Approaches in Radiotherapy.Cancers · 2025Review
- Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.Journal of applied clinical medical physics · 2025Article
- Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.Journal of applied clinical medical physics · 2025Article
- Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI.Medical physics · 2025Article
- Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis.Current oncology (Toronto, Ont.) · 2025Article
- Evaluation and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk.Journal of applied clinical medical physics · 2025Article
- Current status and upcoming developments for online adaptive proton therapy enabling a closed feedback loop for near real-time adaptation.Frontiers in oncology · 2025Review
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- On the trail of CBCT-guided adaptive rectal boost radiotherapy, does daily delineation require a radiation oncologist?Technical innovations & patient support in radiation oncology · 2024Article
- Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation.Clinical and translational radiation oncology · 2024Article
- Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries.Advances in radiation oncology · 2024Article
- Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.Journal of applied clinical medical physics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proper delineation of both target volumes and organs at risk is a crucial step in the radiation therapy workflow. This process is normally carried out manually by medical doctors, hence demanding timewise. To improve efficiency, auto-contouring methods have been proposed. We assessed a specific commercial software to investigate its impact on the radiotherapy workflow on four specific disease sites: head and neck, prostate, breast, and rectum. For the present study, we used a commercial deep learning-based auto-segmentation software, namely Limbus Contour (LC), Version 1.5.0 (Limbus AI Inc., Regina, SK, Canada). The software uses deep convolutional neural network models based on a U-net architecture, specific for each structure. Manual and automatic segmentation were compared on disease-specific organs at risk. Contouring time, geometrical performance (volume variation, Dice Similarity Coefficient-DSC, and center of mass shift), and dosimetric impact (DVH differences) were evaluated. With respect to time savings, the maximum advantage was seen in the setting of head and neck cancer with a 65%-time reduction. The average DSC was 0.72. The best agreement was found for lungs. Good results were highlighted for bladder, heart, and femoral heads. The most relevant dosimetric difference was in the rectal cancer case, where the mean volume covered by the 45 Gy isodose was 10.4 cm
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.