ReviewThe British journal of radiology2024
Deep learning for automated segmentation in radiotherapy: a narrative review.
Review in The British journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 28 citations in OpenAlex.
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Implementation of an AI-Driven Workflow for Daily Dose Reconstruction in Prostate Cancer Radiotherapy.Cancers · 2026Article
- Review
- A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.BMC medical imaging · 2026Article
- Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer.Scientific reports · 2026Article
- Article
- Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment.Cancers · 2025Review
- Article
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- Can Advances in Artificial Intelligence Strengthen the Role of Intraoperative Radiotherapy in the Treatment of Cancer?Cancers · 2025Review
- A method for sensitivity analysis of automatic contouring algorithms across different contrast weightings using synthetic magnetic resonance imaging.Physics and imaging in radiation oncology · 2025Article
- Fan beam computed tomography-guided online adaptive external radiotherapy for cervical cancer achieves pathological complete response: A case report.Oncology letters · 2025Article
- LUND-PROBE - LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset.Scientific data · 2025Article
- Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas.Radiation oncology (London, England) · 2024Article
- DART-Net: A Novel Deep Learning Framework for Precise Radiotherapy Planning with Automated Multiorgan Segmentation and RTSTRUCT Generation.Journal of medical physicsArticle
- A Combined Loss-driven Framework for Automated Parotid Segmentation in Head-and-Neck Computed Tomography.Journal of medical physicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-consuming task. Interobserver variability can also impact the outcomes of radiation therapy. Deep neural networks have recently gained attention for their ability to automate segmentation tasks, with convolutional neural networks (CNNs) being a popular approach. This article provides a descriptive review of the literature on deep learning (DL) techniques for segmentation in radiation therapy planning. This review focuses on five clinical sub-sites and finds that U-net is the most commonly used CNN architecture. The studies using DL for image segmentation were included in brain, head and neck, lung, abdominal, and pelvic cancers. The majority of DL segmentation articles in radiation therapy planning have concentrated on normal tissue structures. N-fold cross-validation was commonly employed, without external validation. This research area is expanding quickly, and standardization of metrics and independent validation are critical to benchmarking and comparing proposed methods.
Indexed as
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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.