ReviewJournal of the National Cancer Center2022
Towards automated organs at risk and target volumes contouring: Defining precision radiation therapy in the modern era.
Review in Journal of the National Cancer Center, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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
15 citing papers in PubMed.
- AI-Based Dose Compliance of Secondary Organs at Risk in Head and Neck Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
- Development of an Integrated Radiotherapy Simulation Platform with AI-Driven Segmentation and Ray-Casting-Based Dosimetric Evaluation.Bioengineering (Basel, Switzerland) · 2026Article
- IRF4 Enhances Radiosensitivity of Cervical Cancer by Inhibiting the PI3K/Akt/mTOR Pathway to Regulate Autophagy.Cancer science · 2026Article
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Educational Impact of a National Training Webinar on AI-based Automatic Contouring in Radiation Oncology.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- A global bibliometric and visualization study of the adaptive radiotherapy research landscape.Discover oncology · 2025Article
- Automated segmentation of esophageal squamous cell carcinoma in contrast-enhanced free-breathing 3D-GRE: a comparative study of UNet, nnUNet, and UMamba for tumor delineation.BMC medical imaging · 2025Article
- Strategies to reduce intestinal toxicity in neoadjuvant management of locally advanced rectal cancer.Discover oncology · 2025Review
- Deep learning-based auto-segmentation model for clinical target volume delineation in brachytherapy after parotid cancer surgery.Journal of contemporary brachytherapy · 2025Article
- Multi-scale channel attention U-Net: a novel framework for automated gallbladder segmentation in medical imaging.Frontiers in oncology · 2025Article
- New rectum dose surface mapping methodology to identify rectal subregions associated with toxicities following prostate cancer radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Artificial intelligence-assisted delineation for postoperative radiotherapy in patients with lung cancer: a prospective, multi-center, cohort study.Frontiers in oncology · 2024Article
- Benefits and considerations in using a novel computed tomography system optimized for radiotherapy planning.Physics and imaging in radiation oncology · 2023Article
- Advances in automatic delineation of target volume and cardiac substructure in breast cancer radiotherapy (Review).Oncology letters · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision radiotherapy is a critical and indispensable cancer treatment means in the modern clinical workflow with the goal of achieving "quality-up and cost-down" in patient care. The challenge of this therapy lies in developing computerized clinical-assistant solutions with precision, automation, and reproducibility built-in to deliver it at scale. In this work, we provide a comprehensive yet ongoing, incomplete survey of and discussions on the recent progress of utilizing advanced deep learning, semantic organ parsing, multimodal imaging fusion, neural architecture search and medical image analytical techniques to address four corner-stone problems or sub-problems required by all precision radiotherapy workflows, namely, organs at risk (OARs) segmentation, gross tumor volume (GTV) segmentation, metastasized lymph node (LN) detection, and clinical tumor volume (CTV) segmentation. Without loss of generality, we mainly focus on using esophageal and head-and-neck cancers as examples, but the methods can be extrapolated to other types of cancers. High-precision, automated and highly reproducible OAR/GTV/LN/CTV auto-delineation techniques have demonstrated their effectiveness in reducing the inter-practitioner variabilities and the time cost to permit rapid treatment planning and adaptive replanning for the benefit of patients. Through the presentation of the achievements and limitations of these techniques in this review, we hope to encourage more collective multidisciplinary precision radiotherapy workflows to transpire.
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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.