ArticleFrontiers in oncology2020
Evaluation of Automatic Segmentation Model With Dosimetric Metrics for Radiotherapy of Esophageal Cancer.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 32 citations in OpenAlex.
- Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.Journal of applied clinical medical physics · 2024Article
- Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.Journal of applied clinical medical physics · 2024Article
- Optimized target delineation procedure for the radiosurgery treatment of ventricular tachycardia: observer-independent accuracy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2024Article
- Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy.Journal of applied clinical medical physics · 2023Article
- A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer.Physics and imaging in radiation oncology · 2023Article
- Online adaptive planning methods for intensity-modulated radiotherapy.Physics in medicine and biology · 2023Review
- Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?Diagnostics (Basel, Switzerland) · 2023Review
- Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning.Cancers · 2022Article
- Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk.Physics and imaging in radiation oncology · 2022Article
- Stereotactic Ablative Radiotherapy of Ventricular Tachycardia Using Tracking: Optimized Target Definition Workflow.Frontiers in cardiovascular medicine · 2022Article
- A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer.Frontiers in oncology · 2022Article
- Geometric and Dosimetric Evaluation of the Automatic Delineation of Organs at Risk (OARs) in Non-Small-Cell Lung Cancer Radiotherapy Based on a Modified DenseNet Deep Learning Network.Frontiers in oncology · 2022Article
- Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine.Cancer communications (London, England) · 2021Review
- Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas.Cancers · 2021Review
- Application of Deep Convolution Network to Automated Image Segmentation of Chest CT for Patients With Tumor.Frontiers in oncology · 2021Article
- Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations.Technology in cancer research & treatmentArticle
- Clinical Validation and Treatment Plan Evaluation Based on Autodelineation of the Clinical Target Volume for Prostate Cancer Radiotherapy.Technology in cancer research & treatmentArticle
- A Review of Artificial Intelligence Application for Radiotherapy.Dose-response : a publication of International Hormesis SocietyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.