ArticleMedical physics2020
A deep learning framework for prostate localization in cone beam CT-guided radiotherapy.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).La Radiologia medica · 2024Pooled it
- Geometric and dosimetric evaluation of auto-segmentation of brain arteriovenous malformations using multimodal imaging in stereotactic radiosurgery.Frontiers in neuroscience · 2025Article
- Impact of technological advances in treatment planning, image guidance, and treatment delivery on target margin design for prostate cancer radiotherapy: an updated review.The British journal of radiology · 2024Review
- 2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking.Bioengineering (Basel, Switzerland) · 2023Article
- Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT.Frontiers in oncology · 2023Article
- Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy.Computers in biology and medicine · 2022Article
- Artificial intelligence in image-guided radiotherapy: a review of treatment target localization.Quantitative imaging in medicine and surgery · 2021Review
- Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study.Frontiers in oncology · 2021Article
- A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT.Frontiers in oncology · 2021Article
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
Abstract
purposeTo develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup.
methodsA two-step task-based residual network (T
resultsThe systematic/random setup errors between the model prediction and the reference are found to be <0.25/2.46 mm and 0.14/1.41° in translation and rotation dimensions, respectively. Pearson's correlation coefficient between model prediction and the reference is higher than 0.94 in translation and rotation dimensions. The Bland-Altman plots show good agreement between the two techniques.
conclusionsA novel T
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.