ArticleJournal of applied clinical medical physics2026
Deep learning-based dose prediction for stereotactic prostate cancer radiotherapy with CyberKnife.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning-based (DL) approaches have gained interest in predicting dose distributions in radiotherapy of prostate cancer treated with volumetric modulated arc therapy and intensity-modulated radiation therapy. Meanwhile, research on predicting dose distributions in high-precision stereotactic radiotherapy treatments has remained relatively underrepresented. PURPOSE: We aimed to expand the previous studies by developing a DL-based framework for predicting dose distributions for robotic, stereotactic prostate cancer radiotherapy.
methodsWe harnessed a U-Net-based convolutional neural network for predicting clinically achievable dose distributions based on CT images, delineated structures, and distance information from the planning target volume. A dataset of 462 patients treated with CyberKnife (Accuray Inc.) utilizing an Iris collimator was divided into training (70%, n = 323), validation (10%, n = 46), and test (20%, n = 93) sets.
resultsIn the independent test set, the mean absolute error between the mean doses of predictions and clinical plans was 0.63 Gy for the rectum and 1.04 Gy for the bladder.
conclusionsThe proposed U-Net-based model demonstrated the ability to learn and reproduce characteristic dose distributions in CyberKnife prostate cancer radiotherapy. The model may provide patient-specific dose estimates for setting initial planning objectives to assist in automating treatment planning and improving inter-planner consistency.
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.