ArticleFrontiers in oncology2026
CT-based deep learning auto-segmentation of high-risk clinical target volume in CT-guided cervical cancer brachytherapy: a single-center pragmatic study.
Article in Frontiers in oncology, 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Computed tomography (CT)-based high-risk clinical target volume (HR-CTV) auto-segmentation has been previously investigated, but evidence remains heterogeneous across applicators, target definitions, architectures, and clinical evaluation procedures. A pragmatic within-cohort benchmark of 2D U-Net, 3D U-Net, and nnFormer was performed in a CT-only, applicator- Methods: CT images from 544 brachytherapy fractions in 182 patients were analyzed, including 509 fractions from 163 patients treated with tandem-and-ovoid applicators and 35 fractions from 19 patients treated with vaginal cylinders. All fractions from a patient remained in one partition (HR-CTV Results: In the intact-cervix test cohort, 3D U-Net showed the most favorable descriptive combination of overlap and surface agreement (DSC 0.848 ± 0.059; HD95 2.755 ± 1.688 mm; ASD 1.008 ± 0.622 mm). In the exploratory vaginal stump cohort, corresponding values were 0.788 ± 0.060, 4.724 ± 2.434 mm, and 2.917 ± 2.150 mm. On physician consensus review, 91/102 HR-CTV Conclusions: In this internally validated CT-guided cohort, 3D U-Net provided the most favorable overall performance among the evaluated architectures for intact-cervix cases. The postoperative results provide a preliminary feasibility signal, and confirmation in a larger cohort is required. These findings support further physician-supervised implementation, followed by multicenter external validation and cohort-level dosimetric assessment.
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.