ArticleJournal of applied clinical medical physics2025
Validating clinical feasibility of MRCAT and deep learning-based synthetic CT images for cervical cancer patient.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Validating clinical feasibility of MRCAT and deep learning-based synthetic CT images for cervical cancer patient.Journal of applied clinical medical physics · 2025Article
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.
Funding
Abstract
backgroundVarious methods have been developed to generate synthetic computed tomography (CT) images from magnetic resonance (MR) images, including segmentation-based approach with MR calculating attenuation (MRCAT) and deep learning (DL)-based approach. PURPOSE: In this study, we aimed to validate the conventional radiotherapy (RT) planning process with MRCAT and DL-based synthetic CT images for five patients with cervical cancer.
methodsDL-based synthetic CT images of the five patients were inferred using a network trained with 40 pairs of CT and deformed, normalized T2-weighted MR scans; MRCAT images were obtained from mDixon sequences for the tested cases only. On the synthetic CT images, the contouring process for organs-at-risk (OARs) was automatically performed with minor adjustments, while two experienced radiation oncologists defined target volumes. Simultaneous integrated boost plans (2.2/2.0/1.8 Gy with 25 fractions) were produced from a commercial treatment planning system (TPS) TomoTherapy.
resultsThe plans with two synthetic CT images were compared with those based on genuine CT images for the five test cases. High geometric similarity was confirmed for the planning target volume (PTV), with average dice similarity coefficient (DSC) of 0.844 for the DL-based and 0.829 for the MRCAT images. The mean percentage difference in gross tumor volume (GTV) was 20.71
conclusionMRCAT and DL-based synthetic CT images demonstrated clinical applicability, achieving plan quality similar to that of plans based on genuine planning CT images.
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.