ArticlePhysics and imaging in radiation oncology2026
Longitudinal radiomics of liver metastases during magnetic resonance-guided radiotherapy.
Article in Physics and imaging in radiation 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Magnetic resonance-guided radiotherapy (MRgRT) offers high-quality soft tissue contrast and treatment monitoring, supporting longitudinal radiomics analysis. We investigated treatment-related radiomic changes during stereotactic body radiotherapy (SBRT) for liver metastases and healthy liver. Materials and methods: Features were extracted from the gross tumour volume (GTV) and five 2-cm liver rings around the planning target volume (PTV) in 34 patients treated with five-fraction MRgSBRT. Features showing changes below baseline variability or high correlation (Spearman's |r| > 0.9) were excluded, with pre-treatment values averaged into a robust baseline. Linear regression slopes informed uniform manifold approximation and projection (UMAP) followed by hierarchical density-based spatial clustering of applications with noise (HDBSCAN) for patient stratification, with false discovery rate-corrected Kruskal-Wallis testing identifying representative features. Correlations with GTV Results: Of 160 GTV features, 55 uncorrelated features remained. Three patient groups ( Conclusions: This longitudinal MR-based radiomics framework identified GTV and healthy liver patient groups, with distinct treatment-related feature changes. This pipeline could be extended to integrate treatment response assessment in future studies.
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.