ArticleEuropean radiology2026
Subtraction iodine maps combined with deep learning reconstruction for improved evaluation of bony invasion in temporal bone lesions.
Article in European radiology, 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
9 authors.
Funding
Abstract
objectivesTo assess the capability of subtraction iodine maps (SIM) combined with deep learning reconstruction (DLR) to evaluate the identification of bone involvement in patients with temporal bone lesions, compared with conventional enhanced CT with hybrid iterative reconstruction (HIR). MATERIALS AND
methodsThis prospective study continuously recruited seventy-seven patients (mean age: 46.1 ± 18.9 years; 35 males) with temporal bone lesions who underwent enhanced temporal bone CT scans. Pre-contrast and contrast-enhanced phase images were reconstructed using HIR and DLR. The colored SIM fusion images for evaluation were generated by subtracting the pre-contrast image from the contrast-enhanced image to obtain the SIM, which was then overlaid onto the bone window image. Qualitative scores and quantitative parameters were computed and compared among four groups of images: Enhanced-HIR, Enhanced-DLR, SIM-HIR, and SIM-DLR.
resultsQuantitative analysis demonstrated that SIM-DLR significantly reduced noise (lower SD values) and increased CNR of the lesion compared to Enhanced-HIR (both p < 0.001). Qualitatively, SIM-DLR exhibited significantly higher scores than conventional Enhanced-HIR in overall image quality, temporal bone lesion detection confidence, lesion contour delineation capability, and temporal bone involvement visualization (all p < 0.001), with high inter-reader agreement (kappa: 0.812-0.904). Enhancement-dependent analysis revealed that SIM showed markedly beneficial scores of lesion detection, contour delineation, and bone involvement visualization in high-enhancement lesions, compared to low-enhancement lesions (all p < 0.05).
conclusionCompared to conventional Enhanced-HIR, SIM-DLR significantly enhanced temporal bone CT imaging by improving image quality, anatomical delineation, and bone involvement visualization, especially for hypervascular lesions. KEY POINTS: Question Accurately assessing bony invasion by temporal bone lesions is challenging on conventional enhanced CT due to poor lesion-to-bone contrast. Findings SIM combined with DLR improve image quality, lesion delineation, and visualization of bone involvement compared to conventional CT. Clinical relevance This technique enhances preoperative evaluation of temporal bone lesions, particularly hypervascular pathologies, aiding surgical planning and potentially improving treatment precision and outcomes.
Indexed as
Identifiers
42855533What 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.