ArticlePloS one2026
Impact of dual-layer spectral CT-derived stopping power ratio on single-energy CT-based carbon-ion treatment planning for thoracic cancer.
Article in PloS one, 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
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the impact of stopping-power ratio (SPR) predictions derived from dual-layer detector-based spectral CT (DLCT) on conventional single-energy CT (SECT)-based carbon-ion treatment planning for thoracic cancer.
methodsTwenty patients with thoracic cancer who underwent non-contrast DLCT were retrospectively included. DLCT-based SPR images were generated from effective atomic number and relative electron density maps using the Bethe equation. SECT-based SPR was derived using a Hounsfield unit look-up table generated via stoichiometric calibration of a commercial electron density phantom. Using the same phantom, a phantom-based prediction assessment was performed by comparing SECT- and DLCT-based SPR predictions with theoretical SPR values for the inserts. For patient analysis, normal-tissue SPR values derived from SECT and DLCT were compared. Carbon-ion treatment plans were first generated based on SECT images and then recalculated using DLCT-derived SPR images. Range differences in the beam's-eye view and dose-volume histogram parameters for the planning target volume and organs at risk were evaluated.
resultsIn the phantom-based assessment, the root-mean-square error relative to the theoretical SPR was 5.57% for SECT and 2.83% for DLCT. In patient images, mean SPR differences between SECT and DLCT were 3.62% for the lungs, 1.93% for the esophagus, 0.04% for the heart, 0.93% for the spinal cord, and 1.53% for the ribs. Across the patient cohort, the mean recalculated range difference was 0.71 ± 0.89 mm, corresponding to a relative difference of 0.60 ± 0.67%. No statistically significant differences were observed in target dose metrics, whereas selected organ-at-risk metrics showed statistically significant differences between SECT-based plans and DLCT-based recalculations.
conclusionDLCT-derived SPR prediction showed better agreement with theoretical phantom SPR values in the phantom-based prediction assessment and produced tissue-dependent SPR differences in thoracic patient images compared with conventional SECT-based prediction. Recalculation on DLCT-derived SPR maps resulted in modest but systematic range differences and limited changes in selected dose metrics. These results may inform the implementation and future independent validation of patient-specific DLCT-based SPR workflows in carbon-ion treatment planning for thoracic cancer.
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.