ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2022
Preliminary study of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted MRI and diffusion kurtosis imaging on differential diagnosis of spinal metastases and chordoma.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Preoperative differentiation of primary vs. metastatic lumbar spine tumors: development and external validation of a multiparametric MRI-based radiomics nomogram.Frontiers in oncology · 2026Article
- The role of magnetic resonance imaging (MRI) in breast cancer molecular subtypes.Polish journal of radiology · 2026Review
- Improved localization and segmentation of spinal bone metastases in MRI with nnUNet radiomics.Journal of bone oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeQuantitative comparison of diffusion parameters from various models of diffusion-weighted (DWI) and diffusion kurtosis (DKI) imaging for distinguishing spinal metastases and chordomas.
methodsDWI and DKI examinations were performed in 31 and 13 cases of spinal metastases and chordomas, respectively. DWI derived apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), water molecular distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α). DKI derived mean diffusivity (MD) and mean kurtosis (MK). Independent sample t-testing compared statistical differences among parameters. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis evaluated the parameters' correlations.
resultsADC, D, f, DDC, α, and MD were significantly lower in spinal metastases than chordomas (all P < 0.05). MK was significantly higher in spinal metastases than chordomas (P < 0.05). D had the highest area under the ROC curve (AUC) of 0.886, greater than MD (AUC = 0.706) or DDC (AUC = 0.742) in differentiating the two tumors (both P < 0.05). Combining D with f and α statistically significantly increased the AUC for diagnosis (to 0.995) relative to D alone (P < 0.05). There was a certain correlation among DDC, ADC, and D (all P < 0.05).
conclusionsMonoexponential, biexponential, and stretched-exponential models of DWI and DKI can potentially differentiate spinal metastases and chordomas. D combined with f and α performed best.
Indexed as
Identifiers
35648206What 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.