ArticleBioengineering (Basel, Switzerland)2026
Machine Learning with Multiparametric MRI and Clinical Biomarkers for Noninvasive Renal Interstitial Fibrosis Staging.
Article in Bioengineering (Basel, Switzerland), 2026. 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
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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.
- Texture Analysis of Multiparametric Kidney MRI-A Non-Invasive Approach to Chronic Kidney Disease State.Biomedicines · 2026Article
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
Renal interstitial fibrosis (RIF) is currently assessed by invasive biopsy. This prospective study developed and validated a noninvasive random forest model combining multiparametric MRI and clinical biomarkers for identifying severe RIF in 116 patients with biopsy-confirmed renal disease. Quantitative parameters were extracted from IVIM, ASL, phase-contrast MRI, T1 mapping, and BOLD sequences. Fibrosis was classified as mild (<25%) or severe (≥25%). In the held-out test set, the random forest model achieved an AUC of 0.89 (95% CI 0.82-0.96), sensitivity of 0.91, and specificity of 0.73, significantly outperforming clinical-only (AUC 0.63), MRI-only (AUC 0.63), and combined LASSO logistic regression (AUC 0.73) benchmarks. The model also demonstrated superior calibration (Brier score 0.154) and net clinical benefit on decision curve analysis. This integrated MRI-clinical model shows promise for noninvasive identification of severe RIF and warrants external prospective validation.
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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.