ArticleClinics (Sao Paulo, Brazil)2026
A multimodal MRI radiomics sonata for differentiating small renal masses: a two-center retrospective study.
Article in Clinics (Sao Paulo, Brazil), 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurately distinguishing benign from malignant small renal masses (SRMs) is crucial to avoid unnecessary surgeries, yet reliable preoperative diagnostic tools remain limited.
methodsA total of 189 patients with SRMs from two hospitals (2018-2022) were enrolled. Clinical data and MRI features were analyzed by two radiologists. Radiomics features were automatically extracted from manually segmented ROIs using dedicated software. Significant features were selected via univariate analysis, multivariate regression, and Lasso to develop three models: radiomics, clinical, and combined nomogram. Diagnostic performance was evaluated using the Area Under the receiver operating characteristic Curve (AUC).
resultsThe radiomics-clinical nomogram was constructed using T2WI signal intensity (p < 0.001), pseudocapsule (p = 0.001), and radiomics score (Rad-score, p = 0.001) via multivariate logistic regression. In the training set, the nomogram achieved an AUC of 0.962 (95% CI: 0.931-0.993), outperforming both the radiomics model (AUC = 0.918, 95% CI: 0.862-0.974) and the clinical model (AUC = 0.856, 95% CI: 0.780-0.933). Favorable performance was observed in the internal validation set (nomogram: AUC = 0.981, 95% CI: 0.944-1.000; radiomics model: AUC = 0.912, 95% CI: 0.750-1.000; clinical model: AUC = 0.844, 95% CI: 0.578-1.000) and external validation set (nomogram: AUC = 0.971, 95% CI: 0.886-1.000; radiomics model: AUC = 0.928, 95% CI: 0.841-0.989; clinical model: AUC = 0.916,95% CI: 0.760-0.976).
conclusionAll three models enable effective and non-invasive preoperative differentiation of benign from malignant SRMs, with the radiomics-clinical nomogram exhibiting the highest diagnostic accuracy, supporting its potential as a valuable clinical tool.
Indexed as
Identifiers
42887293What 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.