ArticleFrontiers in oncology2025
Intratumoral and peritumoral radiomics using multi-phase contrast-enhanced CT for diagnosis of renal oncocytoma and chromophobe renal cell carcinoma: a multicenter retrospective study.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Renal oncocytoma remains a diagnostic dilemma: imaging heterogeneity and potential overtreatment in a retrospective cohort.International urology and nephrology · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Advancing early detection of chromophobe renal cell carcinoma: a Bayesian optimization approach to machine learning models.Scientific reports · 2026Article
- Tabular prior-data fitted network in real-world CT radiomics: benignQuantitative imaging in medicine and surgery · 2025Article
- Constructing an early warning model for elderly sepsis patients based on machine learning.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To construct diagnostic models that distinguish renal oncocytoma (RO) from chromophobe renal cell carcinoma (CRCC) using intratumoral and peritumoral radiomic features from the corticomedullary phase (CMP) and nephrographic phase (NP) of computed tomography, and compare model results with manual and radiological results. Methods: The RO and CRCC cases from five centers were split into a training set (70%) and a validation set (30%). CMP and NP intratumoral and peritumoral (1-3 mm) radiomic features were extracted. Segmentation was performed by radiologists and software. Features with high intraclass correlation coefficients (ICC>0.75) were selected through univariate analysis, followed by the LASSO method to determine the final features for the SVM model. All images were assessed by two radiologists, and radiological reports were also examined. The diagnostic performances of the different methods were compared using several statistical methods. Results: The training set had 65 cases (29 RO, 36 CRCC) and the validation set had 27 cases (12 RO, 15 CRCC). All the training models had excellent performance (area under the curve [AUC]: 0.828-0.942); the AUC values of the validation models ranged from 0.900 (Model 4) to 0.600 (Model 2). CMP models (AUC: 0.811-0.900) generally outperformed NP and fusion models (AUC: 0.728-0.756). SVM models (sensitivity: 62.50-88.89%; specificity: 63.16-77.78%; accuracy: 62.96-81.48%) outperformed manual diagnosis (sensitivity: 46.74-70.59%; specificity: 41.67-46.34%; accuracy: 52.27-59.78%). The clinical reports alone had no diagnostic value. Conclusion: CMP intratumoral and peritumoral radiomics models reliably distinguished RO from CRCC.
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.