ArticlePolish journal of radiology2026
Radiomics analysis of parotid gland tumors using DWI and DCE-MRI: comparison of multiple machine learning models for differential diagnosis.
Article in Polish journal of radiology, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop and validate the value of different radiomics models based on diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) images for preoperative discrimination of parotid gland tumors (PGTs). Material and methods: A total of 158 patients with pathologically confirmed PGTs (123 benign and 35 malignant PGTs) were divided into training ( Results: Logistic regression (LR) was selected as the optimal classifier for distinguishing benign and malignant PGTs. The radiomics model based on DWI + DCE-MRI achieved the highest AUC values in the training and test cohorts (AUC = 0.915 and 0.861), outperforming the DCE-only model (AUC = 0.903 and 0.847) and the DWI-only model (AUC = 0.839 and 0.769). The DCA based on the DWI + DCE-MRI radiomics model demonstrated high clinical usefulness. Conclusions: Radiomics is a useful tool for distinguishing malignant from benign PGTs. The radiomics predictive model that combines DWI and DCE-MRI yielded outstanding performance for improving clinical decision-making regarding the treatment of PGTs.
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.