Observational studyMedicine2026
A CT-based radiomics model for noninvasive prediction of MMP9 expression and prognostic evaluation in renal cell carcinoma.
Observational study in Medicine, 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
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
1 citing paper in PubMed.
- Cancer Cachexia in Advanced Renal Cell Carcinoma: From Molecular Mechanisms to Prognostic Assessment.International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Renal cell carcinoma (RCC) represents one of the most common malignant tumors of the urinary system, characterized by high heterogeneity and aggressiveness. Studies indicate that matrix metalloproteinase-9 (MMP9) plays a critical role in the invasion, metastasis, and prognosis of RCC, and is recognized as a potential molecular biomarker and therapeutic target. This study aims to develop a radiomics-based prediction model for noninvasive identification of MMP9 expression levels in RCC patients and to assess its clinical relevance in patient prognosis. This retrospective study utilized enhanced computed tomography images and clinical data from The Cancer Genome Atlas database. We manually delineated volumes of interest, extracted radiomic features, and performed standardization procedures. Key features were selected using minimum redundancy maximum relevance and recursive feature elimination algorithms, followed by the construction of a radiomics model for predicting MMP9 expression through the gradient boosting machine algorithm. Survival analysis was performed using Kaplan-Meier curves and Cox proportional hazards regression models. Furthermore, immune cell infiltration analysis and enrichment analysis were employed to explore the underlying biological mechanisms associated with MMP9. MMP9 demonstrated independent prognostic value in RCC patients (hazard ratio = 1.447). The radiomics model incorporating 3 features, constructed using multiple machine learning algorithms, exhibited favorable predictive performance for MMP9 expression levels (area under the curve = 0.865). The radiomics score mapping MMP9 expression levels served as an independent risk predictor for RCC patients (hazard ratio = 2.287, 95% confidence interval = 1.025-5.103, P = .043). Furthermore, MMP9 correlates with pathways such as immune response regulation signals, carbon metabolism, and PI3K-Akt, as well as immune cell infiltration, including M2-type macrophages and regulatory T cells. MMP9 serves as an independent prognostic factor in RCC. The enhanced computed tomography radiomics model composed of 3 features can noninvasively predict MMP9 expression levels in RCC patients.
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.