ArticleThe Canadian journal of urology2025
AI-driven radiogenomic analysis of clear cell renal cell carcinoma: perinephric adipose tissue stranding as a key feature of the NIPAL4-associated imaging pattern.
Article in The Canadian journal of urology, 2025. 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiogenomics offers a non-invasive approach to correlate imaging features with tumor molecular profiles. This study aims to identify computed tomography (CT) imaging characteristics associated with positive NIPA-like domain containing 4 (NIPAL4) expression in clear cell renal cell carcinoma (ccRCC) and to develop a radiogenomic predictive model to support personalized risk stratification.
methodsA retrospective analysis was conducted on 241 ccRCC patients from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases. Clinical, pathological, and CT features were compared between NIPAL4-positive and NIPAL4-negative groups. A penalized logistic regression model was built to predict NIPAL4 expression, and its performance was assessed using Receiver Operating Characteristic (ROC) and Decision Curve Analysis (DCA). Additionally, unsupervised K-means clustering was used to identify radiologic phenotypes, and a nomogram was developed to enable individualized risk estimation.
resultsAmong 241 ccRCC patients, 29 (12.03%) showed positive NIPAL4 expression. Compared to NIPAL4-negative cases, positive expression was significantly associated with larger tumor size (median 70.5 mm vs. 52 mm, p = 0.0371), ill-defined margins (61.5% vs. 32.4%, p = 0.0077), perinephric adipose tissue stranding (76.9% vs. 50.0%, p = 0.0114), renal vein thrombosis (24.0% vs. 4.7%, p = 0.021), Gerota's fascia thickening (61.5% vs. 35.2%, p = 0.0163), and collecting system invasion (52.0% vs. 26.5%, p = 0.0171). A multivariate penalized logistic regression model incorporating these features achieved an AUC of 0.973% and 92.1% accuracy in predicting NIPAL4 positivity.
conclusionsPositive NIPAL4 expression in ccRCC is significantly associated with aggressive CT features-particularly perinephric adipose tissue stranding, ill-defined margins, and renal vein thrombosis. A radiogenomic model based on these features achieved excellent predictive performance (AUC = 0.973), supporting its potential role in non-invasive risk stratification and personalized clinical decision-making.
Indexed as
Identifiers
41220353What 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.