ArticleInsights into imaging2026
CT-based subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma: an exploratory study of biological mechanisms.
Article in Insights into imaging, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesTo evaluate intratumoral subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma (ccRCC), and investigate the biological mechanisms of radiomics. MATERIALS AND
methodsThis retrospective study included 323 ccRCC patients from two centers, divided into training (n = 148), internal test (n = 38), and external validation (n = 137) sets. Patients were stratified into low (T1 and T2, n = 222) and high (T3 and T4, n = 101) T stage groups. The tumors were segmented into different intratumoral subregions via the Gaussian mixture model (GMM). Radiomic features (RFs) were extracted from the whole tumor region (VOI_whole), intratumoral subregions (VOI_subx), and the peritumoral region (VOI_peri). Several machine learning (ML) models and radiomic score (Radscore) were developed to predict pathological T stage and prognosis of ccRCC. Radiogenomics analysis was used to explore the relationship between radiomics and biologic pathways.
resultsTwo intratumoral subregions were segmented. The support vector machine (SVM)-based combined model, constructed using RFs from VOI_sub1 and VOI_peri, achieved the highest AUC values, of 0.82 (95% CI: 0.68-0.96) and 0.80 (95% CI: 0.71-0.88) in the internal test and external validation sets, respectively. A higher Radscore was correlated with poorer overall survival (OS) (p < 0.001). Radiogenomics analysis revealed that radiomics was associated with extracellular matrix remodeling, vesicle transport, protein processing in the endoplasmic reticulum, and the Hippo signaling pathway.
conclusionsAn ML model combining intratumoral subregion and peritumoral RFs showed good performance in predicting the pathological T stage of ccRCC, and these RFs were associated with biological pathways underlying tumor invasion. CRITICAL RELEVANCE STATEMENT: This study develops a validated CT-radiomics model (intratumoral subregions + peritumoral) predicting ccRCC T stage. The prognostic Radscore links to invasion biology (ECM remodeling, Hippo/ER dysregulation), enabling clinical translation. KEY POINTS: Subregional and peritumoral radiomics models accurately predicted ccRCC (clear cell renal cell carcinoma) histological T stage. Radiomics score identified that high-risk ccRCC patients had poorer overall survival. Predictive radiomic features (RFs) were associated with biological pathways underlying tumor invasion.
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.