# CT-based subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma: an exploratory study of biological mechanisms

## TL;DR

This study uses CT scans and radiomics to predict the T stage of kidney cancer and links these imaging features to biological pathways involved in tumor invasion.

## Contribution

A novel radiomics model combining intratumoral subregions and peritumoral features is developed to predict ccRCC T stage and associated with tumor invasion biology.

## Key findings

- An ML model combining intratumoral subregion and peritumoral RFs achieved AUCs of 0.82 and 0.80 in internal and external validation.
- Higher radiomic scores correlated with poorer overall survival (p < 0.001).
- Radiomics features were linked to biological pathways like ECM remodeling and Hippo signaling.

## Abstract

To 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.

This 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.

Two 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.

An 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.

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.

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.

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.