Integrative Radiogenomics Approach for Risk Assessment of Post-Operative Metastasis in Pathological T1 Renal Cell Carcinoma: A Pilot Retrospective Cohort Study

Abstract

Despite the increasing incidence of pathological stage T1 renal cell carcinoma (pT1 RCC), postoperative distant metastases develop in many surgically treated patients, causing death in certain cases. Therefore, this study aimed to create a radiomics model using imaging features from multiphase computed tomography (CT) to more accurately predict the postoperative metastasis of pT1 RCC and further investigate the possible link between radiomics parameters and gene expression profiles generated by whole transcriptome sequencing (WTS). Four radiomic features, including the minimum value of a histogram feature from inner regions of interest (ROIs) (INNER_Min_hist), the histogram of the energy feature from outer ROIs (OUTER_Energy_Hist), the maximum probability of gray-level co-occurrence matrix (GLCM) feature from inner ROIs (INNER_MaxProb_GLCM), and the ratio of voxels under 80 Hounsfield units (Hus) in the nephrographic phase of postcontrast CT (Under80HURatio), were detected to predict the postsurgical metastasis of patients with pathological stage T1 RCC, and the clinical outcomes of patients could be successfully stratified based on their radiomic risk scores. Furthermore, we identified heterogenous-trait-associated gene signatures correlated with these four radiomic features, which captured clinically relevant molecular pathways, tumor immune microenvironment, and potential treatment strategies. Our results of accurate surrogates using radiogenomics could lead to additional benefit from adjuvant therapy or postsurgical metastases in pT1 RCC.

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

“…Payel Ghosh et al provided a radiomic-genetics pipeline that can extract 3D intra-tumor heterogeneity features from CECT images and explore associations between features and gene mutation status (36). A proposed integrative radiogenomics method could evaluate risk of postoperative metastasis in KIRC with pathological stage T1, which would be beneficial for postsurgical metastasis treatment of KIRC patients (37). Burak Kocak et al provided a radiomic model to predict histopathologic nuclear grade by using the radiomic features extracted from unenhanced CT texture analysis of KIRC tumors (13).…”

Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis

“…A set of recommendations was established for the reporting of the studies assessing prediction models to minimize bias and to increase their clinical usefulness [ 18 ]. A relatively large number of radiomics studies are pilot or feasibility studies, but they do not provide open data or the codes [ 17 , 19 , 20 ]. This prevents validation analyses to confirm the respective results.…”

Radiogenomics Analysis Reveals Tumor Heterogeneity of Triple-Negative Breast Cancer

“…Several studies have made efforts to explore the association of radiomic features and transcriptomic data (18,38). Lee et al (16) found that a four-feature radiomic signature could predict the clinical outcomes of pathological T1 renal cell carcinoma and was associated with the abundance of certain immune cell types. This was consistent with our ndings, but the exact mechanisms of forming an immunosuppressive microenvironment in tumors with low peritumoral heterogeneity were not explored in this article.…”

“…First, radiomics is a non-invasive method to infer tumor characteristics and can be carried out several times during treatment (11,14,15). Moreover, compared to genomic sequencing, which selects only a small part of the tumors, radiomics elucidates the landscape of a tumor and is not subjected to selection bias, and thus could comprehensively explore tumor heterogeneity (16)(17)(18). Previous studies focused on radiomic texture analysis have quanti ed tumor heterogeneity and suggested its associations with unfavorable prognosis in breast cancer (19,20).…”