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.
Highlights
Renal cell carcinoma (RCC) originates from the highly heterogeneous renal tubular epithelium, resulting in significant inter- and intratumoral heterogeneity in tumor metastasis and therapeutic responses [1,2,3].In recent years, due to the widespread use of multiparametric imaging, most RCCs have been detected at pathological T1 (75%) with a 97% 5-year survival rate, suggesting that most localized RCCs can be surgically treated [1]
Only 11 samples among our radiomics discovery cohort were used in the genomic discovery cohort due to the difficulty in obtaining frozen samples that had adequate quality and quantity for the whole transcriptome sequencing (WTS), we further explored the molecular underpinning of the identified all-relevant features by evaluating their possible radiogenomics link using the RNA-Seq technology
Our study showed that a radiomics signature is a strong preoperative predictor for the distant metastasis of T1 RCC, which might lead to a better stratification of patients for surgery, enabling clinicians to choose optimal treatment strategies and individualized monitoring protocols to enhance clinical outcomes
Summary
Due to the widespread use of multiparametric imaging, most RCCs have been detected at pathological T1 (pT1) (75%) with a 97% 5-year survival rate, suggesting that most localized RCCs can be surgically treated [1]. The evaluation of patients for postnephrectomy adjuvant therapy relies mainly on the tumor, nodes, and metastasis (TNM) staging system [3]. These RCC subsets exhibit markedly different tumor aggressiveness and diverse clinical outcomes [1,4]. Radiomics has several advantages, including noninvasiveness, ease of use for serial monitoring, clinical implementation using standard-of-care imaging, and data acquisition from the entire heterogeneous tumor [5,8,9]. Radiogenomics can integrate multiscale data at the fine-grained genome level to more macro-multiparametric imaging data through high-throughput computing to develop new tools to provide insight into relationships between imaging and cellular and subcellular data, reflecting underlying multifaceted and heterogeneous phenotypes [5,6,7,10,11]
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