Sub-regional Radiomics Analysis for Predicting Metastasis Risk in Clear Cell Renal Cell Carcinoma: A Multicenter Retrospective Study.

ABSTRACT

RATIONALE AND OBJECTIVES: Clear cell renal cell carcinoma (ccRCC) is the most common malignant neoplasm affecting the kidney, exhibiting a dismal prognosis in metastatic instances. Elucidating the composition of ccRCC holds promise for the discovery of highly sensitive biomarkers. Our objective was to utilize habitat imaging techniques and integrate multimodal data to precisely predict the risk of metastasis, ultimately enabling early intervention and enhancing patient survival rates. MATERIAL AND METHODS: A retrospective analysis was performed on a cohort of 263 patients with ccRCC from three hospitals between April 2013 and March 2021. Preoperative CT images, ultrasound images, and clinical data were comprehensively analyzed. Patients from two campuses of Qilu Hospital of Shandong University were assigned to the training dataset, while the third hospital served as the independent testing dataset. A robust consensus clustering method was used to classify the primary tumor space into distinct sub-regions (i.e., habitats) using contrast-enhanced CT images. Radiomic features were extracted from these tumor sub-regions and subsequently reduced to identify meaningful features for constructing a predictive model for ccRCC metastasis risk assessment. In addition, the potential value of radiomics in predicting ccRCC metastasis risk was explored by integrating ultrasound image features and clinical data to construct and compare alternative models. RESULTS: In this study, we performed k-means clustering within the tumor region to generate three distinct tumor subregions. We quantified the Hounsfiled Unit (HU) value, volume fraction, and distribution of high- and low-risk groups in each subregion. Our investigation focused on 252 patients with Habitat1 + Habitat3 to assess the discriminative power of these two subregions. We then developed a risk prediction model for ccRCC metastasis risk classification based on radiomic features extracted from CT and ultrasound images, and clinical data. The Combined model and the CT_Habitat3 model showed AUC values of 0.935 [95%CI 0.902-0.968] and 0.934 [95%CI 0.902-0.966], respectively, in the training dataset, while in the independent testing dataset, they achieved AUC values of 0.891 [95%CI 0.794-0.988] and 0.903 [95%CI 0.819-0.987], respectively. CONCLUSION: We have identified a non-invasive imaging predictor and the proposed sub-regional radiomics model can accurately predict the risk of metastasis in ccRCC. This predictive tool has potential for clinical application to refine individualized treatment strategies for patients with ccRCC.