Abstract
The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.
Highlights
Renal cell carcinoma (RCC) accounts for 2 to 3% of all cancer types [1]
The aim of this work is to investigate the applicability of radiomic features and clinical data for the prediction of RCC patients’ overall survival after partial or radical nephrectomy
After fitting the model for radiomic, clinical, and clinical + radiomic data, features with non-zero coefficients were listed according to their importance value
Summary
Renal cell carcinoma (RCC) accounts for 2 to 3% of all cancer types [1]. Worldwide, RCC is the tenth and sixth commonly diagnosed cancer in women and men, respectively [2]. Predictive and prognostic models have a pivotal role in treatment and management, precision medicine, and prediction of the overall cancer outcome [6]. The most important prognostic model and commonly accepted staging system for RCC is The American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging [7]. This system is limited to anatomic prognostic factors. The rapid expansion of our understanding of cancer biology and the development of novel effective treatments along with advancements in medical imaging technology have pushed researchers toward looking beyond the TNM staging system and developing new predictive models [7]. Recent studies have proposed prognostic factors, including histologic, clinical, genomic, and imaging features [8,9,10,11]
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