Journal
JOURNAL OF DIGITAL IMAGING
Volume 34, Issue 5, Pages 1086-1098Publisher
SPRINGER
DOI: 10.1007/s10278-021-00500-y
Keywords
CT; Radiomics; Survival prediction; Machine learning; Renal cell carcinoma
Funding
- Universite de Geneve
- Swiss National Science Foundation
- SNRF [320030_176052]
- Swiss National Science Foundation (SNF) [320030_176052] Funding Source: Swiss National Science Foundation (SNF)
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By combining radiomic features with clinical information, it is possible to predict the overall survival of renal cell carcinoma patients. Clinical features such as tumor grade, tumor malignancy, and pathology t-stage, as well as radiomic features such as flatness, area density, and median, are strongly associated with overall survival time.
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.
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