Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

Zahra Khodabakhshi, Mehdi Amini, Shayan Mostafaei, Atlas Haddadi Avval, Mostafa Nazari, Mehrdad Oveisi, Isaac Shiri, Habib Zaidi*

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    26 Citations (Scopus)
    19 Downloads (Pure)


    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.

    Original languageEnglish
    Pages (from-to)1086-1098
    Number of pages13
    Issue number5
    Publication statusPublished - Oct-2021


    • CT
    • Radiomics
    • Survival prediction
    • Machine learning
    • Renal cell carcinoma
    • GRADE
    • MODELS
    • SIZE

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