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A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma

  • Nima Nassiri
    Correspondence
    Corresponding author. Department of Urology, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Tel. +1 323-442-9550.
    Affiliations
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
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  • Marissa Maas
    Affiliations
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
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  • Giovanni Cacciamani
    Affiliations
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA

    Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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  • Bino Varghese
    Affiliations
    Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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  • Darryl Hwang
    Affiliations
    Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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  • Xiaomeng Lei
    Affiliations
    Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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  • Monish Aron
    Affiliations
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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  • Mihir Desai
    Affiliations
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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  • Assad A. Oberai
    Affiliations
    Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA

    Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
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  • Steven Y. Cen
    Affiliations
    Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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  • Inderbir S. Gill
    Affiliations
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
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  • Vinay A. Duddalwar
    Affiliations
    USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA

    Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Published:September 16, 2021DOI:https://doi.org/10.1016/j.euf.2021.09.004

      Abstract

      Background

      A substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate.

      Objective

      To determine whether radiomic-based machine learning platforms can distinguish benign from malignant renal masses.

      Design, setting, and participants

      A prospectively maintained single-institutional renal mass registry was queried to identify patients with a computed tomography–proven clinically localized renal mass who underwent partial or radical nephrectomy.

      Intervention

      Radiomic analysis of preoperative scans was performed. Clinical and radiomic variables of importance were identified through decision tree analysis, which were incorporated into Random Forest and REAL Adaboost predictive models.

      Outcome measurements and statistical analysis

      The primary outcome was the degree of congruity between the virtual diagnosis and final pathology. Subanalyses were performed for small renal masses and patients who had percutaneous renal mass biopsies as part of their workup. Receiver operating characteristic curves were used to evaluate each model’s discriminatory function.

      Results and limitations

      A total of 684 patients met the selection criteria. Of them, 76% had renal cell carcinoma; 57% had small renal masses, of which 73% were malignant. Predictive modeling differentiated benign pathology from malignant with an area under the curve (AUC) of 0.84 (95% confidence interval [CI] 0.79–0.9). In small renal masses, radiomic analysis yielded a discriminatory AUC of 0.77 (95% CI 0.69–0.85). When negative and nondiagnostic biopsies were supplemented with radiomic analysis, accuracy increased from 83.3% to 93.4%.

      Conclusions

      Radiomic-based predictive modeling may distinguish benign from malignant renal masses. Clinical factors did not substantially improve the diagnostic accuracy of predictive models. Enhanced diagnostic predictability may improve patient selection before surgery and increase the utilization of active surveillance protocols.

      Patient summary

      Not all kidney tumors are cancerous, and some can be watched. We evaluated a new method that uses radiographic features invisible to the naked eye to distinguish benign masses from true cancers and found that it can do so with acceptable accuracy.

      Keywords

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