Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

Journal: Ultrasound in medicine & biology
Published Date: May 25, 2019

Abstract

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naïve Bayes, quadratic discriminant analysis and support vector machines (SVMs)-was evaluated, using statistical inputs from the tumor, cortex and combined statistical inputs from tumor, cortex and medulla. After leave-one-out cross validation, models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Tumor median SWV performed poorly (AUC = 0.62; p = 0.23). Except logistic regression, all machine learning algorithms reached statistical significance using combined statistical inputs (AUC = 0.78-0.98; p < 7.1 × 10). SVMs demonstrated 94% accuracy (AUC = 0.98; p = 3.13 × 10) and clearly outperformed median SWV in differentiating RCC from AML (p = 2.8 × 10).

Authors

  • Hersh Sagreiya
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • Alireza Akhbardeh
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Dandan Li
    School of Medicine, Yangtze University, Jingzhou 434000, China.
  • Rosa Sigrist
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Benjamin I Chung
    Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
  • Geoffrey A Sonn
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
  • Lu Tian
    Department of Health, Research & Policy, Stanford University, Stanford, CA, USA.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.
  • Jürgen K Willmann
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

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