Predictive radiomics signature for treatment response to nivolumab in patients with advanced renal cell carcinoma

Authors

  • Eoghan R. Malone Princess Margaret Hospital, Toronto https://orcid.org/0000-0001-9012-7141
  • Hao-Wen Sim Princess Margaret Cancer Centre
  • Audrius Stundzia Tomographix IP Ltd., Toronto
  • Sacha Pierre Joint Division of Medical Imaging, University of Toronto
  • Ur Metser Joint Division of Medical Imaging, University of Toronto
  • Martin O'Malley Joint Division of Medical Imaging, University of Toronto
  • Adrian G. Sacher Princess Margaret Cancer Centre
  • Srikala S. Sridhar Princess Margaret Cancer Centre
  • Aaron R. Hansen Princess Margaret Cancer Centre

DOI:

https://doi.org/10.5489/cuaj.7467

Keywords:

Radiomics, renal cell cancer, immuntherapy, nivolumab, predictive biomarker

Abstract

Introduction: The anti-PD-1 immune checkpoint inhibitor nivolumab is currently approved for the treatment of patients with metastatic renal cell carcinoma (mRCC); approximately 25% of patients respond. We hypothesized that we could identify a biomarker of response using radiomics to train a machine learning classifier to predict nivolumab response outcomes.

Methods: Patients with mRCC of different histologies treated with nivolumab in a single institution between 2013 and 2017 were retrospectively identified. Patients were labelled as responders (complete response [CR]/particle response [PR]/durable stable disease [SD]) or non-responders based on investigator tumor assessment using RECIST 1.1 criteria. For each patient, lesions were contoured from pre-treatment and first post-treatment computed tomography (CT) scans. This information was used to train a radial basis function support vector machine classifier to learn a prediction rule to distinguish responders from non-responders. The classifier was internally validated by a 10-fold nested cross-validation.

Results: Thirty-seven patients were identified; 27 (73%) met the inclusion criteria. One hundred and four lesions were contoured from these 27 patients. The median patient age was 56 years, 78% were male, 89% had clear-cell histology, 89% had prior nephrectomy, and 89% had prior systemic therapy. There were 19 responders vs. eight non-responders. The lesions selected were lymph nodes (60%), lung metastases (23%), and renal/adrenal metastases (17%). For the classifier trained on the baseline CT scans, 69% accuracy was achieved. For the classifier trained on the first post-treatment CT scans, 66% accuracy was achieved.

Conclusions: The set of radiomic signatures was found to have limited ability to discriminate nivolumab responders from non-responders. The use of novel texture features (two-point correlation measure, two-point cluster measure, and minimum spanning tree measure) did not improve performance.

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Published

2021-08-20

How to Cite

Malone, E. R., Sim, H.-W., Stundzia, A., Pierre, S., Metser, U., O’Malley, M., Sacher, A. G., Sridhar, S. S., & Hansen, A. R. (2021). Predictive radiomics signature for treatment response to nivolumab in patients with advanced renal cell carcinoma. Canadian Urological Association Journal, 16(2). https://doi.org/10.5489/cuaj.7467

Issue

Section

Original Research