Predictive radiomics signature for treatment response to nivolumab in patients with advanced renal cell carcinoma
Keywords:Radiomics, renal cell cancer, immuntherapy, nivolumab, predictive biomarker
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.
How to Cite
You, the Author(s), assign your copyright in and to the Article to the Canadian Urological Association. This means that you may not, without the prior written permission of the CUA:
- Post the Article on any Web site
- Translate or authorize a translation of the Article
- Copy or otherwise reproduce the Article, in any format, beyond what is permitted under Canadian copyright law, or authorize others to do so
- Copy or otherwise reproduce portions of the Article, including tables and figures, beyond what is permitted under Canadian copyright law, or authorize others to do so.
The CUA encourages use for non-commercial educational purposes and will not unreasonably deny any such permission request.
You retain your moral rights in and to the Article. This means that the CUA may not assert its copyright in such a way that would negatively reflect on your reputation or your right to be associated with the Article.
The CUA also requires you to warrant the following:
- That you are the Author(s) and sole owner(s), that the Article is original and unpublished and that you have not previously assigned copyright or granted a licence to any other third party;
- That all individuals who have made a substantive contribution to the article are acknowledged;
- That the Article does not infringe any proprietary right of any third party and that you have received the permissions necessary to include the work of others in the Article; and
- That the Article does not libel or violate the privacy rights of any third party.