Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma—Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation

Author:

Duwe Gregor1ORCID,Müller Lukas2,Ruckes Christian3ORCID,Fischer Nikita Dhruva1,Frey Lisa Johanna1,Börner Jan Hendrik1,Rölz Niklas1,Haack Maximilian1,Sparwasser Peter1,Jorg Tobias2,Neumann Christopher C. M.4ORCID,Tsaur Igor1ORCID,Höfner Thomas15,Haferkamp Axel1,Hahn Felix2,Mager Rene1,Brandt Maximilian Peter1ORCID

Affiliation:

1. Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany

2. Department of Diagnostic and Interventional Radiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany

3. Interdisciplinary Center for Clinical Trials Mainz, University Medical Center, Johannes Gutenberg-University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany

4. Department of Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, 10117 Berlin, Germany

5. Department of Urology, Ordensklinikum Linz Elisabethinen, Fadingerstraße 1, 4020 Linz, Austria

Abstract

Background: In the treatment of advanced urothelial (aUC) and renal cell carcinoma (aRCC), biomarkers such as PD-1 and PD-L1 are not robust prognostic markers for immunotherapy (IO) response. Previously, a significant association between IO and a change in splenic volume (SV) was described for several tumour entities. To the best of our knowledge, this study presents the first correlation of SV to IO in aUC and aRCC. Methods: All patients with aUC (05/2017–10/2021) and aRCC (01/2012–05/2022) treated with IO at our academic centre were included. SV was measured at baseline, 3 and 9 months after initiation of IO using an in-house developed convolutional neural network-based spleen segmentation method. Uni- and multivariate Cox regression models for overall survival (OS) and progression-free survival (PFS) were used. Results: In total, 35 patients with aUC and 30 patients with aRCC were included in the analysis. Lower SV at the three-month follow-up was significantly associated with improved OS in the aRCC group. Conclusions: We describe a new, innovative artificial intelligence-based approach of a radiological surrogate marker for IO response in aUC and aRCC which presents a promising new predictive imaging marker. The data presented implicate improved OS with lower follow-up SV in patients with aRCC.

Publisher

MDPI AG

Subject

General Biochemistry, Genetics and Molecular Biology,Medicine (miscellaneous)

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