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

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 sever...

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Bibliographic Details
Published in:Biomedicines 2023-09, Vol.11 (9), p.2482
Main Authors: Duwe, Gregor, Müller, Lukas, Ruckes, Christian, Fischer, Nikita Dhruva, Frey, Lisa Johanna, Börner, Jan Hendrik, Rölz, Niklas, Haack, Maximilian, Sparwasser, Peter, Jorg, Tobias, Neumann, Christopher C. M, Tsaur, Igor, Höfner, Thomas, Haferkamp, Axel, Hahn, Felix, Mager, Rene, Brandt, Maximilian Peter
Format: Article
Language:English
Subjects:
Abdomen
advanced renal cell carcinoma
advanced urothelial carcinoma
Antibodies
Antimitotic agents
Antineoplastic agents
Artificial intelligence
Automation
Biomarkers
Carcinoma, Renal cell
Care and treatment
Chemotherapy
immune checkpoint inhibitor
Immunotherapy
Kidney cancer
Ligands
Liver cancer
Medical prognosis
Monoclonal antibodies
Neural networks
Patients
PD-1 protein
PD-L1 protein
predictive marker
prognostic marker
Regression analysis
Renal cell carcinoma
Response rates
Segmentation
Software
Spleen
Statistical analysis
Urothelial carcinoma
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Summary: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.
ISSN:2227-9059
2227-9059
DOI:10.3390/biomedicines11092482