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Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy

Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic...

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Autores principales: Trebeschi, Stefano, Bodalal, Zuhir, van Dijk, Nick, Boellaard, Thierry N., Apfaltrer, Paul, Tareco Bucho, Teresa M., Nguyen-Kim, Thi Dan Linh, van der Heijden, Michiel S., Aerts, Hugo J. W. L., Beets-Tan, Regina G. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056079/
https://www.ncbi.nlm.nih.gov/pubmed/33889546
http://dx.doi.org/10.3389/fonc.2021.637804
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author Trebeschi, Stefano
Bodalal, Zuhir
van Dijk, Nick
Boellaard, Thierry N.
Apfaltrer, Paul
Tareco Bucho, Teresa M.
Nguyen-Kim, Thi Dan Linh
van der Heijden, Michiel S.
Aerts, Hugo J. W. L.
Beets-Tan, Regina G. H.
author_facet Trebeschi, Stefano
Bodalal, Zuhir
van Dijk, Nick
Boellaard, Thierry N.
Apfaltrer, Paul
Tareco Bucho, Teresa M.
Nguyen-Kim, Thi Dan Linh
van der Heijden, Michiel S.
Aerts, Hugo J. W. L.
Beets-Tan, Regina G. H.
author_sort Trebeschi, Stefano
collection PubMed
description Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods. Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival. Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p < 0.001) for abdominal imaging, and 0.67 AUC (p < 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p < 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p < 0.001), suggesting its complementary value in current clinical settings. Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings.
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spelling pubmed-80560792021-04-21 Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy Trebeschi, Stefano Bodalal, Zuhir van Dijk, Nick Boellaard, Thierry N. Apfaltrer, Paul Tareco Bucho, Teresa M. Nguyen-Kim, Thi Dan Linh van der Heijden, Michiel S. Aerts, Hugo J. W. L. Beets-Tan, Regina G. H. Front Oncol Oncology Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods. Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival. Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p < 0.001) for abdominal imaging, and 0.67 AUC (p < 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p < 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p < 0.001), suggesting its complementary value in current clinical settings. Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings. Frontiers Media S.A. 2021-04-06 /pmc/articles/PMC8056079/ /pubmed/33889546 http://dx.doi.org/10.3389/fonc.2021.637804 Text en Copyright © 2021 Trebeschi, Bodalal, van Dijk, Boellaard, Apfaltrer, Tareco Bucho, Nguyen-Kim, van der Heijden, Aerts and Beets-Tan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Trebeschi, Stefano
Bodalal, Zuhir
van Dijk, Nick
Boellaard, Thierry N.
Apfaltrer, Paul
Tareco Bucho, Teresa M.
Nguyen-Kim, Thi Dan Linh
van der Heijden, Michiel S.
Aerts, Hugo J. W. L.
Beets-Tan, Regina G. H.
Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
title Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
title_full Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
title_fullStr Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
title_full_unstemmed Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
title_short Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
title_sort development of a prognostic ai-monitor for metastatic urothelial cancer patients receiving immunotherapy
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056079/
https://www.ncbi.nlm.nih.gov/pubmed/33889546
http://dx.doi.org/10.3389/fonc.2021.637804
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