TY - JOUR
T1 - Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
AU - Trebeschi, Stefano
AU - Bodalal, Zuhir
AU - van Dijk, Nick
AU - Boellaard, Thierry N.
AU - Apfaltrer, Paul
AU - Tareco Bucho, Teresa M.
AU - Nguyen-Kim, Thi Dan Linh
AU - van der Heijden, Michiel S.
AU - Aerts, Hugo J. W. L.
AU - Beets-Tan, Regina G. H.
N1 - Funding Information: This work was also carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. TN-K was funded by the Oncologic Imaging Fellowship Grant from the European Society of Radiology. Funding Information: The authors would also like to thank NVIDIA for their kind donation via the Academic GPU Grant Program as well as the Maurits en Anna de Kock Stichting for its financial support. Publisher Copyright: © Copyright © 2021 Trebeschi, Bodalal, van Dijk, Boellaard, Apfaltrer, Tareco Bucho, Nguyen-Kim, van der Heijden, Aerts and Beets-Tan. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4/6
Y1 - 2021/4/6
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - checkpoint inhibitors
KW - imaging - computed tomography
KW - immunotherapy
KW - prognostication
KW - response assessment
KW - treatment monitoring
KW - urothelial cancer
UR - http://www.scopus.com/inward/record.url?scp=85104680632&partnerID=8YFLogxK
U2 - https://doi.org/10.3389/fonc.2021.637804
DO - https://doi.org/10.3389/fonc.2021.637804
M3 - Article
C2 - 33889546
SN - 2234-943X
VL - 11
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 637804
ER -