RT Journal Article
T1 Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment
A1 Mansouri, Nesrin
A1 Balvay, Daniel
A1 Zenteno, Omar
A1 Facchin, Caterina
A1 Yoganathan, Thulaciga
A1 Viel, Thomas
A1 López Herraiz, Joaquín
A1 Tavitian, Bertrand
A1 Pérez Liva, Mailyn
AB The standard assessment of response to cancer treatments is based on gross tumor characteristics, such as tumor size or glycolysis, which provide very indirect information about the effect of precision treatments on the pharmacological targets of tumors. Several advanced imaging modalities allow for the visualization of targeted tumor hallmarks. Descriptors extracted from these images can help establishing new classifications of precision treatment response. We propose a machine learning (ML) framework to analyze metabolic–anatomical–vascular imaging features from positron emission tomography, ultrafast Doppler, and computed tomography in a mouse model of paraganglioma undergoing anti-angiogenic treatment with sunitinib. Imaging features from the follow-up of sunitinib-treated (n = 8, imaged once-per-week/6-weeks) and sham-treated (n = 8, imaged once-per-week/3-weeks) mice groups were dimensionally reduced and analyzed with hierarchical clustering Analysis (HCA). The classes extracted from HCA were used with 10 ML classifiers to find a generalized tumor stage prediction model, which was validated with an independent dataset of sunitinib-treated mice. HCA provided three stages of treatment response that were validated using the best-performing ML classifier. The Gaussian naive Bayes classifier showed the best performance, with a training accuracy of 98.7 and an average area under curve of 100. Our results show that metabolic–anatomical–vascular markers allow defining treatment response trajectories that reflect the efficacy of an anti-angiogenic drug on the tumor target hallmark.
SN 2072-6694
YR 2023
FD 2023
LK https://hdl.handle.net/20.500.14352/73096
UL https://hdl.handle.net/20.500.14352/73096
LA spa
NO "Funding: This work received funding from the Cancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC), from the Plan Cancer Physicancer (grant C16025KS), and from the Région Ile-de-France. In vivo imaging was performed at the Life ImagingFacility of Université Paris Cité (Plateforme Imageries du Vivant - PIV), supported by France Life Imaging (grant ANR-11-INBS-0006) and Infrastructures Biologie-Santé (IBiSa). Nesrin Mansouri received a scholarship from the Ministère de l’Enseignement Supérieur et de la Recherche. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement no. 101030046 of M. P.-L.""Acknowledgments: The authors thank Laure Fournier, Judith Favier, Charlotte Lussey-Lepoutre,Irène Buvat, Béatrice Berthon and J.M. Udías for rich scientific advice and discussions"
NO Cancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC)
NO Plan Cancer Physicancer
NO Région Ile-de-France
NO e Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant - PIV)
NO France Life Imaging
NO Infrastructures Biologie-Santé (IBiSa)
NO Ministère de l’Enseignement Supérieur et de la Recherche
NO European Union’s Horizon 2020 research and innovation program
DS Docta Complutense
RD 3 may 2024