Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment



Downloads per month over past year

Mansouri, Nesrin and Balvay, Daniel and Zenteno, Omar and Facchin, Caterina and Yoganathan, Thulaciga and Viel, Thomas and López Herráiz, Joaquín and Tavitian, Bertrand and Pérez Liva, Mailyn (2023) Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment. Cancers, 15 (6). p. 1751. ISSN 2072-6694

[thumbnail of cancers-15-01751 (3).pdf]
Creative Commons Attribution.


Official URL:


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.

Item Type:Article
Additional Information:

"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 Imaging
Facility 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"

Uncontrolled Keywords:multi-modal imaging; paraganglioma; machine learning; hierarchical clustering; treatment response
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:77060
Deposited On:22 Mar 2023 10:12
Last Modified:22 Mar 2023 10:12

Origin of downloads

Repository Staff Only: item control page