Enhancing Tumor Microstructural Quantification With Machine Learning and Diffusion-Relaxation MRI

Abstract

Cuantificación microestructural de tumores; Aprendizaje automático; Resonancia magnética de difusión-relajación

Quantificació microestructural del tumor; Aprenentatge automàtic; Ressonància magnètica de difusió-relaxació

Tumor microstructural quantification; Machine learning; Diffusion-relaxation magnetic resonance imaging

We thank the whole medical oncology, radiology, pathology, molecular biology, clinical trial, and IT teams at the Vall d'Hebron Campus. We would also like to express our sincere gratitude to all patients and their families for dedicating their time to research. VHIO acknowledges the State Agency for Research (Agencia Estatal de Investigación) for the financial support as a Center of Excellence Severo Ochoa (CEX2020-001024-S/AEI/10.13039/501100011033), the Cellex Foundation for providing research facilities and equipment and the CERCA Programme from the Generalitat de Catalunya for their support. This research has been supported by PREdICT, sponsored by AstraZeneca. This study has been co-funded by the European Regional Development Fund/European Social Fund “A way to make Europe” (to R.P.L.). RPL is supported by “la Caixa” Foundation, the Prostate Cancer Foundation (18YOUN19), a CRIS Foundation Talent Award (TALENT19-05), the FERO Foundation through the XVIII Fero Fellowship for Oncological Research, the Instituto de Salud Carlos III-Investigación en Salud (PI18/01395 and PI21/01019), the Asociación Española Contra el Cancer (AECC) (PRYCO211023SERR) and the Agency for Management of University and Research Grants of Catalonia (AGAUR) (2023PROD00178). This research has been funded by the CaixaResearch Advanced Oncology Research Program supported by “La Caixa” Foundation (to R.P.L.). The project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/PR22/11920010”, funding F.G. This research has received support from the Beatriu de Pinós Postdoctoral Program from the Secretariat of Universities and Research of the Department of Business and Knowledge of the Government of Catalonia, and the support from the Marie Sklodowska-Curie COFUND program (BP3, contract number 801370; reference 2019 BP 00182) of the H2020 program (to K.B.). C.M. is supported by the Asociación Española Contra el Cancer (PRYCO211023SERR). VHIO is also grateful to the Generalitat de Catalunya, Comissió Interdepartamental de Recerca i Innovació Tecnològica.