Grup de Radiòmica

INVESTIGACIÓ BIOMÈDICA

El Grup de Radiòmica del VHIO està dedicat a impulsar l’oncologia de precisió mitjançant el desenvolupament i la translació clínica d’eines d’intel·ligència artificial (IA) i integració de dades, amb un enfocament particular en la imatge mèdica. La nostra visió és aprofitar tot el potencial de la radiómica i la modelització computacional per transformar la cura del càncer, fent que els diagnòstics siguin més precisos, els tractaments més personalitzats i es millorin els resultats dels pacients.

Combinant experiència en enginyeria i aprenentatge automàtic, treballem en el descobriment, validació i qualificació clínica de biomarcadors d’imatge que orientin i guiïn la presa de decisions terapèutiques. També estem fermament compromesos a donar suport al desenvolupament de fàrmacs mitjançant l’ús de tècniques d’imatge funcional en assaigs clínics, la qual cosa permet obtenir informació en temps real sobre la resposta al tractament.

A través del nostre enfocament interdisciplinari, sòlides col·laboracions i compromís amb la innovació, el Grup de Radiòmica del VHIO aspira a situar-se a l’avantguarda de l’oncologia computacional, marcant el futur del diagnòstic i de les teràpies oncològiques.

Raquel Perez-Lopez

Cap de grup

Biosketch

Francesco Grussu

Investigador Senior

Biosketch

Línies de recerca

Desenvolupar i optimitzar pipelines per a models d’IA d’integració de dades amb un enfocament particular en la imatge mèdica i en la integració de models explicables.
Aportar experiència en enginyeria i bioinformàtica per al desenvolupament i la qualificació clínica de biomarcadors d’imatge per a l’oncologia de precisió, amb l’objectiu de millorar els resultats dels pacients amb càncer.
Utilitzar la imatge funcional per optimitzar el desenvolupament de fàrmacs en assaigs clínics.
Integrar la radiómica amb altres òmiques en estudis traslacionals per assolir una comprensió més profunda de l’evolució tumoral i dels mecanismes de resistència a les teràpies antitumorals.

Equip

Cap de grup

Raquel Perez-Lopez

Investigador Sènior

Francesco Grussu

Estudiants de posdoctorat

Alonso García

Estudiants de doctorat

Athanasios Grigoriou

Olivia Prior

Anna Voronova

Daniel Navarro

Maria Balaguer

Marta Buetas

Carlos Macarro

Estudiants

Eva Magallón

Tècnica de laboratori

Cristina Mendoza

Informàtics

Adrià Marcos

Camilo Monreal

Research Fellow

Luz María Atlagich

Nikolaos Staikoglou

Data curator

Christina Zatse

Publicacions

Navarro-Garcia D, Grussu F, Zatse C, Klümper N, Macarro C, Hernando-Calvo A, Sanz M, Vieito M, Braña I, Mirallas O, Alonso G, Galvao V, Pretelli G, Lostes J, Oberoi A, Toledo R, Nuciforo P, Garralda E, Perez-Lopez R. Integrating C-reactive protein flare and early MRI dynamics for enhanced prediction of immunotherapy response. J Immunother Cancer. 2025 Dec 21;13(12):e012143.
Balaguer-Montero M, Marcos Morales A, Ligero M, Zatse C, Leiva D, Atlagich LM, Staikoglou N, Viaplana C, Monreal C, Mateo J, Hernando J, García-Álvarez A, Salvà F, Capdevila J, Elez E, Dienstmann R, Garralda E, Perez-Lopez R. A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer. Cell Rep Med. 2025 Apr 15;6(4):102032.
Voronova AK, Grigoriou A, Bernatowicz K, Simonetti S, Serna G, Roson N, Escobar M, Vieito M, Nuciforo P, Toledo R, Garralda E, Fieremans E, Novikov DS, Palombo M, Perez-Lopez R, Grussu F. SpinFlowSim: A blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer. Med Image Anal. 2025 May;102:103531.
Mathes S, Ferber D, Dreyer T, Borm KJ, Modersohn L, Willem T, Dirven R, Vibert J, Kreutzfeldt S, Perez-Lopez R, Prelaj A, Strand F, Baird RD, Boeker M, Kather JN, Tschochohei M, Lammert J. Collaborative framework on responsible AI in LLM-driven CDSS for precision oncology leveraging real-world patient data. NPJ Precis Oncol. 2025 Dec 4;10(1):15. doi: 10.1038/s41698-025-01180-5. PMID: 41345244; PMCID: PMC12796327.
Grussu F, Grigoriou A, Bernatowicz K, Palombo M, Casanova-Salas I, Navarro-Garcia D, Barba I, Simonetti S, Serna G, Macarro C, Voronova AK, Garay V, Corral JF, Vidorreta M, García-Polo García P, Merino X, Mast R, Rosón N, Escobar M, Vieito M, Toledo R, Nuciforo P, Mateo J, Garralda E, Perez-Lopez R. Clinically feasible liver tumour cell size measurement through histology-informed in vivo diffusion MRI. Commun Med (Lond). 2025 Nov 20;5(1):535. doi: 10.1038/s43856-025-01246-2. PMID: 41266804; PMCID: PMC12749696.
Aldea M, Salto-Tellez M, Marra A, Umeton R, Stenzinger A, Koopman M, Prelaj A, Kehl KL, Gilbert S, Leßmann ME, Lipkova J, Provenzano L, Meric-Bernstam F, Halabi S, Wu J, Pellat A, Suijkerbuijk KPM, Besse B, Ryll B, Marchió C, Crispin-Ortuzar M, Fehrmann R, Vibert J, Ferber D, Pauli C, Valachis A, Corso F, Brinker TJ, Mateo J, Harbeck N, Winkler EC, Lopez-Rios F, Perez-Lopez R, Pentheroudakis G, Delaloge S, Benedikt Westphalen C, Kather JN. ESMO basic requirements for AI-based biomarkers in oncology (EBAI). Ann Oncol. 2025 Nov 18:S0923-7534(25)06267-2. doi: 10.1016/j.annonc.2025.11.009. Epub ahead of print. PMID: 41260261.
Palombo M, Bodini B, Grussu F, Le Bihan D, Nilsson M, Perez-Lopez R, Oei EHG, Schoots IG, Smits M, Jelescu IO. ESR Essentials: diffusion-weighted MRI-practice recommendations by the European Society for Magnetic Resonance in Medicine and Biology. Eur Radiol. 2025 Oct 2. doi: 10.1007/s00330-025-12033-x. Epub ahead of print. PMID: 41037070.
Simulation-informed evaluation of microvascular parameter mapping for diffusion MR imaging of solid tumours AK Voronova, O Prior, A Grigoriou, et al. medRxiv, 2025.08. 27.25334553
de Grandis MC, Baraibar I, Prior O, Balaguer-Montero M, Salvà F, Ros J, Rodríguez-Castells M, Tabernero J, Lonardi S, Perez-Lopez R, Élez E. Differentiating low tumor burden from oligometastatic disease in colorectal cancer: a call for individualized therapeutic approaches. ESMO Open. 2025 Aug;10(8):105520. doi: 10.1016/j.esmoop.2025.105520. Epub 2025 Aug 12. PMID: 40803019; PMCID: PMC12361753.
Clusmann J, Balaguer-Montero M, Bassegoda O, Schneider CV, Seraphin T, Paintsil E, Luedde T, Lopez RP, Calderaro J, Gilbert S, Marjot T, Spann A, Shawcross DL, Lens S, Trépo E, Kather JN. The barriers to uptake of artificial intelligence in hepatology and how to overcome them. J Hepatol. 2025 Dec;83(6):1410-1426. doi: 10.1016/j.jhep.2025.07.003. Epub 2025 Jul 18. PMID: 40920593.
Real-world radiology data for artificial intelligence-driven cancer support systems and biomarker development D Navarro-Garcia, A Marcos, R Beets-Tan, et al. ESMO Real World Data and Digital Oncology
Voronova AK, Grigoriou A, Bernatowicz K, Simonetti S, Serna G, Roson N, Escobar M, Vieito M, Nuciforo P, Toledo R, Garralda E, Fieremans E, Novikov DS, Palombo M, Perez-Lopez R, Grussu F. SpinFlowSim: A blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer. Med Image Anal. 2025 May;102:103531. doi: 10.1016/j.media.2025.103531. Epub 2025 Mar 7. PMID: 40073583; PMCID: PMC12034030.
Balaguer-Montero M, Marcos Morales A, Ligero M, Zatse C, Leiva D, Atlagich LM, Staikoglou N, Viaplana C, Monreal C, Mateo J, Hernando J, García-Álvarez A, Salvà F, Capdevila J, Elez E, Dienstmann R, Garralda E, Perez-Lopez R. A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer. Cell Rep Med. 2025 Apr 15;6(4):102032. doi: 10.1016/j.xcrm.2025.102032. Epub 2025 Mar 20. PMID: 40118052; PMCID: PMC12047525.
Saldanha OL, Zhu J, Müller-Franzes G, Carrero ZI, Payne NR, Escudero Sánchez L, Varoutas PC, Kyathanahally S, Laleh NG, Pfeiffer K, Ligero M, Behner J, Abdullah KA, Apostolakos G, Kolofousi C, Kleanthous A, Kalogeropoulos M, Rossi C, Nowakowska S, Athanasiou A, Perez-Lopez R, Mann R, Veldhuis W, Camps J, Schulz V, Wenzel M, Morozov S, Ciritsis A, Kuhl C, Gilbert FJ, Truhn D, Kather JN. Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging. Commun Med (Lond). 2025 Feb 6;5(1):38. doi: 10.1038/s43856-024-00722-5. PMID: 39915630; PMCID: PMC11802753.
Bernatowicz K, Amat R, Prior O, Frigola J, Ligero M, Grussu F, Zatse C, Serna G, Nuciforo P, Toledo R, Escobar M, Garralda E, Felip E, Perez-Lopez R. Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction. J Immunother Cancer. 2025 Jan 11;13(1):e009140. doi: 10.1136/jitc-2024-009140. PMID: 39800381; PMCID: PMC11749429.
Grigoriou A, Macarro C, Palombo M, Navarro-Garcia D, Voronova AK, Bernatowicz K, Barba I, Escriche A, Greco E, Abad M, Simonetti S, Serna G, Mast R, Merino X, Roson N, Escobar M, Vieito M, Nuciforo P, Toledo R, Garralda E, Sala-Llonch R, Fieremans E, Novikov DS, Perez-Lopez R, Grussu F. Histology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework. Commun Biol. 2025 Nov 26;8(1):1695. doi: 10.1038/s42003-025-09096-3. PMID: 41298809; PMCID: PMC12657972.
Perez-Lopez R, Ghaffari Laleh N, Mahmood F, Kather JN. A guide to artificial intelligence for cancer researchers. Nat Rev Cancer. 2024 Jun;24(6):427-441. doi: 10.1038/s41568-024-00694-7. Epub 2024 May 16.
Garcia-Ruiz A, Macarro C, Zacchi F, Morales-Barrera R, Grussu F, Casanova-Salas I, Sanguedolce F, Gonzalez M, Cresta-Morgado P, de Albert M, Garcia-Bennett J, Marmolejo D, Planas J, Roche S, Mast R, Zatse C, Piulats JM, Herrera-Imbroda B, Regis L, Agundez L, Olmos D, Calvo N, Escobar M, Carles J, Mateo J, Perez-Lopez R*. Whole-body Magnetic Resonance Imaging as a Treatment Response Biomarker in Castration-resistant Prostate Cancer with Bone Metastases: The iPROMET Clinical Trial. Eur Urol. 2024 Sep;86(3):272-274. doi: 10.1016/j.eururo.2024.02.016. Epub 2024 Mar 14.
Garcia-Ruiz A, Pons-Escoda A, Grussu F, Naval-Baudin P, Monreal-Aguero C, Hermann G, Karunamuni R, Ligero M, Lopez-Rueda A, Oleaga L, Berbís MÁ, Cabrera-Zubizarreta A, Martin-Noguerol T, Luna A, Seibert TM, Majos C, Perez-Lopez R*. An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI. Cell Rep Med. 2024 Mar 19;5(3):101464. doi: 10.1016/j.xcrm.2024.101464. Epub 2024 Mar 11.
Prior O, Macarro C, Navarro V, Monreal C, Ligero M, Garcia-Ruiz A, Serna G, Simonetti S, Braña I, Vieito M, Escobar M, Capdevila J, Byrne AT, Dienstmann R, Toledo R, Nuciforo P, Garralda E, Grussu F, Bernatowicz K, Perez-Lopez R*. Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer. Radiol Artif Intell. 2024 Mar;6(2):e230118. doi: 10.1148/ryai.230118. Erratum in: Radiol Artif Intell. 2024 May;6(3):e249001. doi: 10.1148/ryai.249001.
Ligero M, Gielen B, Navarro V, Cresta Morgado P, Prior O, Dienstmann R, Nuciforo P, Trebeschi S, Beets-Tan R, Sala E, Garralda E, Perez-Lopez R*. A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking? NPJ Precis Oncol. 2024 Feb 21;8(1):42. doi: 10.1038/s41698-024-00534-9.
Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging. 2024 Oct;60(4):1278-1304. doi: 10.1002/jmri.29144. Epub 2023 Nov 30.
Ligero M, Serna G, El Nahhas OSM, Sansano I, Mauchanski S, Viaplana C, Calderaro J, Toledo RA, Dienstmann R, Vanguri RS, Sauter JL, Sanchez-Vega F, Shah SP, Ramón Y Cajal S, Garralda E, Nuciforo P, Perez-Lopez R, Kather JN. Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression. Cancer Res Commun. 2024 Jan 11;4(1):92-102. doi: 10.1158/2767-9764.CRC-23-0287.
Ghaffari Laleh N, Ligero M, Perez-Lopez R, Kather JN. Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer. Clin Cancer Res. 2023 Jan 17;29(2):316-323.
Ligero, M., Hernando, J., Delgado, E. et al. Radiomics and outcome prediction to antiangiogenic treatment in advanced gastroenteropancreatic neuroendocrine tumours: findings from the phase II TALENT trial. BJC Rep 1, 9 (2023).
Ligero M, Simó M, Carpio C, Iacoboni G, Balaguer-Montero M, Navarro V, Sánchez-Salinas MA, Bobillo S, Marín-Niebla A, Iraola-Truchuelo J, Abrisqueta P, Sala-Llonch R, Bosch F, Perez-Lopez R, Barba P. PET-based radiomics signature can predict durable responses to CAR T-cell therapy in patients with large B-cell lymphoma. EJHaem. 2023 Sep 11;4(4):1081-1088.
Ramlee S, Hulse D, Bernatowicz K, Pérez-López R, Sala E, Aloj L. Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers (Basel). 2022 Jul 27;14(15):3656.
Grussu F, Bernatowicz K, Casanova-Salas I, Castro N, Nuciforo P, Mateo J, Barba I, Perez-Lopez R. Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology. Magn Reson Med. 2022 Jul;88(1):365-379.
Elez E, Ros J, Fernández J, Villacampa G, Moreno-Cárdenas AB, Arenillas C, Bernatowicz K, Comas R, Li S, Kodack DP, Fasani R, Garcia A, Gonzalo-Ruiz J, Piris-Gimenez A, Nuciforo P, Kerr G, Intini R, Montagna A, Germani MM, Randon G, Vivancos A, Smits R, Graus D, Perez-Lopez R, Cremolini C, Lonardi S, Pietrantonio F, Dienstmann R, Tabernero J, Toledo RA. RNF43 mutations predict response to anti-BRAF/EGFR combinatory therapies in BRAFV600E metastatic colorectal cancer. Nat Med. 2022 Oct;28(10):2162-2170.
Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Grussu F, Fernandez JJS, Simo AC, Sarro NV, Fernandez-Coello A, Bruna J, Cos M, Perez-Lopez R, Majos C. Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis. Eur Radiol. 2022 Jun;32(6):3705-3715.
Grussu, F; et al. Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: insights from simulations, 9.4T imaging and histology. Magnetic Resonance in Medicine. 2022. doi: 10.1002/mrm.29174 (en prensa).
Pons-Escoda A, García-Ruiz A, Naval-Baudin P, Grussu F, Fernández JJS, Simóo AC, Sarróo NV, Fernández-Coello A, Bruna J, Cos M, Pérez-López R, Majos C. Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis. Eur Radiol. 2022 Feb 1. DOI: 10.1007/s00330-021-08498-1.
Bernatowicz, K., Grussu, F., Ligero, M. et al. Robust imaging habitat computation using voxel-wise radiomics features. Sci Rep 11, 20133 (2021). doi: 10.1038/s41598-021-99701-2.
Ligero M, García-Ruiz A, Viaplana C, Villacampa G, Raciti MV, Landa J, Matos I, Martín-Liberal J, Ochoa-de-Olza M, Hierro C, Mateo J, González M, Morales-Barrera R, Suárez C, Rodón J, Elez E, Braña I, Muñoz-Couselo E, Oaknin A, Fasani R, Nuciforo P, Gil D, Rubio-Pérez C, Seoane J, Felip E, Escobar M, Tabernero J, Carles J, Dienstmann R, Garralda E, Pérez-López R. A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors. Radiology. 2021 Apr;299(1):109-119. doi: 10.1148/radiol.2021200928.
García-Ruiz A, Naval-Baudin P, Ligero M, Pons-Escoda A, Bruna J, Plans G, Calvo N, Cos M, Majós C, Pérez-López R. Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma. Sci Rep. 2021 Jan 12;11(1):695. doi: 10.1038/s41598-020-79829-3.
Ligero M, Jordi-Ollero O, Bernatowicz K, García-Ruiz A, Delgado-Muñoz E, Leiva D, Mast R, Suárez C, Sala-Llonch R, Calvo N, Escobar M, Navarro-Martín A, Villacampa G, Dienstmann R, Pérez-López R. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur Radiol. 2021 Mar;31(3):1460-1470. doi: 10.1007/s00330-020-07174-0.
Zunder SM, Pérez-López R, de Kok BM, Raciti MV, van Pelt GW, Dienstmann R, García-Ruiz A, Meijer CA, Gelderblom H, Tollenaar RA, Nuciforo P, Wasser MN, Mesker WE. Correlation of the tumour-stroma ratio with diffusion weighted MRI in rectal cancer. Eur J Radiol. 2020 Dec;133:109345. doi: 10.1016/j.ejrad.2020.109345.
Matos I, Martín-Liberal J, García-Ruiz A, Hierro C, Ochoa de Olza M, Viaplana C, Azaro A, Vieito M, Braña I, Mur G, Ros J, Mateos J, Villacampa G, Berché R, Oliveira M, Alsina M, Elez E, Oaknin A, Muñoz-Couselo E, Carles J, Felip E, Rodón J, Tabernero J, Dienstmann R, Pérez-López R, Garralda E. Capturing Hyperprogressive Disease with Immune-Checkpoint Inhibitors Using RECIST 1.1 Criteria. Clin Cancer Res. 2020 Apr 15;26(8):1846-1855. doi: 10.1158/1078-0432.CCR-19-2226.

Projectes

ProstateVISION: Transforming BRCAness Biomarker Detection through Deep Learning is a newly awarded project funded by the FERO Foundation and led by Raquel Perez-Lopez. The project leverages deep learning applied to multiparametric prostate MRI to enable non-invasive identification of BRCAness-related phenotypes. By integrating radiomics, artificial intelligence, and clinical–genomic data, ProstateVISION aims to refine patient stratification, inform treatment decisions, and advance precision medicine strategies in prostate cancer.
At the institutional level, the group established a close collaboration with the VHIO Sarcoma Unit through a study coordinated by Claudia Valverde under GEIS support. Within this framework, the group participates in the multicentric study PRESARC (PREdicción en SARComas retroperitoneales), focused on the application of radiomics and artificial intelligence for histological prediction and surgical planning in retroperitoneal sarcomas. This collaboration enables the integration of advanced imaging biomarkers into complex clinical decision-making pathways and represents a clear example of successful interdisciplinary research between imaging scientists, oncologists and surgical oncology teams.
In parallel, the Radiomics Group expanded its collaboration with the Urogenital Cancer Unit at VHIO, coordinated by Cristina Suárez, through a Fondo de Investigaciones Sanitarias (FIS) project funded by the Spanish Ministry of Health. This collaboration focuses on the development and validation of AI-driven imaging biomarkers in renal cancer, strengthening the group’s role in nationally funded precision oncology initiatives.
Industry engagement also increased during this period. The group expanded its collaborations with pharmaceutical and technology partners, contributing imaging and AI expertise to clinical trials and translational programs. Notably, the group is involved in imaging biomarker development and data integration efforts within industry-sponsored studies such as the TOPAZ trial CT-radiomics study.

Magnetic Resonance Imaging (MRI) foundational artificial intelligence for non-invasive, histologically-meaningful cancer characterisation (MRI-Found-Histo). Funded by: Agencia Estatal de Investigación – Ministerio de Ciencia e Innovación. Reference: PID2024-158670OA-I00. Execution period: 01/09/2025 – 31/08/2028. PI: Francesco Grussu

Supporting Health Data Access Bodies to establish AI pathways enabling Deployment of AI as medical device tolos – SHAIPED. Funded by the European Commission. Reference 101195135. Execution period: 03/01/2024-02/28/2027. PI: Raquel Pérez-López
IMPRINT: Imaging Markers for Personalized Response in ImmunoTherapy. Department of Research and Universities of Catalonia. 2024-2026.
TANGERINE: Artificial-intelligence-based end-to-end prediction of cancer immunotherapy response. TRANSCAN Program (Spanish Association against Cancer and Instituto de Salud Carlos III). 2023-2025.
ODELIA: An Open Consortium for Decentralized Medical Artificial Intelligence. H2020 Program (European Commission). 2023-2028.
MARION: Multimodal biomarkers for precise management of metastatic prostate cancer. Proyectos de colaboración público-privada (Industry Ministry Spanish Government). 2023-2025.
PRECISE: Deciphering colon cancer heterogeneity with machine learning and precision imaging. Instituto de Salud Carlos III. 2022-2025.
Tumoral senescence induced by anti-cancer therapies constitutes a novel prognostic biomarker and a therapeutic target. Fundación Científica Asociación Española Contra el Cáncer-Proyectos Coordinados. 2021-2026.
CCE-DART: Building Data Rich Clinical Horizon 2020 Program – European Commission. VHIO. 2021-2026.
Unraveling the tumor immunophenotype with deep-learning based FERO Foundation Research Fellowship.
PREdICT: Personalized REsponse Imaging biomarker for Cancer Therapy. CRIS Cancer Foundation Research Talent Program, AstraZeneca PoC Award
PrecIMet: precision imaging for bone metastases. Fundació La Marató.
Immune-Image: Specific Imaging of Immune Cell Dynamics Using Novel Tracer Horizon 2020-Innovative Medicine Initiatives (IMI2-Call4; 831514).
Validación clínica de la resonancia de cuerpo completo con difusión en pacientes con cáncer de próstata resistente a la castración y metástasis óseas. Instituto de Salud Carlos III-Investigación en Salud (PI18/01395).

Beques

Beca postdoctoral Beatriu de Pinós: “Advancing Magnetic Resonance Imaging against liver cancer”. Destinatari: Francesco Grussu. 2022-2024.
Fundació La Caixa, Beca INPhINIT Retaining. Doctorat en enginyeria biomèdica (Universitat Politècnica de Catalunya). Investigadora predoctoral: Olivia Prior. 2021-2024.
PERIS – Beca predoctoral. Doctorat en enginyeria biomèdica (Universitat de Barcelona) Investigadora predoctoral: Marta Ligero. 2021-2024.