The VHIO Radiomics Group is dedicated to advancing precision oncology through the development and clinical translation of artificial intelligence (AI) and data integration tools, with a particular focus on medical imaging. Our vision is to harness the full potential of radiomics and computational modelling to transform cancer care, making diagnostics more accurate, treatments more personalized, and patient outcomes improved.
By combining expertise in engineering and machine learning, we work toward the discovery, validation, and clinical qualification of imaging biomarkers that inform and guide treatment decision-making. We are also deeply committed to supporting drug development by leveraging functional imaging techniques within clinical trials, enabling real-time insights into treatment response.
Through our interdisciplinary approach, strong collaborations, and commitment to innovation, the VHIO Radiomics Group aims to be at the forefront of computational oncology, shaping the future of cancer diagnostics and therapeutics.
In 2024, we were thrilled to welcome Marta Buetas as a new technician to our team. Marta has a solid background in biomedical engineering and valuable experience in medical image analysis, specifically in oncology. We are confident that her fresh perspective will enrich the group’s work, and we eagerly anticipate her future doctoral studies within our team. Her potential for innovative ideas, proactive approach, and leadership in project management are qualities we believe will significantly benefit our research activities.
We are proud to announce that Alonso Garcia successfully defended his doctoral thesis at the end of 2024, earning the distinguished grade of magna cum laude. His outstanding research over the past four years has yielded excellent results, with publications in prestigious journals such as European Radiology and Cell Reports Medicine. Additionally, Alonso has been involved in multiple collaborations with both internal groups at VHIO and various national and international partners, further amplifying the impact of his work.
We also had the pleasure of hosting Bernat Alberich, a master’s thesis student from the University of Girona. Bernat conducted exceptional research on applying deep learning models for the automatic detection and segmentation of bone metastases. His contributions showcased the promising potential of advanced computational techniques in oncological imaging.
This year, our team secured several important grants, both as Principal Investigators and collaborators. Among these, we successfully obtained funding from the Fondo de Investigaciones Sanitarias (FIS), enabling us to further advance our research initiatives.
Raquel Perez-Lopez received notable appointments this year, becoming the Head of the Cancer Core Europe (CCE) Imaging Task Force, member of the European Association for the Study of Liver disease (EASL) Artificial Intelligence Task Force and the Head of the Oncological Unit at Sociedad Española de Radiología Médica (SERAM). These prestigious roles underscore her leadership in the field of oncological imaging and her continued commitment to advancing the integration of artificial intelligence in cancer research.
- Develop and optimize pipelines for AI models of data integration with particular focus on medical imaging and the integration of explainable models.
- Provide expertise in engineering and bioinformatics for the development and clinical qualification of imaging biomarkers for precision oncology to improve outcomes for cancer patients.
- Use functional imaging to optimize drug development in clinical trials.
- Integrate radiomics with other omics in translational studies to achieve a deeper understanding of tumor evolution and mechanisms of resistance to anti-cancer therapies.
Figure: DW microstructural MRI metrics provide biological-specific metrics that offer sensitivity to tumorigenic processes in the liver such as metastases.
Group Leader
Raquel Perez-Lopez
Senior Researcher
Francesco Grussu
PhD Students
Alonso García
Athanasios Grigoriou
Olivia Prior
Anna Voronova
Daniel Navarro
Maria Balaguer
Carlos Macarro
Students
Bernat Argelich
Laboratory Technician
Marta Buetas
Computer Scientist
Adrià Marcos
Camilo Monreal
Research Fellow
Luz María Atlagich
Nikos Satikoglou
Data curator
Christina Zatse
Most relevant scientific publications
- 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.
Main R&D projects
- 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).
Personnel grants
- Beatriu de Pinós post-doctoral fellowship: “Advancing Magnetic Resonance Imaging against liver cancer”. Fellow: Francesco Grussu. 2022-2024.
- Fundación La Caixa, INPhINIT Retaining Fellowship. PhD in Biomedical Engineering (Universidad Politecnica de Cataluña). Predoctoral researcher: Olivia Prior.2021-2024.
- PERIS-Predoctoral fellowship. PhD in Biomedical Engineering (University of Barcelona). Predoctoral researcher: Marta Ligero. 2021-2024.
Related News
