Marta is a biomedical engineer who joined the Radiomics group in 2018. She’s focused on the implementation of artificial intelligence for developing CT-based radiomics biomarkers of response to immunotherapy. She also has research interest in radiomics variability and biological translation.
Scientific trajectory
Marta is a PhD student with expertise in artificial intelligence algorithms for medical imaging applications, especially on CT-based radiomics for predicting response to treatment. Marta graduated in Biomedical Engineering (2017) from the University of Barcelona (UB) and received her M.Sc. in Biomedical engineering (2018) from the Polytechnic University of Barcelona (UPC). She specialized in medical image quantification during her final master thesis at Radiomics group from Vall d’Hebron Institute of Oncology (VHIO), developing Radiomics-based algorithms for response prediction in cancer patients.
Marta is currently working as a predoctoral researcher at Vall d’Hebron Institute of Oncology (VHIO) since 2018. During her time at VHIO, she has been working on developing machine learning algorithms using CT-based radiomics data for predicting response to immunotherapy. The results of these studies have been published (M. Ligero et. al, Radiology 2021) and presented in oral presentations at the European Congress of Radiology (M. Ligero, ECR 2020 Vienna, Austria) and ESMO Congress (M. Ligero, ESMO Congress 2019 Barcelona, Spain)
Moreover, she has conducted CT-based radiomics reproducibility studies (M. Ligero et. al, European Radiology 2020) aiming to correct radiomics variability due to image acquisition and reconstruction techniques from different manufacturers. Now, she is working on improving CT-based radiomics signatures by implementing deep learning architectures and addressing differential response. Furthermore, she has strong research interest on AI explainability and biological validation.
Links and contact
- LinkedIn
- Google Scholar
- ORCID
- Github
- Twitter
- Email
Open-source research software
All published radiomics data analysis codes can be found on GitHub, e.g.:
- Immune-CT-Radiomics for replicating the radiomics pipeline for predicting response to immunotherapy
Latest articles
K Bernatowicz, F Grussu, M Ligero, et al., R Perez-Lopez. Robust imaging habitat computation using voxel-wise radiomics features. Scientific Reports 11, 20133(2021); doi:10.1038/s41598-021-99701-2.
M Ligero🞱, K Bernatowicz🞱, R Perez-Lopez. The Role of CT-Based Radiomics in Precise Imaging of Renal Cancer. Journal of Nephrological Science 3(2), 1-7 (2021); url: https://www.nephrojournal.com. 🞱joint first authors.
M Ligero, A Garcia-Ruiz, C Viaplana, et al., R Perez-Lopez. A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors. Radiology. Vol 299, No 1 (2021); doi: 10.1148/radiol.2021200928
A Garcia-Ruiz, P Naval-Baudin, M Ligero, et al., R Perez-Lopez. Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma. Scientific Report. 11 - 695 (2021); doi: 10.1038/s41598-020-79829-3
M Ligero, O Jordi-Ollero, K Bernatowicz,et al., R Perez-Lopez. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. European Radiology.31, 1460–1470 (2020); doi: 10.1007/s00330-020-07174-0
Recent grants and awards
2020 PERIS-Predoctoral fellowship. “Precision Medicine and Artificial Intelligence for characterizing cancer response to immunotherapy” Role: Predoctoral Researcher. Duration: 01/2021-12/2024.
