Marta Ligero

801 total citations · 1 hit paper
20 papers, 350 citations indexed

About

Marta Ligero is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Artificial Intelligence. According to data from OpenAlex, Marta Ligero has authored 20 papers receiving a total of 350 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Oncology and 5 papers in Artificial Intelligence. Recurrent topics in Marta Ligero's work include Radiomics and Machine Learning in Medical Imaging (17 papers), AI in cancer detection (5 papers) and Advanced X-ray and CT Imaging (5 papers). Marta Ligero is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (17 papers), AI in cancer detection (5 papers) and Advanced X-ray and CT Imaging (5 papers). Marta Ligero collaborates with scholars based in Spain, Germany and United Kingdom. Marta Ligero's co-authors include Raquel Pérez-López, Jakob Nikolas Kather, Alonso García-Ruiz, Narmin Ghaffari Laleh, Kinga Bernatowicz, Omar S. M. El Nahhas, Rodrigo Dienstmann, Nahum Calvo, Manuel Escobar and Arturo Navarro-Martín and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Marta Ligero

19 papers receiving 348 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

In-context learning enables multimodal large language models to classify cancer pathology images

2024 47 citations Dyke Ferber, Georg Wölflein et al. Nature Communications profile →

Peers

Marta Ligero

RNMI

PRM

ONCOL

Mireia Crispin‐Ortuzar United Kingdom

Camilla Scapicchio Italy

Martijn P. A. Starmans Netherlands

Gaia Ninatti Italy

Diem Vuong Switzerland

Patrick Leo United States

Vidya Sankar Viswanathan United States

H. Geng United States

Yunfeng Cui United States

Mireia Crispin‐Ortuzar United Kingdom

Marta Ligero

337 ×1.4

RNMI

109 ×1.2

PRM

121 ×1.3

67 ×0.8

ONCOL

92 ×1.5

Citations per year, relative to Marta Ligero Marta Ligero (= 1×) peers Mireia Crispin‐Ortuzar

Countries citing papers authored by Marta Ligero

Since Specialization

Citations

This map shows the geographic impact of Marta Ligero's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marta Ligero with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marta Ligero more than expected).

Fields of papers citing papers by Marta Ligero

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marta Ligero. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marta Ligero. The network helps show where Marta Ligero may publish in the future.

Co-authorship network of co-authors of Marta Ligero

This figure shows the co-authorship network connecting the top 25 collaborators of Marta Ligero. A scholar is included among the top collaborators of Marta Ligero based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marta Ligero. Marta Ligero is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Bernatowicz, Kinga, Ramon Amat, Joan Frigola, et al.. (2025). Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction. Journal for ImmunoTherapy of Cancer. 13(1). e009140–e009140. 9 indexed citations

Ligero, Marta, et al.. (2025). Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning. 30807–30817.

Ligero, Marta, Omar S. M. El Nahhas, Mihaela Aldea, & Jakob Nikolas Kather. (2025). Artificial intelligence-based biomarkers for treatment decisions in oncology. Trends in cancer. 11(3). 232–244. 14 indexed citations

Ligero, Marta, Jens‐Peter Kühn, Steffen Löck, et al.. (2025). End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning. PubMed. 1(1). 3–3. 2 indexed citations

Ligero, Marta, et al.. (2025). Abstract 5008: A multi-dimensional vision-language deep learning model for radiology and pathology imaging. Cancer Research. 85(8_Supplement_1). 5008–5008. 1 indexed citations

Navarro, Vı́ctor, Marta Ligero, Alonso García-Ruiz, et al.. (2024). Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer. Radiology Artificial Intelligence. 6(2). e230118–e230118. 15 indexed citations

Ferber, Dyke, Georg Wölflein, Isabella C. Wiest, et al.. (2024). In-context learning enables multimodal large language models to classify cancer pathology images. Nature Communications. 15(1). 10104–10104. 47 indexed citations breakdown →

Ligero, Marta, Vı́ctor Navarro, Rodrigo Dienstmann, et al.. (2024). A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?. npj Precision Oncology. 8(1). 42–42. 8 indexed citations

Nahhas, Omar S. M. El, Marko van Treeck, Georg Wölflein, et al.. (2024). From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nature Protocols. 20(1). 293–316. 27 indexed citations

10.

Valera, M., et al.. (2024). Characterisation of white facial markings in Pura Raza Española horses (a worldwide population genetic study). Italian Journal of Animal Science. 23(1). 929–937. 1 indexed citations

11.

Ligero, Marta, Garazi Serna, Omar S. M. El Nahhas, et al.. (2023). Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression. Cancer Research Communications. 4(1). 92–102. 13 indexed citations

12.

Ligero, Marta, Omar S. M. El Nahhas, Óscar Palomares, et al.. (2023). 176P Enhancing immunotherapy response prediction via multimodal integration of radiology and pathology deep learning models. Annals of Oncology. 34. S251–S252. 1 indexed citations

13.

Ligero, Marta, Marc Simó, Cecilia Carpio, et al.. (2023). PET‐based radiomics signature can predict durable responses to CAR T‐cell therapy in patients with large B‐cell lymphoma. SHILAP Revista de lepidopterología. 4(4). 1081–1088. 7 indexed citations

14.

Laleh, Narmin Ghaffari, Marta Ligero, Raquel Pérez-López, & Jakob Nikolas Kather. (2022). Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer. Clinical Cancer Research. 29(2). 316–323. 35 indexed citations

15.

García-Ruiz, Alonso, Pablo Naval‐Baudin, Marta Ligero, et al.. (2021). Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma. Scientific Reports. 11(1). 695–695. 23 indexed citations

16.

Bernatowicz, Kinga, et al.. (2021). Robust imaging habitat computation using voxel-wise radiomics features. Scientific Reports. 11(1). 20133–20133. 17 indexed citations

17.

Ligero, Marta, Kinga Bernatowicz, Alonso García-Ruiz, et al.. (2020). Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. European Radiology. 31(3). 1460–1470. 113 indexed citations

18.

Suárez, Cristina, Rafael Morales‐Barrera, Alonso García-Ruiz, et al.. (2019). Hyperprogressive disease in patients with metastatic genitourinary tumors treated with immune checkpoint inhibitors.. Journal of Clinical Oncology. 37(7_suppl). 448–448. 1 indexed citations

19.

Ligero, Marta, Alonso García-Ruiz, Cristina Viaplana, et al.. (2019). Artificial intelligence combining radiomics and clinical data for predicting response to immunotherapy. Annals of Oncology. 30. v476–v476. 4 indexed citations

20.

Ligero, Marta, et al.. (2019). Selection of Radiomics Features based on their Reproducibility. PubMed. 2019. 403–408. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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