Non-invasive PD-L1 stratification in non-small cell lung cancer using dynamic contrast-enhanced MRI
Question Could Dynamic Contrast-Enhanced (DCE) MRI offer a safe and non-invasive way to assess Programmed Death-Ligand 1 (PD-L1) expression? Findings Quantitative DCE-MRI parameters K<sup>trans</sup> (the volume transfer rate) and K<sub>ep</sub> (the efflux rate constant) show potential for distinguishing PD-L1 hyperexpression from hypoexpression. Clinical relevance Preliminary results suggest that DCE-MRI could be a safe method to stratify PD-L1 hypo/hyperexpression in non-small cell lung cancer, potentially optimizing treatment decisions, given the high cost of immunotherapy.
Authors
- Raffaella Fiamma Cabini (ORCID: https://orcid.org/0000-0003-2983-989X)
- Lorenzo Preda
- Francesco Agustoni (ORCID: https://orcid.org/0000-0002-4334-1230)
- Andrea Riccardo Filippi (ORCID: https://orcid.org/0000-0001-7159-7869)
- Sithin Thulasi Seetha (ORCID: https://orcid.org/0000-0002-9079-9949)
- Agnese Robustelli Test (ORCID: https://orcid.org/0009-0001-1865-0963)
- Jessica Saddi (ORCID: https://orcid.org/0009-0004-8176-0737)
- Giulia Maria Stella (ORCID: https://orcid.org/0000-0003-0929-4394)
- Francesco Sanvito (ORCID: https://orcid.org/0000-0003-3379-9958)
- Domenico Zacà (ORCID: https://orcid.org/0009-0001-6567-0684)
- Gaia Messana (ORCID: https://orcid.org/0000-0002-3212-5305)
- Francesca Brero (ORCID: https://orcid.org/0000-0003-1822-8217)
- Alessandro Lascialfari
- Chandra Bortolotto (ORCID: https://orcid.org/0000-0002-9193-9309)
- Alessandra Marrocco
- Carlotta Pairazzi
Institutions
- University of California, Los Angeles (US)
- University of Milan (IT)
- University of Pavia (IT)
- National Center for Oncological Hadrontherapy (IT)
- Istituto Nazionale di Fisica Nucleare, Sezione di Pavia (IT)
- Siemens (Italy) (IT)
- Policlinico San Matteo Fondazione (IT)
- Siemens Healthcare (United States) (US)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- Siemens Healthineers (Germany) (DE)
- Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
- Università della Svizzera italiana (CH)
Publication Details
- Journal
- European Radiology
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1007/s00330-025-11524-1
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Fondazione IRCCS Policlinico San Matteo
- Università degli Studi di Pavia