Predicting PD-L1 expression status in NSCLC using radiomic analysis of 18 F-FDG-PET/CT images
Radiomic features derived from 18 F-FDG-PET/CT images can potentially differentiate between PD-L1 positive and negative NSCLC. Consequently, radiomics with multimodal imaging presents a promising non-invasive approach for selecting patients who may benefit from targeted immunotherapy.
Authors
- Krisztián Szigeti (ORCID: https://orcid.org/0000-0002-0828-9828)
- Ramasamy Paulmurugan (ORCID: https://orcid.org/0000-0001-7155-4738)
- Domokos Máthé (ORCID: https://orcid.org/0000-0001-7343-0413)
- Eszter Regős (ORCID: https://orcid.org/0000-0002-1707-1943)
- Csaba Benedek (ORCID: https://orcid.org/0000-0003-3203-0741)
- Agnes I. Bartha
- Andrea Manno-Kovács (ORCID: https://orcid.org/0000-0002-9392-379X)
- Parasuraman Padmanabhan (ORCID: https://orcid.org/0000-0003-4112-4600)
- Fanni Júlia Kiss
- Tamás Györke
- Anna Izabell Járó
- Vilmos Madaras
Institutions
- Semmelweis University (HU)
- Nanyang Technological University (SG)
- Palo Alto University (US)
- Budapest University of Technology and Economics (HU)
- Pázmány Péter Catholic University (HU)
- Hungarian National Blood Transfusion Service (HU)
- HUN-REN Institute for Computer Science and Control (HU)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1007/s00259-025-07453-2
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- European Commission
- Semmelweis Egyetem
- Hungarian Scientific Research Fund
- Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
- Horizon 2020 Framework Programme
- National Research, Development and Innovation Office