Kiss, Fanni Júlia and Járó, A I and Máthé, Domokos and Benedek, Csaba and Madaras, Vilmos and Manno-Kovács, Andrea and Bartha, Á L and Padmanabhan, P and Paulmurugan, R and Regős, Eszter and Györke, Tamás and Szigeti, Krisztián (2025) Predicting PD-L1 expression status in NSCLC using radiomic analysis of 18 F-FDG-PET/CT images. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, In pre. ISSN 1619-7070 10.1007/s00259-025-07453-2

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Abstract

PurposeLung cancer is the most prevalent malignancy globally, with prognosis and treatment largely influenced by histological and molecular analyses. Molecular features like PD-L1 positivity help identify patients suitable for immunotherapy. However, obtaining histological samples can be challenging and limited. Radiomic analysis of imaging data provides a non-invasive way to characterize tumor heterogeneity and its complex patterns, which may help predict PD-L1 expression. This study investigates the efficacy of radiomics in forecasting PD-L1 status in NSCLC patients using 18 F-FDG-PET/CT images.MethodsIn this retrospective study, the primary staging 18 F-FDG-PET/CT scans of 105 patients with NSCLC of different phenotypes (72 ACC, 33 SCC, 64 PD-L1 positive, 41 PD-L1 negative) were analysed. Various segmentation techniques were employed. Radiomic features were obtained from the original and transformed images using the PyRadiomics package. Records were split into training and test sets in the ratio of 7:3. Feature reduction involved the Mann-Whitney U test, LASSO regression, and Spearman correlation analysis. A logistic regression model was developed using the selected features, and performance was assessed with ROC curve, AUC score, and other metrics.ResultsThe optimal model achieved an AUC of 0.783 (95% CI: 0.625, 942), with high accuracy (81.25%), sensitivity (90.00%), PPV (81.81%), and NPV (80.00%).ConclusionRadiomic 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.

Item Type:	Article
Uncontrolled Keywords:	non-small cell lung cancer; Computed tomography (CT); PD-L1; radiomics; positron-emission tomography (PET);
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Machine Perception Research Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	09 Jan 2026 07:27
Last Modified:	09 Jan 2026 07:27
URI:	https://eprints.sztaki.hu/id/eprint/11019

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