Tumor Characterization Using [18F]FDG PET Radiomics in a PD-L1-Positive NSCLC Cohort

<b>Background</b>: Durvalumab consolidation following radiochemotherapy is now the standard treatment for unresectable stage III non-small cell lung cancer (NSCLC). [<sup>18</sup>F]FDG PET/CT offers valuable insights not just for staging but also for tumor characterization via radiomics, which can potentially predict histology, immunophenotype, and prognosis. <b>Methods</b>: We conducted a retrospective analysis of [<sup>18</sup>F]FDG PET/CT scans from stage IIIA-IIIB NSCLC patients treated at the Clinical Centre, University of Pécs. All biopsy samples were classified histologically (squamous vs. adenocarcinoma) and tested for PD-L1. Lung tumors were segmented using MEDISO InterViewTM FUSION software (version 3.12.002.0000). with an SUVmax threshold of four. Imaging features were extracted and compared based on histology, PD-L1 status, and neutrophil-to-lymphocyte ratio (NLR)-based prognosis groups. Statistical analyses were performed with Jamovi (v2.6.44), using Shapiro-Wilk, <i>t</i>-test/ANOVA, Mann-Whitney/Kruskal-Wallis, or Chi-square tests as appropriate. <b>Results</b>: Fifty-six patients were included (38 PD-L1-positive, 18 -negative). Among PD-L1-positive cases, poor versus good NLR prognosis groups differed in maximum diameter (<i>p</i> = 0.046), short-zone emphasis (<i>p</i> = 0.026), and zone-length non-uniformity (<i>p</i> = 0.027). Focusing on PD-L1-positive squamous carcinoma, maximum diameter, metabolic tumor volume, busyness, and coarseness showed significant differences (all <i>p</i> < 0.05). SUVmax, mean SUV, SUVpeak, and complexity were higher in squamous than in adenocarcinoma subtypes. PD-L1-positive and -negative squamous tumors differed in zone percentage (<i>p</i> = 0.039) and long-zone high gray-level emphasis (<i>p</i> = 0.024), while no significant differences were observed among adenocarcinomas. <b>Conclusions</b>: [<sup>18</sup>F]FDG PET/CT radiomics showed potential for differentiating NSCLC histological subtypes and for identifying PD-L1-associated imaging patterns in squamous cell carcinoma. In addition, certain metabolic features were associated with NLR-based prognostic groups in PD-L1-positive patients.

Authors

Institutions

Publication Details

Journal
Pharmaceuticals
Published
2026-01-07
DOI
https://doi.org/10.3390/ph19010103
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tumor Characterization Using [18F]FDG PET Radiomics in a PD-L1-Positive NSCLC Cohort

Zsombor Ritter, Bernadett Kálmán, Dániel Dezső, Éva Pusztai et al.
Pharmaceuticals
Radiomics and Machine Learning in Medical Imaging
article

Tumor Characterization Using [18F]FDG PET Radiomics in a PD-L1-Positive NSCLC Cohort

Zsombor Ritter, Bernadett Kálmán, Dániel Dezső, Éva Pusztai, János Girán, Árpád Boronkai, Dávid Sipos, Ferenc Budán, Domokos Máthé, Agnieszka Bos-Liedke, Ewelina Kaminska, Mateusz Matusewicz
article en

Abstract

<b>Background</b>: Durvalumab consolidation following radiochemotherapy is now the standard treatment for unresectable stage III non-small cell lung cancer (NSCLC). [<sup>18</sup>F]FDG PET/CT offers valuable insights not just for staging but also for tumor characterization via radiomics, which can potentially predict histology, immunophenotype, and prognosis. <b>Methods</b>: We conducted a retrospective analysis of [<sup>18</sup>F]FDG PET/CT scans from stage IIIA-IIIB NSCLC patients treated at the Clinical Centre, University of Pécs. All biopsy samples were classified histologically (squamous vs. adenocarcinoma) and tested for PD-L1. Lung tumors were segmented using MEDISO InterViewTM FUSION software (version 3.12.002.0000). with an SUVmax threshold of four. Imaging features were extracted and compared based on histology, PD-L1 status, and neutrophil-to-lymphocyte ratio (NLR)-based prognosis groups. Statistical analyses were performed with Jamovi (v2.6.44), using Shapiro-Wilk, <i>t</i>-test/ANOVA, Mann-Whitney/Kruskal-Wallis, or Chi-square tests as appropriate. <b>Results</b>: Fifty-six patients were included (38 PD-L1-positive, 18 -negative). Among PD-L1-positive cases, poor versus good NLR prognosis groups differed in maximum diameter (<i>p</i> = 0.046), short-zone emphasis (<i>p</i> = 0.026), and zone-length non-uniformity (<i>p</i> = 0.027). Focusing on PD-L1-positive squamous carcinoma, maximum diameter, metabolic tumor volume, busyness, and coarseness showed significant differences (all <i>p</i> < 0.05). SUVmax, mean SUV, SUVpeak, and complexity were higher in squamous than in adenocarcinoma subtypes. PD-L1-positive and -negative squamous tumors differed in zone percentage (<i>p</i> = 0.039) and long-zone high gray-level emphasis (<i>p</i> = 0.024), while no significant differences were observed among adenocarcinomas. <b>Conclusions</b>: [<sup>18</sup>F]FDG PET/CT radiomics showed potential for differentiating NSCLC histological subtypes and for identifying PD-L1-associated imaging patterns in squamous cell carcinoma. In addition, certain metabolic features were associated with NLR-based prognostic groups in PD-L1-positive patients.

PharmaceuticalsVol. 19(1)
Semmelweis University (HU), Poznań University of Economics and Business (PL), University of Life Sciences in Poznań (PL), Adam Mickiewicz University in Poznań (PL), University of Pecs (HU)
Semmelweis Egyetem, Pécsi Tudományegyetem, Medical Research Council, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NIH Clinical Center
No poverty
Openalex Percentile: Top 96%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.