Prognostic Value of 18F-FDG PET/CT-Derived Secondary Lymphoid Organ Ratios and Hematologic Inflammation Markers in Advanced Non-Small Cell Lung Cancer Treated with Nivolumab
Erkam Kocaaslan, Ali Kaan Güren, Fırat Akagündüz, Ahmet Demirel, Mustafa Alperen Tunç, Burak Paçacı, Yeşim Ağyol, Pınar Erel, Abdüssamed Çelebi, Selver Işık, Ezgi Çoban, Nazım Can Demircan, Salih Özgüven, Zeynep Ceren Balaban Genç, Nargiz Majidova, Nadiye Sever, Murat Sarı

TL;DR
This study found that blood-based inflammation markers, but not PET/CT lymphoid ratios, predict survival in lung cancer patients treated with nivolumab.
Contribution
Shows that hematological markers like NLR are better prognostic indicators than PET/CT ratios in nivolumab-treated NSCLC patients.
Findings
High NLR and SII levels were significantly linked to shorter survival in patients.
PET/CT-derived SLR, BLR, and ILR ratios were not significantly associated with survival outcomes.
Liver metastases and high NLR were identified as independent adverse prognostic factors.
Abstract
Background/Objectives: This study aimed to evaluate the prognostic value of 18F-FDG PET/CT-based secondary lymphoid organ metabolic ratios—spleen/liver (SLR), bone marrow/liver (BLR), and ileocecal region/liver (ILR)—and hematological inflammation markers (neutrophil/lymphocyte ratio [NLR] and systemic immune-inflammation index [SII]) obtained before nivolumab treatment in relation to survival in patients with advanced non-small cell lung cancer (NSCLC). Methods: This retrospective single-center study included 79 advanced NSCLC patients who were treated with nivolumab monotherapy at Marmara University Faculty of Medicine Hospital between 2022 and 2024. Pretreatment SLR, BLR, and ILR ratios were calculated from 18F-FDG PET/CT examinations; NLR and SII values were obtained from hematological data. Survival outcomes were analyzed using the Kaplan–Meier method, and prognostic factors were…
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Taxonomy
TopicsInflammatory Biomarkers in Disease Prognosis · Cancer Immunotherapy and Biomarkers · Radiomics and Machine Learning in Medical Imaging
