Importance of 18F-fluorodeoxyglucose positron emission tomography/computed tomography heterogeneity indices in non-small cell lung cancer


Ogul A., Birsenogul I., Kelle A. P., Solmaz A. A., Coskun Y., Yetisir A. E., ...Daha Fazla

Revista da Associacao Medica Brasileira, cilt.71, sa.5, 2025 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 71 Sayı: 5
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1590/1806-9282.20241408
  • Dergi Adı: Revista da Associacao Medica Brasileira
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE
  • Anahtar Kelimeler: cancer. Positron, emission, tomography, Biomarkers
  • Sağlık Bilimleri Üniversitesi Adresli: Evet

Özet

OBJECTIVE: The aim of this study was to assess the relationship between metabolic tumor heterogeneity indices in patients with driver mutation-positive non-small cell lung cancer, with a particular focus on the impact of heterogeneity indices on prognostic outcomes. METHODS: Patients diagnosed with metastatic small-cell lung cancer with driver mutations were included in this study. The relationship between the patients’ 18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters and heterogeneity indices and survival was examined. RESULTS: Heterogeneity index-1 values greater than 0.29 were associated with an increased likelihood of mortality (61.7% in the exitus group compared to 28% in the survivor group, p=0.006). Lower heterogeneity index-2 values were more prevalent among the exitus group, with a statistically significant difference observed between the groups (55.3% in the exitus group versus 24% in the survivor group, p=0.010). The mean overall survival time was longer in patients with low heterogeneity index-1 levels (p=0.006). The mean overall survival time was found to be similar between the heterogeneity index-2 level groups (p=0.672). CONCLUSION: 18F-fluorodeoxyglucose positron emission tomography/computed tomography metabolic tumor heterogeneity indices are an important tool for predicting the prognosis of non-small cell lung cancer patients with driver mutations.