Can post neoadjuvant chemotherapy 18F-FDG PET/CT predict residual cancer burden in locally advanced breast cancer? La TEP/TDM au 18F-FDG postchimiothérapie néoadjuvante peut-elle prédire la charge résiduelle de cancer dans le cancer du sein localement avancé ?
Medecine Nucleaire, cilt.48, sa.3, ss.141-149, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 48 Sayı: 3
- Basım Tarihi: 2024
- Doi Numarası: 10.1016/j.mednuc.2024.03.001
- Dergi Adı: Medecine Nucleaire
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.141-149
- Anahtar Kelimeler: 18F FDG PET/CT, Locally advanced breast cancer, Neoadjuvant chemotherapy, Residual cancer burden
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Objective: To assess the utility of metabolic parameters obtained from baseline and post-neoadjuvant chemotherapy (NAC) 18F FDG PET/CT scans in predicting postoperative residual cancer burden (RCB) scores in locally-advanced breast cancer (LABC). Methods: In our retrospective study, we enrolled 58 LABC patients who underwent baseline and post-treatment 18F FDG PET/CT scans followed by surgery between June 2020 and February 2022. Patients were categorized by their molecular subtypes as Luminal groupe (Luminal A and Luminal B (HER 2 negative)), HER2 positive and triple-negative (TN). We recorded various metabolic parameters, including maximum standardized uptake value (SUVmax), mean SUV (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), maximum standardized uptake lean body mass (SULmax), and mean SUV lean body mass (SULmean) of the primary tumor (T). To minimize the effect of SUV changes between PET studies, SUV ratios of tumor and liver were recorded for each study as TLR1 and TLR2 respectively. We calculated the percent reduction in SUVmax (ΔSUVmax%) between 2 PET studies. Patients were categorized into two groups based on postoperative RCB scores: RCB0/I (pathological responders, pR) and RCB II/III (pathological non-responders, non-pR). Results: Twenty-six patients (44.8%) were pR and 32 (55.2%) were non-pR. Baseline metabolic parameters were similar in 2 groups. Post-treatment T SUVmax2, T SUVmean2, T SULmax2, T SULmean2, TLR SUV2, and TLR SUL2 values were significantly different between the pR and non-pR patients across all molecular subgroups. Also, pR patients exhibited a significantly higher mean ΔSUVmax compared to non-pR patients. In the Luminal and HER2 positive groups, T SUVmax2 and T SUVmean2 values successfully discriminated the pR and non-pR groups with high accuracy, achieving 100% sensitivity and 100% specificity in the luminal group. In the luminal group, a −75.4% cut-off value for ΔSUVmax predicted pR with 100% sensitivity. Conclusion: Our findings indicate that SUV parameters, normalized to lean body mass as recommended by PERCIST, can be valuable for the early non-invasive prediction of pR and non-pR patients using post-NAC 18F FDG PET/CT.