Fat Loss in Patients with Metastatic Clear Cell Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors.
Ji Hyun LeeSoohyun HwangByulA JeeJae-Hun KimJihwan LeeJae Hoon ChungWan SongHyun Hwan SungHwang Gyun JeonByong Chang JeongSeong Il SeoSeong Soo JeonHyun Moo LeeSe Hoon ParkGhee Young KwonMinyong KangPublished in: International journal of molecular sciences (2023)
The purpose of this study was to determine the prognostic impact of fat loss after immune checkpoint inhibitor (ICI) treatment in patients with metastatic clear cell renal cell carcinoma (ccRCC). Data from 60 patients treated with ICI therapy for metastatic ccRCC were retrospectively analyzed. Changes in cross-sectional areas of subcutaneous fat (SF) between the pre-treatment and post-treatment abdominal computed tomography (CT) images were expressed as percentages and were divided by the interval between the CT scans to calculate ΔSF (%/month). SF loss was defined as ΔSF < -5%/month. Survival analyses for overall survival (OS) and progression-free survival (PFS) were performed. Patients with SF loss had shorter OS (median, 9.5 months vs. not reached; p < 0.001) and PFS (median, 2.6 months vs. 33.5 months; p < 0.001) than patients without SF loss. ΔSF was independently associated with OS (adjusted hazard ratio (HR), 1.49; 95% confidence interval (CI), 1.07-2.07; p = 0.020) and PFS (adjusted HR, 1.57; 95% CI, 1.17-2.12; p = 0.003), with a 5%/month decrease in SF increasing the risk of death and progression by 49% and 57%, respectively. In conclusion, Loss of SF after treatment initiation is a significant and independent poor prognostic factor for OS and PFS in patients with metastatic ccRCC who receive ICI therapy.
Keyphrases
- computed tomography
- free survival
- prognostic factors
- cross sectional
- adipose tissue
- magnetic resonance imaging
- squamous cell carcinoma
- dual energy
- small cell lung cancer
- newly diagnosed
- contrast enhanced
- positron emission tomography
- end stage renal disease
- stem cells
- deep learning
- mesenchymal stem cells
- fatty acid
- chronic kidney disease
- magnetic resonance
- artificial intelligence
- bone marrow