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Σάββατο 15 Σεπτεμβρίου 2018

The significant value of predicting prognosis in patients with colorectal cancer using 18 F-FDG PET metabolic parameters of primary tumors and hematological parameters

Abstract

Objects

The purpose was to evaluate the correlation of the pre-treatment hematological parameters with metabolic parameters of primary tumor in baseline 18F-FDG PET/CT in patients with colorectal cancer (CRC) and estimate the prognostic value of both.

Methods

We retrospectively investigated 231 patients with CRC who underwent baseline 18F-FDG PET/CT. Routine blood sampling was tested in the same term. PET parameters in term of hematological parameters and pathological characteristics of primary tumor were compared. Kaplan–Meier survival analysis was performed in the patients without distant metastasis. The differences of disease-free survival between groups were compared by log-rank tests.

Results

Neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) were significantly correlated with all the metabolic parameters including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG). The patients with NLR > 3 had higher MTV (24.82 ± 18.16 vs 19.06 ± 13.30, P = 0.039) and TLG (219.04 ± 186.94 vs 166.45 ± 146.39, P = 0.047) than those whose NLR ≤ 3. NLR in those patients with distant metastasis was significantly higher than those without distant metastasis (P = 0.018) while LMR in those patients with distant metastasis was significantly lower than those without distant metastasis (P = 0.032). Survival analysis showed that those patients with low MTV (P = 0.015), low NLR (P = 0.008) and high LMR (P = 0.027) revealed significant survival benefit.

Conclusions

There was a significant association between the pre-treatment hematological parameters and metabolic parameters of baseline 18F-FDG PET/CT in the patients with CRC. It might be helpful in those patients with high NLR and low LMR to undergo 18F-FDG PET/CT to detect distant metastasis and predict prognosis.



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