Prognostic value of the pre-treatment SUVmax of 18F-FDG PET/CT combined with peripheral absolute lymphocyte in patients with newly diagnosed extranodal natural killer/T-cell lymphoma.

Abstract

Background: Accurate assessment and prediction of patient prognosis, early identification of high-risk patients, and improvement of clinical outcomes for individuals with extranodal natural killer/T-cell lymphoma (ENKTCL) are critical. This study evaluates the prognostic value of a novel model combining maximum standardized uptake value (SUVmax) and absolute lymphocyte count (ALC) in ENKTCL patients.

Methods: We conducted a retrospective analysis of clinical data from 57 patients diagnosed with primary ENKTCL. Optimal cut-off values for SUVmax and ALC were determined using receiver operating characteristic (ROC) curves. Clinical characteristics were analyzed by Chi-squared tests or Fisher's exact tests. Survival analysis was performed using the Kaplan-Meier method and log-rank test, while independent prognostic factors were identified through Cox regression analysis.

Results: The optimal cut-off values for SUVmax and ALC were established at 11.8 and 0.87 × 10⁹/L, respectively. Univariate and multivariate analyses confirmed that both SUVmax and ALC were independent predictors of prognosis in ENKTCL patients. According to the combined SUVmax-ALC model, the patients were stratified into low-risk, intermediate-risk and high-risk groups. Kaplan-Meier analysis revealed significant differences in overall survival (OS) and progression-free survival (PFS) among these groups (p < 0.001). ROC curve analysis showed that the area under the curve (AUC) for the SUVmax-ALC model was 0.714, superior to individual tests (SUVmax, AUC = 0.674; ALC, AUC = 0.589). In addition, the AUC of the SUVmax-ALC model was higher than the International Prognostic Index (IPI, AUC = 0.632), nomogram-revised risk index (NRI, AUC = 0.566), and prognostic index of natural killer T-cell lymphoma (PINK, AUC = 0.592). Furthermore, the SUVmax-ALC model more effectively identified high-risk patients within low-risk IPI, PINK, or NRI groups, providing additional prognostic information. These findings indicate that the combination of SUVmax and ALC offers enhanced predictive accuracy for ENKTCL prognosis.

Conclusion: Pre-treatment SUVmax and ALC can serve as valuable indicators for predicting the prognosis of ENKTCL patients. Compared to IPI, NRI, and PINK scores, the SUVmax-ALC model demonstrates superior performance in risk stratification, suggesting its potential as an effective personalized prognostic tool for ENKTCL patients.