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ASSOCIATION OF BODY COMPOSITION AND SYSTEMIC INFLAMMATION FOR PATIENTS WITH LOCALLY ADVANCED CERVICAL CANCER FOLLOWING CONCURRENT CHEMORADIOTHERAPY
JUAN L CUL NU LNG ZHANG YANMN MU XUYN GU
Diagnostic and Interventional Radiology - 2024;30(5):279-290
Xingtai Third Hospital, Clinic of Gynecology, Xingtai, China

PURPOSE Systemic inflammation and body composition are associated with survival outcomes of cancer patients. This study aimed to examine the combined prognostic value of systemic inflammatory markers and body composition parameters in patients with locally advanced cervical cancer (LACC). METHODS Patients who underwent concurrent chemoradiotherapy (CCRT) for LACC at a tertiary referral teaching hospital between January 2010 and January 2018 were enrolled. A predictive model was established based on systemic immune-inflammation index (SII) and computer tomography-derived visceral fat-to-muscle ratio (vFMR). Overall survival (OS) and progression-free survival (PFS) were assessed using the KaplanMeier method and Cox regression models. The model performance was assessed using discrimination, calibration, and clinical usefulness. RESULTS In total, 212 patients were enrolled. The SII and vFMR were closely related, and both independently predicted survival (P < 0.05). A predictive model was established based on the above biomarkers and included three subgroups: high-risk [both high SII (>828) and high vFMR (>1.1)], middle-risk (either high SII or high vFMR), and low-risk (neither high SII nor high vFMR). The 3-year OS (PFS) rates for low-, middle-, and high-risk patients were 90.5% (86.0%), 73.9% (58.4%), and 46.8% (36.1%), respectively (P < 0.05). This model demonstrated satisfactory predictive accuracy (area under the curve values for predicting 3-year OS and PFS were 0.704 and 0.718, respectively), good fit (HosmerLemeshow tests: P > 0.05), and clinical usefulness. CONCLUSION Systemic inflammatory markers combined with body composition parameters could independently predict the prognosis of patients with LACC, highlighting the utilization of commonly collected indicators in decision-making processes.

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