Correlation of Hematological Parameters With Clinical Outcomes in Cervical Cancer Patients Treated With Radical Radio(chemo)therapy: A Retrospective Study

Abstract

Variations in the levels of systemic inflammatory biomarker levels have been linked with outcomes in various malignancies including cervical cancer. In this study, we investigated prognostic implications of pretreatment hematological factors/indices in locally advanced cervical cancers treated with radical radio(chemo)therapy. Electronic medical records of 1051 patients with cervical cancer of FIGO (International Federation of Gynecology and Obstetrics) stage IB2-IVA treated in various prospective trials at our institute between 2003 and 2017 were reviewed. All clinical parameters such as age (dichotomized at the median), stage (IB2-IIB vs III-IVA), histologic type (squamous vs others), and hematological parameters (hemoglobin, platelets, absolute neutrophil count, absolute lymphocyte count, absolute monocyte count) were recorded. Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and prognostic nutritional index (PNI; defined as 10×albumin concentration [g/dL]+0.005×total lymphocyte count [μL]) were calculated. Univariate and multivariate (Cox regression) analyses were performed to evaluate these factors with disease-free survival (DFS) and overall survival (OS). With a median follow-up of 69 months, the 5-year DFS and OS were 65% and 69%, respectively. On multivariate analysis, FIGO stage (hazard ratio [HR], 1.9; P=.000) and PLR (HR, 1.002; P=.008) significantly affected DFS while FIGO stage (HR, 1.804; P=.000), LMR (HR, 0.92; P=.018), PNI (HR, 0.96; P=.013), and PLR (HR, 1.002; P=.006) significantly affected OS. Apart from FIGO stage, PLR significantly affected both DFS and OS. This correlation of hematological parameters is stronger in stage IIIB cervical cancer. Hematological indices, including PNI, PLR, and LMR, can serve as reliable prognostic indicators for patients with cervical cancer. By incorporating these indices into routine assessment and monitoring, clinicians can better stratify patients, personalize treatment plans, and more accurately predict outcomes, ultimately improving patient care and management.