Assessment of Peripheral Platelet to Lymphocyte Ratio and Prognostic Nutritional Index in the Efficacy and Prognosis of Radiotherapy for Cervical Cancer

**Background:** Cervical cancer is the second most common malignancy among women worldwide, with high incidence and mortality in developing countries. Despite advances in surgery and radiotherapy, approximately one-third of patients die from disease recurrence or progression each year. There is growing evidence that systemic inflammatory response and nutritional status influence cancer prognosis. The platelet-to-lymphocyte ratio (PLR) and prognostic nutritional index (PNI) are readily available blood-based biomarkers that reflect these states. While PLR and PNI have been studied in various malignancies, their role specifically in cervical cancer patients undergoing radiotherapy has been insufficiently explored.

**Methods:** This retrospective single-centre study included 110 women with histologically confirmed cervical cancer (FIGO stages I–IV) treated at Shanghai Sixth People's Hospital. Inclusion criteria required pathologically confirmed cervical cancer, normal liver and kidney function, no prior other tumours, and complete haematological data before radiotherapy. Exclusion criteria included secondary malignancies, acute/chronic inflammatory conditions, distant metastasis, blood-related diseases, incomplete follow-up, pregnancy, serious infections, and immune dysfunction. All patients received external pelvic radiotherapy (total dose 45.0–50.4 Gy, 1.8–2.0 Gy/fraction, 5 fractions/week), with some receiving additional brachytherapy. PLR was calculated as platelet count divided by lymphocyte count. PNI was calculated as 5 × lymphocyte count (×10⁹/L) + serum albumin (g/L). Optimal cut-off values were determined using receiver operating characteristic (ROC) curves: PLR >186.88 (high) vs ≤186.88 (low); PNI >47.35 (high) vs ≤47.35 (low). Treatment efficacy was assessed using WHO criteria (CR, PR, SD, PD). Survival analysis used Kaplan–Meier methods with log-rank tests, and Cox regression models identified independent prognostic factors. Median follow-up was 26 months.

**Key Results:** Among 110 patients, 73 (66.4%) achieved effective treatment response (CR+PR) and 37 (33.6%) did not (SD+PD). The overall 3-year OS was 90.91% and 3-year PFS was 65.50%. High PLR was significantly associated with non-SCC histological type (p=0.014), positive lymph node metastasis (p=0.039), and worse treatment efficacy (p=0.001). Low PNI was significantly associated with age ≥50 years (p=0.027) and worse treatment efficacy (p=0.013). The high PLR group had significantly lower 3-year OS (81.00% vs 97.10%, p=0.035) and 3-year PFS (59.50% vs 88.20%, p=0.029) compared to the low PLR group. The low PNI group had significantly lower 3-year OS (74.20% vs 97.50%, p=0.004) and 3-year PFS (51.60% vs 87.30%, p=0.018) compared to the high PNI group. On univariate Cox analysis, risk factors for OS included FIGO stage III+IV (HR 4.01, 95% CI 1.22–13.14, p=0.023), non-SCC histological type (HR 12.03, 95% CI 2.96–48.81, p=0.016), PLR >186.88 (HR 1.01, 95% CI 1.00–1.03, p=0.043), and PNI ≤47.35 (HR 0.89, 95% CI 0.79–0.99, p=0.032). Risk factors for PFS included age ≥50 years (HR 1.81, 95% CI 1.12–2.93, p=0.020), non-SCC histological type (HR 0.16, 95% CI 0.07–0.39, p=0.012), PLR >186.88 (HR 2.81, 95% CI 1.66–4.77, p=0.001), and PNI ≤47.35 (HR 0.32, 95% CI 0.17–0.58, p=0.001). On multivariate Cox analysis, independent poor prognostic factors for OS were FIGO stage III+IV (HR 2.61, 95% CI 1.12–5.92, p=0.021), non-SCC histological type (HR 10.53, 95% CI 3.12–35.56, p=0.040), PLR >186.88 (HR 1.99, 95% CI 1.41–2.83, p=0.026), and PNI ≤47.35 (HR 1.07, 95% CI 1.03–1.11, p=0.016). For PFS, independent risk factors were age ≥50 years (HR 1.77, 95% CI 1.03–3.04, p=0.042) and non-SCC histological type (HR 3.31, 95% CI 1.06–10.41, p=0.041); PLR and PNI were not independent predictors of PFS.

**Clinical Implications:** Pre-treatment PLR and PNI are independent predictors of overall survival in cervical cancer patients receiving radiotherapy. These simple, inexpensive, and non-invasive biomarkers can be assessed before treatment initiation, unlike many traditional prognostic factors that require post-treatment evaluation. Low PNI (indicating malnutrition and impaired immune function) and high PLR (indicating systemic inflammation) identify patients at higher risk of poor outcomes who may benefit from more intensive monitoring or adjunctive interventions such as nutritional support. However, the study has limitations including small sample size (n=110), retrospective single-centre design, lack of serial biomarker measurements during treatment, and absence of consensus on optimal cut-off values. Prospective multicentre trials with larger sample sizes are needed to validate these findings and establish standardized thresholds.