Establishment of a Novel Risk Stratification System Integrating Clinical and Pathological Parameters for Prognostication and Clinical Decision-Making in Early-Stage Cervical Cancer

Background: Highly heterogeneity and inconsistency in terms of prognosis are widely identified for early-stage cervical cancer (esCC). Herein, we aim to investigate for an intuitional risk stratification model for better prognostication and decision-making in combination with clinical and pathological variables.

Methods: We enrolled 2071 CC patients with preoperative biopsy-confirmed and clinically diagnosed with FIGO stage IA-IIA who received radical hysterectomy from 2013 to 2018. Patients were randomly assigned to the training set (n = 1450) and internal validation set (n = 621), in a ratio of 7:3. We used recursive partitioning analysis (RPA) to develop a risk stratification model and assessed the ability of discrimination and calibration of the RPA-derived model. The performances of the model were compared with the conventional FIGO 2018 and 9th edition T or N stage classifications.

Results: RPA divided patients into four risk groups with distinct survival: 5-year OS for RPA I to IV were 98%, 95%, 85.5%, and 64.2%, respectively, in training cohort; and 99.5%, 93.2%, 85%, and 68.3% in internal validation cohort (log-rank p < 0.001). Calibration curves confirmed that the RPA-predicted survivals were in good agreement with the actual survivals. The RPA model outperformed the existing staging systems, with highest AUC for OS (training: 0.778 vs. 0.6-0.717; internal validation: 0.772 vs. 0.595-0.704; all p < 0.05), and C-index for OS (training: 0.768 vs. 0.598-0.707; internal validation: 0.741 vs. 0.583-0.676; all p < 0.05). Importantly, there were associations between RPA groups and the efficacy of treatment regimens. No obvious discrepancy was observed among different treatment modalities in RPA I (p = 0.922), whereas significant survival improvements were identified in patients who received adjuvant chemoradiotherapy in RPA II-IV (p value were 0.028, 0.036, and 0.024, respectively).

Conclusion: We presented a validated novel clinicopathological risk stratification signature for robust prognostication of esCC, which may be used for streamlining treatment strategies.