Foping Chen

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

ABSTRACTBackgroundHighly 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.MethodsWe 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.ResultsRPA 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).ConclusionWe presented a validated novel clinicopathological risk stratification signature for robust prognostication of esCC, which may be used for streamlining treatment strategies.

Construction of refined staging classification systems integrating FIGO/T‐categories and corpus uterine invasion for non‐metastatic cervical cancer

AbstractBackgroundTo investigate the prognostic value of corpus uterine invasion (CUI) in cervical cancer (CC), and determine the necessity to incorporate it for staging.MethodsA total of 809 cases of biopsy‐proven, non‐metastatic CC were identified from an academic cancer center. Recursive partitioning analysis (RPA) method was used to develop the refined staging systems with respect to overall survival (OS). Internal validation was performed by using calibration curve with 1000 bootstrap resampling. Performances of the RPA‐refined stages were compared against the conventional FIGO 2018 and 9th edition TNM‐stage classifications by the receiver operating characteristic curve (ROC) and decision curve analysis (DCA).ResultsWe identified that CUI was independently prognostic for death and relapse in our cohort. RPA modeling using a two‐tiered stratification by CUI (positive and negative) and FIGO/T‐categories divided CC into three risk groupings (FIGO I′‐III'/T1′‐3′), with 5‐year OS of 90.8%, 82.1%, and 68.5% for proposed FIGO stage I′–III', respectively (p ≤ 0.003 for all pairwise comparisons), and 89.7%, 78.8%, and 68.0% for proposed T1′‐3′, respectively (p < 0.001 for all pairwise comparisons). The RPA‐refined staging systems were well validated with RPA‐predicted OS rates showed optimal agreement with actual observed survivals. Additionally, the RPA‐refined stages outperformed the conventional FIGO/TNM‐stage with significantly higher accuracy of survival prediction (AUC: RPA‐FIGO vs. FIGO, 0.663 [95% CI 0.629–0.695] vs. 0.638 [0.604–0.671], p = 0.047; RPA‐T vs. T, 0.661 [0.627–0.694] vs. 0.627 [0.592–0.660], p = 0.036).ConclusionCUI affects the survival outcomes in patients with CC. Disease extended to corpus uterine should be classified as stage III/T3.