Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Cancer Med. 2024 Nov;13(22):e70394. doi: 10.1002/cam4.70394.
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.
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.
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).
We presented a validated novel clinicopathological risk stratification signature for robust prognostication of esCC, which may be used for streamlining treatment strategies.
早期宫颈癌（esCC）的预后存在高度异质性和不一致性。本研究旨在结合临床和病理变量，探索一种新的、直观的风险分层模型，以更好地进行预后判断和决策。
我们纳入了 2071 例接受根治性子宫切除术的术前活检证实和临床诊断为 FIGO 分期 IA-IIA 的 CC 患者，这些患者均来自于 2013 年至 2018 年。患者被随机分配到训练集（n=1450）和内部验证集（n=621），比例为 7:3。我们使用递归分区分析（RPA）来建立风险分层模型，并评估 RPA 衍生模型的区分度和校准能力。该模型的性能与传统的 FIGO 2018 分期和第 9 版 T 或 N 分期分类进行了比较。
RPA 将患者分为四个具有明显不同生存情况的风险组：在训练队列中，RPA I 至 IV 组的 5 年 OS 分别为 98%、95%、85.5%和 64.2%；在内部验证队列中，5 年 OS 分别为 99.5%、93.2%、85%和 68.3%（对数秩检验，p<0.001）。校准曲线证实，RPA 预测的生存率与实际生存率吻合良好。与现有的分期系统相比，RPA 模型具有更高的 OS 曲线下面积（训练集：0.778 比 0.6-0.717；内部验证集：0.772 比 0.595-0.704；均 p<0.05）和 OS 的 C 指数（训练集：0.768 比 0.598-0.707；内部验证集：0.741 比 0.583-0.676；均 p<0.05）。重要的是，RPA 组与治疗方案的疗效之间存在关联。在 RPA I 组中，不同治疗方式之间没有明显差异（p=0.922），而在 RPA II-IV 组中，接受辅助放化疗的患者生存获益明显（p 值分别为 0.028、0.036 和 0.024）。
本研究提出了一种经过验证的新的临床病理风险分层特征，可用于优化治疗策略。
