早期癌症宫颈癌一期根治术后的预后生存模型
Prognostic Survival Model Following Primary Radical Surgery for Early-Stage Cervical Cancer.
摘要(中文译文)
根治性子宫切除术联合盆腔淋巴结清扫术是早期宫颈癌的标准手术方式。然而，即使接受了最佳治疗，部分患者仍会出现疾病复发。本研究旨在开发并验证一种预测模型，根据复发风险对患者进行分类，从而更好地辅助临床医生制定个体化术后治疗方案并改善患者预后。研究收集并分析了接受根治性子宫切除术的早期宫颈癌女性患者数据。主要结局为无复发生存期（RFS）。采用向后消除法构建基于Cox比例风险回归的预测模型。通过自举法进行内部验证。模型的区分能力以一致性指数（C-index）进行评估。通过校准图检验模型的校准度。最终预后模型以列线图和网络计算器的形式呈现，并进一步用于将患者分为低、中、高风险组以供临床应用。在1309例患者中，115例（8.8%）出现复发。中位随访时间为72.2个月。3年和5年RFS率分别为93.0%（95% CI，91.5-94.6%）和90.7%（95% CI，88.9-92.5%）。肿瘤大小、组织学亚型、阳性淋巴结数目、淋巴血管间隙浸润和血小板与淋巴细胞比值与RFS显著相关。这些因素被用于构建预测模型。该模型整体拟合良好，过度拟合极小，校准度良好。以C-index衡量的模型区分能力为0.73。我们提出的生存模型为早期宫颈癌患者的治疗决策提供了一种潜在有价值的工具。该模型表现出稳健的区分能力和预测校准度。然而，仍需在多样化数据集中进行外部验证，以评估该预测模型在更广泛患者群体中的可重复性和适用性。
Abstract
: Radical hysterectomy with pelvic lymphadenectomy is a standard surgical procedure for early-stage cervical cancer. However, even with optimal treatment, some patients still experience disease recurrence. We aimed to develop and validate a prediction model to classify patients according to the risk of recurrence which can better assist clinicians to tailor the postoperative treatment and improve patient outcomes. : Data of women diagnosed with early-stage cervical cancer who underwent radical hysterectomy were collected and analyzed. The primary outcome was recurrence-free survival (RFS). A prediction model based on Cox proportional hazard regression was developed by using the backward elimination procedure. Internal validation was performed by bootstrapping. The model's discriminative ability was demonstrated by the concordance index (C-index). The model's calibration was examined through a calibration plot. The final prognostic model was presented as a nomogram and a web-based calculator, which were further used to categorize patients into low, moderate, and high-risk groups for clinical application. : Among the 1309 patients, 115 (8.8%) experienced a recurrence. The median follow-up was 72.2 months. The 3-year and 5-year RFS rates were 93.0% (95% CI, 91.5-94.6%) and 90.7% (95% CI, 88.9-92.5%), respectively. Tumor size, histological subtype, number of positive lymph nodes, lymphovascular space invasion, and platelet-to-lymphocyte ratio were significantly associated with RFS. These factors were employed to construct a prediction model. The model exhibited a good overall fit with minimal overfitting and good calibration. The model's discriminative performance, as measured by the C-index, was 0.73. : Our proposed survival model offers a potentially valuable tool for therapeutic decision-making in patients with early-stage cervical cancer. This model demonstrates robust discriminative performance and predictive calibration. Nevertheless, external validation across diverse datasets should be conducted to assess the reproducibility and applicability of this predictive model across a broader spectrum of patients.
如何引用
AMA
Rattiya Phianpiset, Chayanid Detwongya, Manatsawee Manopunya, Chalong Cheewakriangkrai, Kittipat Charoenkwan. Prognostic Survival Model Following Primary Radical Surgery for Early-Stage Cervical Cancer.. Cancers. 2026; doi:10.3390/cancers18071134.
APA
Rattiya Phianpiset, Chayanid Detwongya, Manatsawee Manopunya, Chalong Cheewakriangkrai, Kittipat Charoenkwan (2026). Prognostic Survival Model Following Primary Radical Surgery for Early-Stage Cervical Cancer.. Cancers. https://doi.org/10.3390/cancers18071134
GB/T 7714
Rattiya Phianpiset, Chayanid Detwongya, Manatsawee Manopunya, Chalong Cheewakriangkrai, Kittipat Charoenkwan. Prognostic Survival Model Following Primary Radical Surgery for Early-Stage Cervical Cancer.[J]. Cancers, 2026 doi:10.3390/cancers18071134.
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