Asan Medical Center, University of Ulsan, College of Medicine, Seoul, Republic of Korea.
Asan Medical Center, University of Ulsan, College of Medicine, Seoul, Republic of Korea.
Radiother Oncol. 2014 Jun;111(3):437-41. doi: 10.1016/j.radonc.2014.03.025. Epub 2014 Jun 5.
To develop a nomogram predicting the risks of distant metastasis following postoperative adjuvant radiation therapy for early stage cervical cancer.
We reviewed the medical records of 1069 patients from ten participating institutions. Patients were divided into two cohorts: a training set (n=748) and a validation set (n=321). The demographic, clinical, and pathological variables were included in the univariate Cox proportional hazards analysis. Clinically established and statistically significant prognostic variables were utilized to develop a nomogram.
The model was constructed using four variables: histologic type, pelvic lymph node involvement, depth of stromal invasion, and parametrial invasion. This model demonstrated good calibration and discrimination, with an internally validated concordance index of 0.71 and an externally validated c-index of 0.65. Compared to FIGO staging, which showed a broad range in terms of distant metastasis, the developed nomogram can accurately predict individualized risks based on individual risk factors.
The devised model offers a significantly accurate level of prediction and discrimination. In clinical practice it could be useful for counseling patients and selecting the patient group who could benefit from more intensive/further chemotherapy, once validated in a prospective patient cohort.
开发一种列线图，预测早期宫颈癌术后辅助放疗后远处转移的风险。
我们回顾了来自 10 家参与机构的 1069 名患者的病历。患者分为两组：训练集（n=748）和验证集（n=321）。单因素 Cox 比例风险分析纳入了人口统计学、临床和病理学变量。利用临床确定和统计学上有意义的预后变量开发了列线图。
该模型使用 4 个变量构建：组织学类型、盆腔淋巴结受累、间质浸润深度和宫旁浸润。该模型具有良好的校准和区分度，内部验证一致性指数为 0.71，外部验证 c 指数为 0.65。与 FIGO 分期相比，该模型在远处转移方面具有广泛的范围，能够根据个体危险因素准确预测个体化风险。
该模型提供了显著准确的预测和区分度。在临床实践中，它可以用于为患者提供咨询，并为可能从更强化/进一步化疗中获益的患者选择分组，一旦在前瞻性患者队列中得到验证。
