Department of Radiation Oncology, Korea Institute of Radiological and Medical Sciences, Seoul, Korea.
Int J Radiat Oncol Biol Phys. 2011 Mar 1;79(3):782-7. doi: 10.1016/j.ijrobp.2009.11.054. Epub 2010 May 14.
The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer.
We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling.
By multivariate regression analysis, the model showed that age, hemoglobin level before RT, Fédération Internationale de Gynécologie Obstétrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p=0.01).
The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.
本研究旨在开发一个列线图，用于预测接受单纯根治性放疗（RT）而未接受化疗的宫颈癌患者 5 年生存率。
我们回顾性分析了 1994 年 3 月至 2002 年 4 月期间在我院接受根治性 RT 的 549 例宫颈癌患者。采用 Cox 比例风险回归进行多变量分析，并将 Cox 模型作为设计列线图的基础。通过自举重采样对内部分辨度和校准进行验证。
通过多变量回归分析，模型显示年龄、RT 前血红蛋白水平、国际妇产科联合会（FIGO）分期、最大肿瘤直径、淋巴结状态和 A 点 RT 剂量显著预测总生存率。生存预测模型具有良好的校准和区分度。Bootstrap 校正的一致性指数为 0.67。列线图的预测能力优于 FIGO 分期（p=0.01）。
该列线图提供了比 FIGO 分期系统更高的区分度。特别是，它提高了生存概率的预测能力，可用于患者咨询、治疗方式和方案的选择以及临床试验的设计。然而，在该列线图用于临床之前，应该进行外部验证。
