Department of Clinical Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Department of Radiotherapy, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
Br J Radiol. 2020 Jul;93(1111):20190464. doi: 10.1259/bjr.20190464. Epub 2020 May 21.
To analyze survival outcomes in patients with oropharygeal cancer treated with primary intensity modulated radiotherapy (IMRT) using decision tree algorithms.
A total of 273 patients with newly diagnosed oropharyngeal cancer were identified between March 2010 and December 2016. The data set contained nine predictor variables and a dependent variable (overall survival (OS) status). The open-source R software was used. Survival outcomes were estimated by Kaplan-Meier method. Important explanatory variables were selected using the random forest approach. A classification tree that optimally partitioned patients with different OS rates was then built.
The 5 year OS for the entire population was 78.1%. The top three important variables identified were , and to treatment. Patients were partitioned in five groups on the basis of these explanatory variables.
The proposed classification tree could help to guide future research in oropharyngeal cancer field.
Decision tree method seems to be an appropriate tool to partition oropharyngeal cancer patients.
利用决策树算法分析接受原发调强放疗（IMRT）治疗的口咽癌患者的生存结果。
共纳入 2010 年 3 月至 2016 年 12 月期间诊断为新发口咽癌的 273 例患者。该数据集包含 9 个预测变量和一个因变量（总生存（OS）状态）。使用开源 R 软件。采用 Kaplan-Meier 法估计生存结果。采用随机森林方法选择重要的解释变量。然后构建一个最优划分具有不同 OS 率患者的分类树。
全人群 5 年 OS 为 78.1%。确定的前三个最重要的变量是 、 和 。根据这些解释变量，将患者分为五组。
提出的分类树有助于指导口咽癌领域的未来研究。
决策树方法似乎是一种合适的工具，可以对口咽癌患者进行划分。
