University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Int J Radiat Oncol Biol Phys. 2011 Nov 1;81(3):623-30. doi: 10.1016/j.ijrobp.2010.06.012. Epub 2010 Oct 1.
Previous recursive partitioning analysis (RPA) of patients with malignant glioma (glioblastoma multiforme [GBM] and anaplastic astrocytoma [AA]) produced six prognostic groups (I-VI) classified by six factors. We sought here to determine whether the classification for GBM could be improved by using an updated Radiation Therapy Oncology Group (RTOG) GBM database excluding AA and by considering additional baseline variables.
The new analysis considered 42 baseline variables and 1,672 GBM patients from the expanded RTOG glioma database. Patients receiving radiation only were excluded such that all patients received radiation+carmustine. "Radiation dose received" was replaced with "radiation dose assigned." The new RPA models were compared with the original model by applying them to a test dataset comprising 488 patients from six other RTOG trials. Fitness of the original and new models was evaluated using explained variation.
The original RPA model explained more variations in survival in the test dataset than did the new models (20% vs. 15%) and was therefore chosen for further analysis. It was reduced by combining Classes V and VI to produce three prognostic classes (Classes III, IV, and V+VI), as Classes V and VI had indistinguishable survival in the test dataset. The simplified model did not further improve performance (explained variation 18% vs. 20%) but is easier to apply because it involves only four variables: age, performance status, extent of resection, and neurologic function. Applying this simplified model to the updated GBM database resulted in three distinct classes with median survival times of 17.1, 11.2, and 7.5 months for Classes III, IV, and V+VI, respectively.
The final model, the simplified original RPA model combining Classes V and VI, resulted in three distinct prognostic groups defined by age, performance status, extent of resection, and neurologic function. This classification will be used in future RTOG GBM trials.
先前对恶性胶质瘤（多形性胶质母细胞瘤[GBM]和间变性星形细胞瘤[AA]）患者进行的递归分区分析（RPA）产生了六个预后组（I-VI），根据六个因素进行分类。我们在这里试图确定通过使用排除 AA 的更新的放射治疗肿瘤学组（RTOG）GBM 数据库并考虑其他基线变量，是否可以改善 GBM 的分类。
新分析考虑了来自扩展的 RTOG 神经胶质瘤数据库的 42 个基线变量和 1672 名 GBM 患者。排除仅接受放疗的患者，以便所有患者均接受放疗+卡莫司汀。“接受的放疗剂量”被替换为“分配的放疗剂量”。将新的 RPA 模型应用于来自六个其他 RTOG 试验的 488 名患者的测试数据集，以比较原始模型。使用解释的变化来评估原始和新模型的拟合度。
原始 RPA 模型在测试数据集中比新模型（20%对 15%）解释了更多的生存变化，因此选择用于进一步分析。通过将第五类和第六类合并为三类预后类别（第三类、第四类和第五类+第六类）来减少模型，因为在测试数据集中第五类和第六类的生存无明显差异。简化模型并没有进一步提高性能（解释变化为 18%对 20%），但更容易应用，因为它只涉及四个变量：年龄、表现状态、切除范围和神经功能。将简化模型应用于更新的 GBM 数据库，结果产生了三个不同的类别，其中位生存时间分别为第三类、第四类和第五类+第六类的 17.1、11.2 和 7.5 个月。
最终模型，即简化的原始 RPA 模型，将第五类和第六类合并为一个，产生了三个由年龄、表现状态、切除范围和神经功能定义的不同预后组。该分类将用于未来的 RTOG GBM 试验。
