Validation and simplification of the Radiation Therapy Oncology Group recursive partitioning analysis classification for glioblastoma
| Autor: | Minesh P. Mehta, Minhee Won, Meihua Wang, Walter J. Curran, Christopher Coughlin, Edward G. Shaw, Jing Li |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Oncology
Adult Cancer Research medicine.medical_specialty Multivariate analysis Databases Factual medicine.medical_treatment Recursive partitioning Article Young Adult Text mining Glioma Internal medicine medicine Humans Radiology Nuclear Medicine and imaging Karnofsky Performance Status Antineoplastic Agents Alkylating Aged Aged 80 and over Clinical Trials as Topic Radiation Models Statistical Performance status business.industry Brain Neoplasms Decision Trees Age Factors Radiotherapy Dosage Middle Aged medicine.disease Explained variation Prognosis Carmustine Radiation therapy Multivariate Analysis business Glioblastoma Anaplastic astrocytoma |
| Zdroj: | International journal of radiation oncology, biology, physics. 81(3) |
| ISSN: | 1879-355X |
| Popis: | Purpose 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. Methods and Materials 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. Results 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. Conclusions 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|