VALIDATION AND PREDICTIVE POWER OF RADIATION-THERAPY ONCOLOGY GROUP (RTOG) RECURSIVE PARTITIONING ANALYSIS CLASSES FOR MALIGNANT GLIOMA PATIENTS - A REPORT USING RTOG-90-06

Purpose: The recursive partitioning analysis (RPA) classes for maligna nt glioma patients were previously established using data on over 1500 patients entered on Radiation Therapy Oncology Group (RTOG) clinical trials, The purpose of the current analysis was to validate the RPA cl asses with a new dataset (RTOG 90-06), determine the predictive power of the RPA classes, and establish the usefulness of the database norms for the RPA classes, Patients and Methods: There are six RPA classes for malignant glioma patients that comprise distinct groups of patient s with significantly different survival outcome, RTOG 90-06 is a rando mized Phase III. study of 712 patients accrued from 1990 to 1994, The minimum potential follow-up is 18 months. The treatment arms were comb ined for the purpose of this analysis, There were 84, 13, 105, 240, 15 0, and 23 patients in the RPA Classes I-Tn from RTOG 90-06, respective ly, Results: The median survival times (MST) and 2-year survival rates for the six RPA classes in RTOG 90-06 are compared to those previousl y published. The MST and 2-year survival rates for the RTOG RPA classe s were within 95% confidence intervals of the 90-06 estimates for Clas ses I, III, IV, and V, The RPA classes explained 43% of the variation (squared error loss), By comparison, a Cox model explains 30% of the v ariation, The RPA classes within RTOG 90-06 are statistically distinct with all comparisons exceeding 0.0001, except those involving Class I I, A survival analysis from a prior RTOG study indicated that 72.0 Gy had superior outcome to literature controls; analysis of this data by RPA classes indicates the survival results were not superior to the RT OG database norms, Conclusion: The validity of the model is verified b y the reliability of the RPA classes to define distinct groups with re spect to survival, Further evidence is given by prediction of MST and 2-year survival for all classes except Class If, The RPA classes expla ined a good portion of the variation in survival outcome in the data, Lack of correlation in RPA Class II between datasets may be an artifac t of the small sample size or an indication that this class is not dis tinct, The validation of the RPA classes attests to their usefulness a s historical controls for the comparison of future Phase II results, ( C) 1998 Elsevier Science Inc.