PubMed · 9336132
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M V Graham. 1997-10-01. Predicting radiation response.. https://doi.org/10.1016/s0360-3016(97)00353-2
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PURPOSE: To modify existing linear-quadratic (LQ) equations in order to take account of relative biological effectiveness (RBE) using the concept of biologically effective dose (BED). METHODS AND MATERIALS: Clinically useful forms of the LQ model have been modified to incorporate RBE effects in such a way as to allow comparison between high- and low-LET (linear energy transfer) radiations in terms of similar biological dose units. The new parameter in the formulation is RBEM, the intrinsic (or maximum) RBE at zero dose. The principal assumption (following Kellerer and Rossi; ref. 1) is that high-LET radiation modifies the alpha-coefficient of damage while leaving the beta-coefficient unaltered. RESULTS: The equations allow a quantitative estimation of how the apparent RBE will change with changes in dose/fraction or dose-rate and of how the magnitude and rate of change is governed by the low-LET alpha/beta ratio of the irradiated tissue. The modifications are applicable to all types of radiotherapy (fractionated, continuous low dose-rate, therapy with decaying sources, etc.). In cases where the normal tissue RBEM is greater than that for the tumor, the revised formulation helps explain why there will be situations where therapeutic index will be adversely affected by use of high-LET radiation. Such clinical advantages as have been observed are more likely to result from favorable geometrical sparing of critical normal tissues and/or the fact that slowly growing tumors may have alpha/beta values more typical of late-responding normal tissues. CONCLUSIONS: The incorporation of RBE into existing LQ methodology allows quantitative assessment of clinical applications of high-LET radiations via an examination of the associated BEDs. On the basis of such assessments high-LET radiations are shown to confer few advantages.
PURPOSE: To quantify the dose-time fractionation factors in preoperative radiation therapy for microscopic pelvic deposits of rectal cancer. This provides a biologic basis for understanding and improving the results of adjuvant therapies for this disease. METHODS: The reduction in incidence of pelvic relapses as a function of radiation dose and overall treatment time was determined from the literature. The displacement of dose-response curves to higher doses reflects the growth during radiation treatment of subclinical pelvic deposits which are beyond the future surgical margins. RESULTS: Dose-response curves are steep if the effect of overall duration of radiation therapy is accounted for. The time-related displacement of these steep dose-response curves is consistent with a median doubling time for malignant clonogenic cells of about 4 or 5 days, much faster than the growth rate of the average primary tumor at diagnosis. This rapid growth is evident within the first few days of irradiation, implying that the natural growth rate of these microscopic deposits if fast, and/or that an acceleration of growth follows initiation of radiation injury with a very short lag time. CONCLUSION: Subclinical pelvic deposits of rectal cancer grow rapidly during preoperative radiation therapy with an adverse influence on the rate of pelvic tumor control from protracting the duration of adjuvant treatment. Low doses only offer clinically relevant reduction in risk of pelvic relapses if the overall radiation treatment time is short. For a given overall treatment duration there is a relatively steep dose-response curve, predicting that significant improvements in tumor control are possible.
PURPOSE: The recursive partitioning analysis (RPA) classes for malignant 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 classes 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 patients with significantly different survival outcome. RTOG 90-06 is a randomized Phase III study of 712 patients accrued from 1990 to 1994. The minimum potential follow-up is 18 months. The treatment arms were combined for the purpose of this analysis. There were 84, 13, 105, 240, 150, and 23 patients in the RPA Classes I-VI from RTOG 90-06, respectively. RESULTS: The median survival times (MST) and 2-year survival rates for the six RPA classes in RTOG 90-06 are compared to those previously published. The MST and 2-year survival rates for the RTOG RPA classes were within 95% confidence intervals of the 90-06 estimates for Classes 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 variation. The RPA classes within RTOG 90-06 are statistically distinct with all comparisons exceeding 0.0001, except those involving Class II. 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 RTOG database norms. CONCLUSION: The validity of the model is verified by the reliability of the RPA classes to define distinct groups with respect to survival. Further evidence is given by prediction of MST and 2-year survival for all classes except Class II. The RPA classes explained a good portion of the variation in survival outcome in the data. Lack of correlation in RPA Class II between datasets may be an artifact of the small sample size or an indication that this class is not distinct. The validation of the RPA classes attests to their usefulness as historical controls for the comparison of future Phase II results.