Analysis of biopsy outcome after three-dimensional conformal radiation therapy of prostate cancer using dose-distribution variables and tumor control probability models.

Overview

abstract

PURPOSE: To investigate tumor control following three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer and to identify dose-distribution variables that correlate with local control assessed through posttreatment prostate biopsies. METHODS AND MATERIAL: Data from 132 patients, treated at Memorial Sloan-Kettering Cancer Center (MSKCC), who had a prostate biopsy 2.5 years or more after 3D-CRT for T1c-T3 prostate cancer with prescription doses of 64.8-81 Gy were analyzed. Variables derived from the dose distribution in the PTV included: minimum dose (Dmin), maximum dose (Dmax), mean dose (Dmean), dose to n% of the PTV (Dn), where n = 1%,...,99%. The concept of the equivalent uniform dose (EUD) was evaluated for different values of the surviving fraction at 2 Gy (SF(2)). Four tumor control probability (TCP) models (one phenomenologic model using a logistic function and three Poisson cell kill models) were investigated using two sets of input parameters, one for low and one for high T-stage tumors. Application of both sets to all patients was also investigated. In addition, several tumor-related prognostic variables were examined (including T-stage, Gleason score). Univariate and multivariate logistic regression analyses were performed. The ability of the logistic regression models (univariate and multivariate) to predict the biopsy result correctly was tested by performing cross-validation analyses and evaluating the results in terms of receiver operating characteristic (ROC) curves. RESULTS: In univariate analysis, prescription dose (Dprescr), Dmax, Dmean, dose to n% of the PTV with n of 70% or less correlate with outcome (p < 0.01). The area under the ROC curve for Dmean is 0.64. In contrast, Dmin (p = 0.6), D98 (p = 0.2) or D95 (p = 0.1) are not significantly correlated with outcome. The results for EUD depend on the input parameter SF(2): EUD correlates significantly with outcome for SF(2) of 0.4 or more, but not for lower SF(2) values. Using either of the two input parameters sets, all TCP models correlate with outcome (p < 0.05; ROC areas 0.60-0.62). Using T-stage dependent input parameters, the correlation is improved (logistic function: p < 0.01, ROC area 0.67, Poisson models: p < 0.01, ROC areas 0.64-0.66). In comparison, the ROC area is 0.68 for the combination of Dmean and T-stage. After multivariate analysis, a model based on TCP, D20 and Gleason score is the best overall model (ROC area 0.73). However, an alternative model based on Dmean, Gleason score, and T-stage is competitive (ROC area 0.70). CONCLUSION: Biopsy outcome after 3D-CRT of prostate cancer at MSKCC is not correlated with Dmin in the PTV and appears to be insensitive to cold spots in the dose distribution. This observation likely reflects the fact that much of the PTV, especially at the periphery, may not contain viable tumor cells and that the treatment margins were sufficiently large. Therefore, the predictive power of all variables which are sensitive to cold spots, like TCPs with Poisson models and EUD for low SF(2), is limited because the low dose region may not coincide with the tumor location. Instead, for MSKCC prostate cancer patients with their standardized CTV definition, substantial target motion and small dose inhomogeneities, Dmean (or any variable that downplays the effect of cold spots) is a very good predictor of biopsy outcome. While our findings may indicate a general problem in the application of current TCP models to clinical data, these conclusions should not be extrapolated to other disease sites without careful analysis.