We fit phenomenological tumor control probability (TCP) models to biopsy outcome after three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer patients to quantify the local dose-response of prostate cancer. ⋯ Dose-response of prostate cancer, quantified by TCD(50) and gamma(50), varies by prognostic subgroup. Our observations are consistent with the hypothesis that the shallow nature of clinically observed dose-response curves for local control result from a patient population that is a heterogeneous mixture of sub-populations with steeper dose-response curves and varying values of TCD(50). Such results may eventually help to identify patients, based on their individual pre-treatment prognostic factors, that would benefit most from dose-escalation, and to guide dose prescription.
Sabine Levegrün, Andrew Jackson, Michael J Zelefsky, Ennapadam S Venkatraman, Mark W Skwarchuk, Wolfgang Schlegel, Zvi Fuks, Steven A Leibel, and C Clifton Ling.
Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
Radiother Oncol. 2002 Apr 1; 63 (1): 11-26.
Background And PurposeWe fit phenomenological tumor control probability (TCP) models to biopsy outcome after three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer patients to quantify the local dose-response of prostate cancer.Materials And MethodsWe analyzed the outcome after photon beam 3D-CRT of 103 patients with stage T1c-T3 prostate cancer treated at Memorial Sloan-Kettering Cancer Center (MSKCC) (prescribed target doses between 64.8 and 81Gy) who had a prostate biopsy performed >or=2.5 years after end of treatment. A univariate logistic regression model based on D(mean) (mean dose in the planning target volume of each patient) was fit to the whole data set and separately to subgroups characterized by low and high values of tumor-related prognostic factors T-stage (or=T2c), Gleason score (6), and pre-treatment prostate-specific antigen (PSA) (10 ng/ml). In addition, we evaluated five different classifications of the patients into three risk groups, based on all possible combinations of two or three prognostic factors, and fit bivariate logistic regression models with D(mean) and the risk group category to all patients. Dose-response curves were characterized by TCD(50), the dose to control 50% of the tumors, and gamma(50), the normalized slope of the dose-response curve at TCD(50).ResultsD(mean) correlates significantly with biopsy outcome in all patient subgroups and larger values of TCD(50) are observed for patients with unfavorable compared to favorable prognostic factors. For example, TCD(50) for high T-stage patients is 7Gy higher than for low T-stage patients. For all evaluated risk group definitions, D(mean) and the risk group category are independent predictors of biopsy outcome in bivariate analysis. The fit values of TCD(50) show a clear separation of 9-10.6Gy between low and high risk patients. The corresponding dose-response curves are steeper (gamma(50)=3.4-5.2) than those obtained when all patients are analyzed together (gamma(50)=2.9).ConclusionsDose-response of prostate cancer, quantified by TCD(50) and gamma(50), varies by prognostic subgroup. Our observations are consistent with the hypothesis that the shallow nature of clinically observed dose-response curves for local control result from a patient population that is a heterogeneous mixture of sub-populations with steeper dose-response curves and varying values of TCD(50). Such results may eventually help to identify patients, based on their individual pre-treatment prognostic factors, that would benefit most from dose-escalation, and to guide dose prescription.