Literature DB >> 12065099

Risk group dependence of dose-response for biopsy outcome after three-dimensional conformal radiation therapy of prostate cancer.

Sabine Levegrün1, Andrew Jackson, Michael J Zelefsky, Ennapadam S Venkatraman, Mark W Skwarchuk, Wolfgang Schlegel, Zvi Fuks, Steven A Leibel, C Clifton Ling.   

Abstract

BACKGROUND AND
PURPOSE: 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.
MATERIALS AND METHODS: We 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 (<T2c vs. >or=T2c), Gleason score (<or=6 vs. >6), and pre-treatment prostate-specific antigen (PSA) (<or=10 ng/ml vs. >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).
RESULTS: D(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).
CONCLUSIONS: 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.

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Year:  2002        PMID: 12065099     DOI: 10.1016/s0167-8140(02)00062-2

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  22 in total

1.  On Voxel based Iso-Tumor Control Probabilty and Iso-Complication Maps for Selective Boosting and Selective Avoidance Intensity Modulated Radiotherapy.

Authors:  Yusung Kim; Wolfgang A Tomé
Journal:  Imaging Decis (Berl)       Date:  2008

2.  The lessons of QUANTEC: recommendations for reporting and gathering data on dose-volume dependencies of treatment outcome.

Authors:  Andrew Jackson; Lawrence B Marks; Søren M Bentzen; Avraham Eisbruch; Ellen D Yorke; Randal K Ten Haken; Louis S Constine; Joseph O Deasy
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-01       Impact factor: 7.038

3.  Risk-adaptive optimization: selective boosting of high-risk tumor subvolumes.

Authors:  Yusung Kim; Wolfgang A Tomé
Journal:  Int J Radiat Oncol Biol Phys       Date:  2006-12-01       Impact factor: 7.038

4.  On the impact of functional imaging accuracy on selective boosting IMRT.

Authors:  Y Kim; W A Tomé
Journal:  Phys Med       Date:  2008-01-18       Impact factor: 2.685

5.  Is it beneficial to selectively boost high-risk tumor subvolumes? A comparison of selectively boosting high-risk tumor subvolumes versus homogeneous dose escalation of the entire tumor based on equivalent EUD plans.

Authors:  Yusung Kim; Wolfgang A Tome
Journal:  Acta Oncol       Date:  2008       Impact factor: 4.089

6.  Dose-escalated simultaneous integrated-boost treatment of prostate cancer patients via helical tomotherapy.

Authors:  M Geier; S T Astner; M N Duma; V Jacob; C Nieder; J Putzhammer; C Winkler; M Molls; H Geinitz
Journal:  Strahlenther Onkol       Date:  2012-02-26       Impact factor: 3.621

Review 7.  High-risk prostate cancer-classification and therapy.

Authors:  Albert J Chang; Karen A Autio; Mack Roach; Howard I Scher
Journal:  Nat Rev Clin Oncol       Date:  2014-05-20       Impact factor: 66.675

8.  On the Inclusion of Short-distance Bystander Effects into a Logistic Tumor Control Probability Model.

Authors:  David G Tempel; N Patrik Brodin; Wolfgang A Tomé
Journal:  Cureus       Date:  2018-01-01

Review 9.  70 Gy or more: which dose for which prostate cancer?

Authors:  U Ganswindt; F Paulsen; A G Anastasiadis; A Stenzl; M Bamberg; C Belka
Journal:  J Cancer Res Clin Oncol       Date:  2005-05-11       Impact factor: 4.553

10.  Post-radiotherapy prostate biopsies reveal heightened apex positivity relative to other prostate regions sampled.

Authors:  Kris T Huang; Radka Stoyanova; Gail Walker; Kiri Sandler; Matthew T Studenski; Nesrin Dogan; Tahseen Al-Saleem; Mark K Buyyounouski; Eric M Horwitz; Alan Pollack
Journal:  Radiother Oncol       Date:  2015-05-08       Impact factor: 6.280

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