A tumour control probability model for radiotherapy of prostate cancer using magnetic resonance imaging-based apparent diffusion coefficient maps.

BACKGROUND AND
PURPOSE: Standard tumour control probability (TCP) models assume uniform tumour cell density across the tumour. The aim of this study was to develop an individualised TCP model by including index-tumour regions extracted form multi-parametric magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps-based cell density distributions.
MATERIALS AND METHODS: ADC maps in a series of 20 prostate cancer patients were applied to estimate the initial number of cells within each voxel, using three different approaches for the relation between ADC values and cell density: a linear, a binary and a sigmoid relation. All TCP models were based on linear-quadratic cell survival curves assuming α/β=1.93Gy (consistent with a recent meta-analysis) and α set to obtain a 70% of TCP when 77Gy was delivered to the entire prostate in 35 fractions (α=0.18Gy(-1)).
RESULTS: Overall, TCP curves based on ADC maps showed larger differences between individuals than those assuming uniform cell densities. The range of the dose required to reach 50% TCP across the patient cohort was 20.1Gy, 18.7Gy and 13.2Gy using an MRI-based voxel density (linear, binary and sigmoid approach, respectively), compared to 4.1Gy using a constant density.
CONCLUSIONS: Inclusion of tumour-index information together with ADC maps-based cell density increases inter-patient tumour response differentiation for use in prostate cancer RT, resulting in TCP curves with a larger range in D50% across the cohort compared with those based on uniform cell densities.