Applying a variable relative biological effectiveness (RBE) might affect the analysis of clinical trials comparing photon and proton therapy for prostate cancer.

Applying a variable relative biological effectiveness (RBE) might affect the analysis of clinical trials comparing photon and proton therapy for prostate cancer. Phys Med Biol. 2019 May 13;: Authors: Marteinsdottir M, Paganetti H Abstract The purpose of this study was the evaluation of the impact of a variable relative biological effectiveness (RBE) compared to a constant RBE value of 1.1 in proton therapy prostate trials due to uncertainties in α/β ratio.
 
 Twenty patients receiving passive scattered proton therapy (PSPT) and fifteen patients receiving intensity modulated proton therapy (IMPT) were compared to twenty patients treated with 7-field intensity modulated photon therapy (IMRT). For proton beam therapy (PBT), the RBE was estimated using two different RBE models. Tumor control probabilities (TCP) and normal tissue complication probabilities (NTCP) were assessed.
 
 For one of the RBE models, dosimetric indices assuming a low α/β were ~10-11% larger compared to using a fixed RBE. A different model resulted in 1-3% lower values independent of α/β. Comparing PBT with IMRT revealed a negligible difference in TCP for a fixed RBE. Applying a variable RBE revealed an increase in TCP by 6% for PBT compared to IMRT for one model but a decrease of 2% for the other. Variable RBE values in PSPT resulted in an increase in NTCP for rectum from 7% to 11% for a fixed RBE with one model but a dec...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research

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Obtaining accurate segmentation of the prostate and nearby organs at risk (e.g., bladder and rectum) in CT images is critical for radiotherapy of prostate cancer. Currently, the leading automatic segmentation algorithms are based on Fully Convolutional Networks (FCNs), which achieve remarkable performance but usually need large-scale datasets with high-quality voxel-wise annotations for full supervision of the training. Unfortunately, such annotations are difficult to acquire, which becomes a bottleneck to build accurate segmentation models in real clinical applications. In this paper, we propose a novel weakly supervised ...
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