Aims This study aimed to develop a framework for optimising prostate intensity-modulated radiotherapy (IMRT) based on patient-specific tumour biology, derived from multiparametric MRI (mpMRI). The framework included a probabilistic treatment planning technique in the effort to yield dose distributions with an improved expected treatment outcome compared with uniform-dose planning approaches. Methods IMRT plans were generated for five prostate cancer patients using two inverse planning methods: uniform-dose to the planning target volume and probabilistic biological optimisation for clinical target volume tumour control probability (TCP) maximisation. Patient-specific tumour location and clonogen density information were derived from mpMRI and geometric uncertainties were incorporated in the TCP calculation. Potential reduction in dose to sensitive structures was assessed by comparing dose metrics of uniform-dose plans with biologically-optimised plans of an equivalent level of expected tumour control. Results The planning study demonstrated biological optimisation has the potential to reduce expected normal tissue toxicity without sacrificing local control by shaping the dose distribution to the spatial distribution of tumour characteristics. On average, biologically-optimised plans achieved 38.6% (p-value: < 0.01) and 51.2% (p-value: < 0.01) reduction in expected rectum and bladder equivalent uniform dose, respectively, when compared with uniform-dose planning. Conclusions It was concluded that varying the dose distribution within the prostate to take account for each patient’s clonogen distribution was feasible. Lower doses to normal structures compared to uniform-dose plans was possible whilst providing robust plans against geometric uncertainties. Further validation in a larger cohort is warranted along with considerations for adaptive therapy and limiting urethral dose.

Advances in imaging have enabled the identification of prostate cancer foci with an initial application to focal dose escalation, with subvolumes created with image intensity thresholds. Through quantitative imaging techniques, correlations between image parameters and tumour characteristics have been identified. Mathematical functions are typically used to relate image parameters to prescription dose to improve the clinical relevance of the resulting dose distribution. However, these relationships have remained speculative or invalidated. In contrast, the use of radiobiological models during treatment planning optimisation, termed biological optimisation, has the advantage of directly considering the biological effect of the resulting dose distribution. This has led to an increased interest in the accurate derivation of radiobiological parameters from quantitative imaging to inform the models. This article reviews the progress in treatment planning using image-informed tumour biology, from focal dose escalation to the current trend of individualised biological treatment planning using image-derived radiobiological parameters, with the focus on prostate intensity-modulated radiotherapy (IMRT).

Purpose: Hypoxia has been linked to radioresistance. Strategies to safely dose escalate dominant intraprostatic lesions have shown promising results, but further dose escalation to overcome the effects of hypoxia require a novel approach to constrain the dose in normal tissue.to safe levels. In this study, we demonstrate a biologically targeted radiotherapy (BiRT) approach that can utilise multiparametric magnetic resonance imaging (mpMRI) to target hypoxia for favourable treatment outcomes. Methods: mpMRI-derived tumour biology maps, developed via a radiogenomics study, were used to generate individualised, hypoxia-targeting prostate IMRT plans using an ultra- hypofractionation schedule. The spatial distribution of mpMRI textural features associated with hypoxia-related genetic profiles was used as a surrogate of tumour hypoxia. The effectiveness of the proposed approach was assessed by quantifying the potential benefit of a general focal boost approach on tumour control probability, and also by comparing the dose to organs at risk (OARs) with hypoxia-guided focal dose escalation (DE) plans generated for five patients. Results: Applying an appropriately guided focal boost can greatly mitigate the impact of hypoxia. Statistically significant reductions in rectal and bladder dose were observed for hypoxia-targeting, biologically optimised plans compared to isoeffective focal DE plans. Conclusion: Results of this study suggest the use of mpMRI for voxel-level targeting of hypoxia, along with biological optimisation, can provide a mechanism for guiding focal DE that is considerably more efficient than application of a general, dose-based optimisation, focal boost.

Background: In prostate radiation therapy, recent studies have indicated a benefit in increasing the dose to intraprostatic lesions (IPL) compared with standard whole gland radiation therapy. Such approaches typically aim to deliver a target dose to the IPL(s) with no deliberate effort to modulate the dose within the IPL. Prostate cancers demonstrate intra-tumor heterogeneity and hence it is hypothesized that further gains in the optimal delivery of radiation therapy can be achieved through modulation of the dose distribution within the tumor. To account for tumor heterogeneity, biologically targeted radiation therapy (BiRT) aims to utilize a voxel-wise approach to IPL dose prescription by incorporating knowledge of the spatial distribution of tumor characteristics. Purpose:The aim of this study was to develop a workflow for generating voxelwise optimal dose prescriptions that maximize patient tumor control probability (TCP), and evaluate the feasibility and benefits of applying this workflow on a cohort of 62 prostate cancer patients. Method: The source data for this proof -of -concept study included high resolution histology images annotated with tumor location and grade. Image processing techniques were used to compute voxel-level cell density distribution maps. An absolute tumor cell distribution was calculated via linearly scaling according to published estimated tumor cell numbers. For the IPLs of each patient, optimal dose prescriptions were obtained via three alternative methods for redistribution of IPL boost doses according to maximization of TCP. The radiosensitivity uncertainties were considered using a truncated log-normally distributed linear radiosensitivity parameter (𝛼 k ) and compared with Gleason pattern (GP) dependent radiosensitivity parameters that were derived based on previously published methods. An ensemble machine learning method was implemented to identify patient-specific features that predict the TCP improvement resulting from dose redistribution relative to a uniform dose distribution. Results:The Gleason pattern-dependent radiosensitivity parameters were calculated for 20 published prostate cancer 𝛼∕𝛽 ratios. Optimal voxel-level dose prescriptions were generated for all 62 PCa patients. For all dose redistribution scenarios, the optimal dose distribution always shows a higher (or equivalent) TCP level than the uniform dose distribution. The applied random forest regressor could predict patient-specific TCP improvement with low root mean square error (≤1.5%) by using total tumor number, volume of IPLs and the standard deviation of tumor cell number among all voxels.