Voxel-level biological optimisation of prostate IMRT using patient-specific tumour location and clonogen density derived from mpMRI

Abstract

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.

A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy

“…Second, no ground truth is available for assessment of inter-patient registration, making quantification of uncertainty in the registration process difficult. Third, conversion of cross-sectional to volumetric cell density requires linear scaling: one methodology used previously by our research team [50] computes the scaling factor based on estimated clonogenic cell numbers [51]. Fourth, classification of tumour lesions was performed manually by one experienced urological pathologist and may be subject to observer variability.…”

“…Building on our previous work, we will demonstrate the process of co-registration of this model with in vivo imaging data to produce biologically optimised dose distributions for external beam RT or brachytherapy applications [55][56][57][58]. Using models for tumour probability and cell density, it is straightforward to derive a map of clonogen density using voxel-wise multiplication [50]. This provides requisite data for radiobiological optimisation of RT dose distributions.…”

Biologically Targeted Radiation Therapy: Incorporating Patient-Specific Hypoxia Data Derived from Quantitative Magnetic Resonance Imaging

“…In addition to hypoxia modelling, the TCP model used in this study has further limitations in describing tumour radiation response. In this study we have chosen to use our previously validated TCP model [ 36 ], a model that we have used in a number of subsequent biologically targeted radiotherapy studies [ 6 , 35 , 54 ]. As in our previous studies, cell-to-cell communication, intrafraction repair and accelerated repopulation were excluded.…”

Robust dose‐painting‐by‐numbers vs. nonselective dose escalation for non‐small cell lung cancer patients

“…The second method, which is the topic of the current study, aims to directly optimize the probability of tumor control. This requires conversion of 3D tracer maps to 3D maps of intra‐tumor radiosensitivity 19–24 . Theoretical studies that assumed that any dose level could be delivered to any voxel have shown that, if the radiosensitivity of each voxel is known, DPBN could lead to gains in tumor control probability (TCP) of more than 30 percentage points without increasing normal tissue exposure 2,3 …”

“…This requires conversion of 3D tracer maps to 3D maps of intra-tumor radiosensitivity. [19][20][21][22][23][24] Theoretical studies that assumed that any dose level could be delivered to any voxel have shown that, if the radiosensitivity of each voxel is known, DPBN could lead to gains in tumor control probability (TCP) of more than 30 percentage points without increasing normal tissue exposure. 2,3 These gains are very promising, but the gains should be interpreted with caution since they may be affected by multiple patient-specific factors.…”