Dose painting of prostate cancer based on Gleason score correlations with apparent diffusion coefficients

Abstract

We have from retrospective treatment data demonstrated a formalism that yield ADC driven dose painting prescriptions for prostate volumes that potentially can yield significant TCP increases without increasing dose burdens as compared to a homogeneous treatment dose. This motivates further development of the approach to consider more accurate ADC to Gleason mappings, issues with delivery robustness of heterogeneous dose distributions, and patient selection criteria for design of clinical trials.

Robust treatment planning of dose painting for prostate cancer based on ADC-to-Gleason score mappings – what is the potential to increase the tumor control probability?

“…Furthermore, increasing Gleason scores are shown to correlate with decreasing apparent diffusion coefficient (ADC) data acquired from diffusion weighted MR imaging [17][18][19][20][21][22][23]. Similar to Ghobadi et al [16], we hypothesized in our earlier study [24] that Gleason scores could be used to predict differentiated dose-responses and thus used to maximize the TCP with the average dose constrained to that for conventional uniform dose treatments. The underlying Gleason driven doseresponse functions were derived from a learning set of pre-RT Gleason scores and post-RT outcomes from 122 high-risk patients treated with a uniform dose of 91.6 Gy EQD 2 .…”

“…The endpoint for TCP was freedom from biochemical recurrences (BCR) 5-years post-RT. In our previously published DPBN formalism [24] we derived heterogeneous dose painting prescriptions based on image predicted dose-responses to maximize the TCP per used radiant energy. We did so without considering any practical circumstances for safe delivery such as geometrical uncertainties or dose gradient limitations from particle transport physics, etc.…”

Potentials and challenges of diffusion-weighted magnetic resonance imaging in radiotherapy

“…Different concepts for adapting therapy to functional image information within the tumor are available such as a boost of RT dose to the whole tumor volume, region-based adaptation by the definition of boost doses to biological target volumes, or a voxel-based adaptation of dose such as dose-painting by numbers (DPBN) [99] . First planning studies for intra-tumor RT dose adaptation based on DWI have demonstrated the dosimetric feasibility [100] , [101] . Due to the predictive value of DWI for treatment outcome dose adaptations seem promising, however, comprehensive clinical studies are required to prove the validity of this concept.…”

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning

“…The BiRT approach reduces the radiation doses to non-tumour regions to minimise toxicity, whilst boost focal therapy approaches maintain the dose to healthy tissues and escalate the dose to the tumour (e.g., the FLAME and HEIGHT trial [28]). Similarly, recently published dose painting studies [29,30] rely on using ADC maps from DWI for calculating the non-uniform dose distribution, while the BiRT approach utilises the whole mpMRI sequences (T2w MRI, DWI and DCE-MRI).…”