Personalized precision radiotherapy by integration of multi-parametric functional and biological imaging in prostate cancer: A feasibility study

Background: To increase tumour control probability (TCP) in prostate cancer a method was developed integrating multi parametric functional and biological information into a dose painting treatment plan aiming focal dose-escalation to tumour sub-volumes. A dose-escalation map was derived considering individual, multi-parametric estimated tumour aggressiveness. Methods and Materials: Multi-parametric functional imaging (MRI, Choline-/PSMA-/FMISO-PET/CT) was acquired for a high risk prostate cancer patient with a high level of tumour load (cT3b cNO cM0) indicated by subtotal involvement of prostate including the right seminal vesicle and by PSA-level >100. Probability of tumour presence was determined by a combination of multi parametric functional image information resulting in a voxel-based map of tumour aggressiveness. This probability map was directly integrated into dose optimization in order to plan for inhomogeneous, biological imaging based dose painting. Histograms of the multi parametric prescription function were generated in addition to a differential histogram of the planned inhomogeneous doses. Comparison of prescribed doses with planned doses on a voxel level was realized using an effective DVH, containing the ratio of prescribed vs. planned dose for each tumour voxel. Results: Multi parametric imaging data of PSMA, Choline and FMISO PET/CT as well as ADC maps derived from diffusion weighted MRI were combined to an individual probability map of tumour presence. Voxel-based prescription doses ranged from 75.3 Gy up to 93.4 Gy (median: 79.6 Gy), whereas the planned dose painting doses varied only between 72.5 and 80.0 Gy with a median dose of 75.7 Gy. However, inhomogeneous voxel-based dose prescriptions can only be implemented into a treatment plan until a certain level. Conclusion: Multi-parametric probability based dose painting in prostate cancer is technically and clinically feasible. However, detailed calibration functions to define the necessary probability functions need to be assessed in future clinical trials.