Background and purpose: The aim of this study is to derive "dose painting by numbers" prescriptions from retrospectively observed recurrence volumes in a patient group treated with conventional radiotherapy for head and neck squamous cell carcinoma. Materials and methods: The spatial relation between retrospectively observed recurrence volumes and pre-treatment standardized uptake values (SUV) from fluorodeoxyglucose positron emission tomography (FDG-PET) imaging was determined. Based on this information we derived SUV driven dose-response functions and used these to optimize ideal dose redistributions under the constraint of equal average dose to the tumor volumes as for a conventional treatment. The response functions were also implemented into a treatment planning system for realistic dose optimization. Results: The calculated tumor control probabilities (TCP) increased between 0.1-14.6% by the ideal dose redistributions for all included patients, where patients with larger and more heterogeneous tumors got greater increases than smaller and more homogeneous tumors. Conclusions: Dose painting prescriptions can be derived from retrospectively observed recurrence volumes spatial relation to pre-treatment FDG-PET image data. The ideal dose redistributions could significantly increase the TCP for patients with large tumor volumes and large spread in SUV from FDG-PET. The results yield a basis for prospective studies to determine the clinical value for dose painting of head and neck squamous cell carcinomas.

Background: Gleason scores for prostate cancer correlates with an increased recurrence risk after radiotherapy (RT). Furthermore, higher Gleason scores correlates with decreasing apparent diffusion coefficient (ADC) data from diffusion weighted MRI (DWI-MRI). Based on these observations, we present a formalism for dose painting prescriptions of prostate volumes based on ADC images mapped to Gleason score driven dose-responses.Methods: The Gleason score driven dose-responses were derived from a learning data set consisting of pre-RT biopsy data and post-RT outcomes for 122 patients treated with a homogeneous dose to the prostate. For a test data set of 18 prostate cancer patients with pre-RT ADC images, we mapped the ADC data to the Gleason driven dose-responses by using probability distributions constructed from published Gleason score correlations with ADC data. We used the Gleason driven dose-responses to optimize dose painting prescriptions that maximize the tumor control probability (TCP) with equal average dose as for the learning sets homogeneous treatment dose.Results: The dose painting prescriptions increased the estimated TCP compared to the homogeneous dose by 0-51% for the learning set and by 4-30% for the test set. The potential for individual TCP gains with dose painting correlated with increasing Gleason score spread and larger prostate volumes. The TCP gains were also found to be larger for patients with a low expected TCP for the homogeneous dose prescription.Conclusions: 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.

Background and Purpose The aim of this study was to evaluate the potential and robustness to increase the tumor control probability (TCP) for robustly optimized dose painting plans compared to conventional homogeneous dose plans for head and neck cancers.

Material and Methods We optimized a set of dose painting plans with a robust TCP maximizing objective under different mean dose constraints for the primary clinical target volume (CTVT). These plans were optimized with the robust mini-max algorithm together with dose-responses driven by standardized uptake values (SUV) from ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET). The robustness in TCP was evaluated through sampling treatment scenarios with iso-center displacements. We also analyzed the impact on TCP predictions by considering dose-response uncertainties.

Results The average increase in TCP with dose painting ranged between 3 to 20 percentage points (p.p.) which depended on the allowed integral CTVT dose. The median deviation in TCP increase was below 1p.p. for all sampled treatment scenarios versus the nominal plans. Patients with large tumors and large spread of SUV gained the greatest TCP increases. By considering dose-response uncertainties, a decrease of the TCP for a homogeneous dose yielded an increasing dose painting potential.

Conclusions We have found that it is feasible to optimize FDG-PET driven dose painting plans that robustly increase the TCP compared to homogeneous dose treatments for head and neck cancers. The greatest potential TCP increases were found for patients with larger and more SUV heterogeneous tumors, which may give guidance for patient selection to further test the presented dose painting formalism.

Background and Purpose

We have in this study evaluated our earlier published dose painting formalism for prostate cancer that is driven by dose-responses of Gleason scores mapped from apparent diffusion coefficient (ADC) image data. The aim of this study is to evaluate the ability to actualize increases of the tumor control probability (TCP) with optimization of “dose painting by numbers” (DPBN) plans in a treatment planning system (TPS) compared to uniform dose treatments for patients with high-risk prostate cancer.

Material and Methods

We have evaluated the potential to actualize TCP increases with realistic DPBN plans as compared to uniform dose treatments for a test set of 17 patients diagnosed with high-risk prostate cancer and pre-RT ADC image data. This potential was evaluated through calculating the DPBN efficiency, defined as the ratio of TCP increases for realistic DPBN plans by TCP increases for ideal DPBN prescriptions. Both the ideal DPBN prescriptions and the realistic DPBN plans were optimized with the objective to maximize the TCP for the target prostate volumes (CTVT) while retaining the same average dose as for conventional uniform dose treatments. For the realistic DPBN plan optimization we tested the impact on the TCP by applying different photon energies, different levels of precision of the mapping of ADC data into Gleason score driven dose-responses, and with respect to different levels of iso-center positioning uncertainties through optimizing with robust minimax optimization.

Results

The median DPBN efficiency for the most conservative planning scenario optimized with 15MV photons, a low precision ADC-to-Gleason mapping, and a robustness distance of 0.6 cm was 10%, meaning that more than half of the patients had a gain in TCP of at least 10% of the TCP for an ideal DPBN prescription. By using 6MV photons, increasing the precision of the ADC-to-Gleason mapping, and decreasing the robustness distance the median of the DPBN efficiency increased by up to 40%.

Conclusions

Optimization of DPBN plans in a TPS can according to our formalism yield TCP increases compared to conventional uniform dose treatments for prostate cancer. These TCP increases are more likely when there is a high precision on the mapping of image data into dose-responses and a high certainty of the tumor position during treatment. These findings motivate further development to ensure accurate and precise mappings of image data into dose-responses and to ensure a high spatial certainty of the tumor position when implementing DPBN in a TPS.