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?

BACKGROUND AND PURPOSE: The aim of this study was to evaluate the potential to increase the tumor control probability (TCP) with 'dose painting by numbers' (DPBN) plans optimized in a treatment planning system (TPS) compared to uniform dose plans. The DPBN optimization was based on our earlier published formalism for prostate cancer that is driven by dose-responses of Gleason scores mapped from apparent diffusion coefficients (ADC). MATERIAL AND METHODS: For 17 included patients, a set of DPBN plans were optimized in a TPS by maximizing the TCP for an equal average dose to the prostate volume (CTVT) as for a conventional uniform dose treatment. For the plan optimizations we applied different photon energies, different precisions for the ADC-to-Gleason mappings, and different CTVT positioning uncertainties. The TCP increasing potential was evaluated by the DPBN efficiency, defined as the ratio of TCP increases for DPBN plans by TCP increases for ideal DPBN prescriptions (optimized without considering radiation transport phenomena, uncertainties of the CTVT positioning, and uncertainties of the ADC-to-Gleason mapping). RESULTS: The median DPBN efficiency for the most conservative planning scenario optimized with a low precision ADC-to-Gleason mapping, and a positioning uncertainty of 0.6 cm was 10%, meaning that more than half of the patients had a TCP gain of at least 10% of the TCP for an ideal DPBN prescription. By increasing the precision of the ADC-to-Gleason mapping, and decreasing the positioning uncertainty the median DPBN efficiency increased by up to 40%. CONCLUSIONS: TCP increases with DPBN plans optimized in a TPS were found more likely with a high precision mapping of image data into dose-responses and a high certainty of the tumor positioning. These findings motivate further development to ensure precise mappings of image data into dose-responses and to ensure a high spatial certainty of the tumor positioning when implementing DPBN clinically.

Robust maximization of tumor control probability for radicality constrained radiotherapy dose painting by numbers of head and neck cancer.

BACKGROUND AND PURPOSE: Radiotherapy with dose painting by numbers (DPBN) needs another approach than conventional margins to ensure a geometrically robust dose coverage for the tumor. This study presents a method to optimize DPBN plans that as opposed to achieve a robust dose distribution instead robustly maximize the tumor control probability (TCP) for patients diagnosed with head and neck cancer. MATERIAL AND METHODS: Volumetric-modulated arc therapy (VMAT) plans were optimized with a robust TCP maximizing objective for different dose constraints to the primary clinical target volume (CTVT) for a set of 20 patients. These plans were optimized with minimax optimization together with dose-responses driven by standardized uptake values (SUV) from 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET). The robustness in TCP was evaluated through sampling treatment scenarios with isocenter displacements. RESULTS: The average increase in TCP with DPBN compared to a homogeneous dose treatment ranged between 3 and 20 percentage points (p.p.) which depended on the different dose constraints for the CTVT. The median deviation in TCP increase was below 1p.p. for all sampled treatment scenarios versus the nominal plans. The standard deviation of SUV multiplied by the CTVT volume were found to correlate with the TCP gain with R 2 ≥ 0.9. CONCLUSIONS: Minimax optimization of DPBN plans yield, based on the presented TCP modelling, a robust increase of the TCP compared to homogeneous dose treatments for head and neck cancers. The greatest TCP gains were found for patients with large and SUV heterogeneous tumors, which may give guidance for patient selection in prospective trials.

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

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.

Dose painting by numbers based on retrospectively determined recurrence probabilities.

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.