Evaluating the impact of a rigid and a deformable registration method of pre-treatment images for hypoxia-based dose painting.

PURPOSE: To assess the impact of rigid and deformable image registration methods (RIR, DIR) on the outcome of a hypoxia-based dose painting strategy. MATERIALS AND METHODS: Thirty head and neck cancer patients were imaged with [18F]FMISO-PET/CT before radiotherapy. [18F]FMISO-PET/CT images were registered to the planning-CT by RIR or DIR. The [18F]FMISO uptake was converted into oxygen partial pressure (pO2) maps. Hypoxic Target Volumes were contoured on pO2 maps for the deformed (HTVdef) and non-deformed (HTV) cases. A dose escalation strategy by contours, aiming at 95 % tumour control probability (TCP), was applied. HTVs were characterised based on geometry-related metrics, the underlying pO2 distribution, and the dose boost level. A dosimetric and radiobiological evaluation of selected treatment plans made considering RIR and DIR was performed. Moreover, the TCP of the RIR dose distribution was evaluated when considering the deformed [18F]FMISO-PET image as an indicator of the actual target radiosensitivity to determine the potential impact of an unalignment. RESULTS: Statistically significant differences were found between HTV and HTVdef for volume-based metrics and underlying pO2 distribution. Eight out of nine treatment plans for HTV and HTVdef showed differences on the level 10 %/3 mm on a gamma analysis. The TCP difference, however, between RIR and the case when the RIR dose distribution was used with the deformed radiosensitivity map was below 2 pp. CONCLUSIONS: Although the choice of the CTplan-to-PET registration method pre-treatment impacts the HTV localisation and morphology and the corresponding dose distribution, it negligibly affects the TCP in the proposed dose escalation strategy by contours.

Quantification of Tumor Oxygenation Based on FMISO PET: Influence of Location and Oxygen Level of the Well-Oxygenated Reference Region.

Tumor hypoxia may play a fundamental role in determining the radiotherapy outcome for several cancer types. Functional imaging with hypoxia specific radiotracers offers a way to visualize and quantify regions of increased radioresistance, which may benefit from dose escalation strategies. Conversion of the uptake in positron emission tomography (PET) images into oxygenation maps offers a way to quantitatively characterize the microenvironment. However, normalization of the uptake with respect to a well-oxygenated reference volume (WOV), which should be properly selected, is necessary when using conversion functions. This study aims at assessing the sensitivity of quantifying tumor oxygenation based on 18F-fluoromisonidazole (FMISO) PET with respect to the choice of the location and the oxygenation level of the WOV in head and neck cancer patients. WOVs varying not only in shape and location but also with respect to the assigned pO2 level were considered. pO2 values other than the standard 60 mmHg were selected according to the specific tissue type included in the volume. For comparison, the volume which would be considered as hypoxic based on a tissue-to-muscle ratio equal to 1.4 was also delineated, as conventionally done in clinical practice. Hypoxia mapping strategies are found highly sensitive to selection of the location of well-oxygenated region, but also on its assigned oxygenation level, which is crucial for hypoxia-guided adaptive dose escalation strategies.

Dose painting by means of Monte Carlo treatment planning at the voxel level.

PURPOSE: To develop a new optimization algorithm to carry out true dose painting by numbers (DPBN) planning based on full Monte Carlo (MC) calculation. METHODS: Four configurations with different clustering of the voxel values from PET data were proposed. An optimization method at the voxel level under Lineal Programming (LP) formulation was used for an inverse planning and implemented in CARMEN, an in-house Monte Carlo treatment planning system. RESULTS: Beamlet solutions fulfilled the objectives and did not show significant differences between the different configurations. More differences were observed between the segment solutions. The plan for the dose prescription map without clustering was the better solution. CONCLUSIONS: LP optimization at voxel level without dose-volume restrictions can carry out true DPBN planning with the MC accuracy.

Correction: 3D VMAT Verification Based on Monte Carlo Log File Simulation with Experimental Feedback from Film Dosimetry.

[This corrects the article DOI: 10.1371/journal.pone.0166767.].

3D VMAT Verification Based on Monte Carlo Log File Simulation with Experimental Feedback from Film Dosimetry.

A model based on a specific phantom, called QuAArC, has been designed for the evaluation of planning and verification systems of complex radiotherapy treatments, such as volumetric modulated arc therapy (VMAT). This model uses the high accuracy provided by the Monte Carlo (MC) simulation of log files and allows the experimental feedback from the high spatial resolution of films hosted in QuAArC. This cylindrical phantom was specifically designed to host films rolled at different radial distances able to take into account the entrance fluence and the 3D dose distribution. Ionization chamber measurements are also included in the feedback process for absolute dose considerations. In this way, automated MC simulation of treatment log files is implemented to calculate the actual delivery geometries, while the monitor units are experimentally adjusted to reconstruct the dose-volume histogram (DVH) on the patient CT. Prostate and head and neck clinical cases, previously planned with Monaco and Pinnacle treatment planning systems and verified with two different commercial systems (Delta4 and COMPASS), were selected in order to test operational feasibility of the proposed model. The proper operation of the feedback procedure was proved through the achieved high agreement between reconstructed dose distributions and the film measurements (global gamma passing rates > 90% for the 2%/2 mm criteria). The necessary discretization level of the log file for dose calculation and the potential mismatching between calculated control points and detection grid in the verification process were discussed. Besides the effect of dose calculation accuracy of the analytic algorithm implemented in treatment planning systems for a dynamic technique, it was discussed the importance of the detection density level and its location in VMAT specific phantom to obtain a more reliable DVH in the patient CT. The proposed model also showed enough robustness and efficiency to be considered as a pre-treatment VMAT verification system.

MCTP system model based on linear programming optimization of apertures obtained from sequencing patient image data maps.

PURPOSE: The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. METHODS: The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called "biophysical" map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process. RESULTS: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast irradiation case (Case II) solved with photon and electron modulated beams (IMRT + MERT); and a prostatic bed case (Case III) with a pronounced concave-shaped PTV by using volumetric modulated arc therapy. In the three cases, the required target prescription doses and constraints on organs at risk were fulfilled in a short enough time to allow routine clinical implementation. The quality assurance protocol followed to check CARMEN system showed a high agreement with the experimental measurements. CONCLUSIONS: A Monte Carlo treatment planning model exclusively based on maps performed from patient imaging data has been presented. The sequencing of these maps allows obtaining deliverable apertures which are weighted for modulation under a linear programming formulation. The model is able to solve complex radiotherapy treatments with high accuracy in an efficient computation time.