Dose Painting By Numbers Research Articles

Plan robustness and RBE influence for proton dose painting by numbers for head and neck cancers

PurposeTo investigate the feasibility of dose painting by numbers (DPBN) with respect to robustness for proton therapy for head and neck cancers (HNC), and to study the influence of variable RBE on the TCP and OAR dose burden. Methods and materialsData for 19 patients who have been scanned pretreatment with PET-FDG and subsequently treated with photon therapy were used in the study. A dose response model developed for photon therapy was implemented in a TPS, allowing DPBN plans to be created. Conventional homogeneous dose and DPBN plans were created for each patient, optimized with either fixed RBE = 1.1 or a variable RBE model. Robust optimization was used to create clinically acceptable plans. To estimate the maximum potential loss in TCP due to actual SUV variations from the pre-treatment imaging, we applied a test case with randomized SUV distribution. ResultsRegardless of the use of variable RBE for optimization or evaluation, a statistically significant increase (p < 0.001) in TCP was found for DPBN plans as compared to homogeneous dose plans. Randomizing the SUV distribution decreased the TCP for all plans. A correlation between TCP increase and variance of the SUV distribution and target volume was also found. ConclusionDPBN for protons and HNC is feasible and could lead to a TCP gain. Risks associated with the temporal variation of SUV distributions could be mitigated by imposing minimum doses to targets. The correlation found between TCP increase and SUV variance and target volume may be used for patient selection.

Safety of Inhomogeneous Dose Distribution IMRT for High-Grade Glioma Reirradiation: A Prospective Phase I/II Trial (GLIORAD TRIAL).

Simple SummaryGlioblastoma multiforme (GBM) is the most frequent primary malignant brain tumor, and despite advances in imaging techniques and treatment options, the outcome remains poor and recurrence is inevitable. Salvage therapy presents a challenge, and re-irradiation can be a therapeutic option in recurrent GBM. The decision-making process for re-irradiation is a challenge for radiation oncologists due to the expected toxicity of a second course of radiotherapy and the limited radiation tolerance of normal tissue; nevertheless, it is being increasingly used, as several studies have demonstrated its feasibility. The current study aimed to investigate the safety of moderate–high-voxel-based dose escalation radiotherapy in recurrent GBM patients after conventional concurrent chemoradiation. Twelve patients were enrolled in this prospective single-center study. Retreatment consisted of re-irradiation with a total dose range of 30–50 Gy over 5 days using the IMRT (arc VMAT) technique using dose painting planning. The treatment was well tolerated. No toxicities greater than 3 were recorded; only one patient had severe G3 acute toxicity, characterized by muscle weakness and fatigue. Median overall survival (OS2) and progression-free survival (PFS2) from the time of re-irradiation were 10.4 months and 5.7 months, respectively. Our phase I study demonstrated an acceptable tolerance profile of this approach, and the future prospective phase II study will analyze the efficacy in terms of PFS and OS.Glioblastoma multiforme (GBM) is the most aggressive astrocytic primary brain tumor, and concurrent temozolomide (TMZ) and radiotherapy (RT) followed by maintenance of adjuvant TMZ is the current standard of care. Despite advances in imaging techniques and multi-modal treatment options, the median overall survival (OS) remains poor. As an alternative to surgery, re-irradiation (re-RT) can be a therapeutic option in recurrent GBM. Re-irradiation for brain tumors is increasingly used today, and several studies have demonstrated its feasibility. Besides differing techniques, the published data include a wide range of doses, emphasizing that no standard approach exists. The current study aimed to investigate the safety of moderate–high-voxel-based dose escalation in recurrent GBM. From 2016 to 2019, 12 patients met the inclusion criteria and were enrolled in this prospective single-center study. Retreatment consisted of re-irradiation with a total dose of 30 Gy (up to 50 Gy) over 5 days using the IMRT (arc VMAT) technique. A dose painting by numbers (DPBN)/dose escalation plan were performed, and a continuous relation between the voxel intensity of the functional image set and the risk of recurrence in that voxel were used to define target and dose distribution. Re-irradiation was well tolerated in all treated patients. No toxicities greater than G3 were recorded; only one patient had severe G3 acute toxicity, characterized by muscle weakness and fatigue. Median overall survival (OS2) and progression-free survival (PFS2) from the time of re-irradiation were 10.4 months and 5.7 months, respectively; 3-, 6-, and 12-month OS2 were 92%, 75%, and 42%, respectively; and 3-, 6-, and 12-month PFS2 were 83%, 42%, and 8%, respectively. Our work demonstrated a tolerable tolerance profile of this approach, and the future prospective phase II study will analyze the efficacy in terms of PFS and OS.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy.

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Positron emission tomography guided dose painting by numbers of lung cancer: Alanine dosimetry in an anthropomorphic phantom.

Dose painting by numbers (DPBN) require a high degree of dose modulation to fulfill the image-based voxel wise dose prescription. The aim of this study was to assess the dosimetric accuracy of 18F-fluoro-2-deoxy-glucose positron emission tomography(18F-FDG-PET)-based DPBN in an anthropomorphic lung phantom using alanine dosimetry. A linear dose prescription based on 18F-FDG-PET image intensities within the gross tumor volume (GTV) of a lung cancer patient was employed. One DPBN scheme with low dose modulation (Scheme A; minimum/maximum fraction dose to the GTV 2.92/4.26Gy) and one with a high modulation (Scheme B; 2.81/4.52Gy) were generated. The plans were transferred to a computed tomograpy (CT) scan of a thorax phantom based on CT images of the patient. Using volumetric modulated arc therapy (VMAT), DPBN was delivered to the phantom with embedded alanine dosimeters. A plan was also delivered to an intentionally misaligned phantom. Absorbed doses at various points in the phantom were measured by alanine dosimetry. A pointwise comparison between GTV doses from prescription, treatment plan calculation and VMAT delivery showed high correspondence, with a mean and maximum dose difference of <0.1Gy and 0.3Gy, respectively. No difference was found in dosimetric accuracy between scheme A and B. The misalignment caused deviations up to 1Gy between prescription and delivery. DPBN can be delivered with high accuracy, showing that the treatment may be applied correctly from a dosimetric perspective. Still, misalignment may cause considerable dosimetric erros, indicating the need for patient immobilization and monitoring.

Repeatability of hypoxia dose painting by numbers based on EF5-PET in head and neck cancer

Background Hypoxia dose painting is a radiotherapy technique to increase the dose to hypoxic regions of the tumour. Still, the clinical effect relies on the reproducibility of the hypoxic region shown in the medical image. 18F-EF5 is a hypoxia tracer for positron emission tomography (PET), and this study investigated the repeatability of 18F-EF5-based dose painting by numbers (DPBN) in head and neck cancer (HNC). Materials and methods Eight HNC patients undergoing two 18F-EF5-PET/CT sessions (A and B) before radiotherapy were included. A linear conversion of PET signal intensity to radiotherapy dose prescription was employed and DPBN treatment plans were created using the image basis acquired at each PET/CT session. Also, plan A was recalculated on the image basis for session B. Voxel-by-voxel Pearson’s correlation and quality factor were calculated to assess the DPBN plan quality and repeatability. Results The mean (SD) correlation coefficient between DPBN prescription and plan was 0.92 (0.02) and 0.93 (0.02) for sessions A and B, respectively, with corresponding quality factors of 0.02 (0.002) and 0.02 (0.003), respectively. The mean correlation between dose prescriptions at day A and B was 0.72 (0.13), and 0.77 (0.12) for the corresponding plans. A mean correlation of 0.80 (0.08) was found between plan A, recalculated on image basis B, and plan B. Conclusion Hypoxia DPBN planning based on 18F-EF5-PET/CT showed high repeatability. This illustrates that 18F-EF5-PET provides a robust target for dose painting.

Robust dose-painting-by-numbers vs. nonselective dose escalation for non-small cell lung cancer patients.

PurposeTheoretical studies have shown that dose‐painting‐by‐numbers (DPBN) could lead to large gains in tumor control probability (TCP) compared to conventional dose distributions. However, these gains may vary considerably among patients due to (a) variations in the overall radiosensitivity of the tumor, (b) variations in the 3D distribution of intra‐tumor radiosensitivity within the tumor in combination with patient anatomy, (c) uncertainties of the 3D radiosensitivity maps, (d) geometrical uncertainties, and (e) temporal changes in radiosensitivity. The goal of this study was to investigate how much of the theoretical gains of DPBN remain when accounting for these factors. DPBN was compared to both a homogeneous reference dose distribution and to nonselective dose escalation (NSDE), that uses the same dose constraints as DPBN, but does not require 3D radiosensitivity maps.MethodsA fully automated DPBN treatment planning strategy was developed and implemented in our in‐house developed treatment planning system (TPS) that is robust to uncertainties in radiosensitivity and patient positioning. The method optimized the expected TCP based on 3D maps of intra‐tumor radiosensitivity, while accounting for normal tissue constraints, uncertainties in radiosensitivity, and setup uncertainties. Based on FDG‐PETCT scans of 12 non‐small cell lung cancer (NSCLC) patients, data of 324 virtual patients were created synthetically with large variations in the aforementioned parameters. DPBN was compared to both a uniform dose distribution of 60 Gy, and NSDE. In total, 360 DPBN and 24 NSDE treatment plans were optimized.ResultsThe average gain in TCP over all patients and radiosensitivity maps of DPBN was 0.54 ± 0.20 (range 0–0.97) compared to the 60 Gy uniform reference dose distribution, but only 0.03 ± 0.03 (range 0–0.22) compared to NSDE. The gains varied per patient depending on the radiosensitivity of the entire tumor and the 3D radiosensitivity maps. Uncertainty in radiosensitivity led to a considerable loss in TCP gain, which could be recovered almost completely by accounting for the uncertainty directly in the optimization.ConclusionsOur results suggest that the gains of DPBN can be considerable compared to a 60 Gy uniform reference dose distribution, but small compared to NSDE for most patients. Using the robust DPBN treatment planning system developed in this work, the optimal DPBN treatment plan could be derived for any patient for whom 3D intra‐tumor radiosensitivity maps are known, and can be used to select patients that might benefit from DPBN. NSDE could be an effective strategy to increase TCP without requiring biological information of the tumor.

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

Background and purposeRadiotherapy 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 methodsVolumetric-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.ResultsThe 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 R2 ≥ 0.9.ConclusionsMinimax 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.

Impact of different biologically-adapted radiotherapy strategies on tumor control evaluated with a tumor response model.

Motivated by the capabilities of modern radiotherapy techniques and by the recent developments of functional imaging techniques, dose painting by numbers (DPBN) was proposed to treat tumors with heterogeneous biological characteristics. This work studies different DPBN optimization techniques for virtual head and neck tumors assessing tumor response in terms of cell survival and tumor control probability with a previously published tumor response model (TRM). Uniform doses of 2 Gy are redistributed according to the microscopic oxygen distribution and the density distribution of tumor cells in four virtual tumors with different biological characteristics. In addition, two different optimization objective functions are investigated, which: i) minimize tumor cell survival (OFsurv) or; ii) maximize the homogeneity of the density of surviving tumor cells (OFstd). Several adaptive schemes, ranging from single to daily dose optimization, are studied and the treatment response is compared to that of the uniform dose. The results show that the benefit of DPBN treatments depends on the tumor reoxygenation capability, which strongly differed among the set of virtual tumors investigated. The difference between daily (fraction by fraction) and three weekly optimizations (at the beginning of weeks 1, 3 and 4) was found to be small, and higher benefit was observed for the treatments optimized using OFsurv. This in silico study corroborates the hypothesis that DPBN may be beneficial for treatments of tumors which show reoxygenation during treatment, and that a few optimizations may be sufficient to achieve this therapeutic benefit.

Late mucosal ulcers in dose-escalated adaptive dose-painting treatments for head-and-neck cancer

Background: To identify predictive factors for the development of late grade 4 mucosal ulcers in adaptive dose-escalated treatments for head-and-neck cancer.Material and methods: Patient data of four dose-escalated three-phase adaptive dose-painting by numbers (DPBN) clinical trials were analyzed in this study. Correlations between the development of late grade 4 ulcers and factors related with the treatment, disease characteristics and the patient were investigated. Dosimetrical thresholds were searched among the highest doses received by 1.75 cm3 (D1.75cc) of the primary gross tumor volume (GTVT) and the corresponding normalized isoeffective dose (NID21.75cc, with a reference dose of 2Gy/fraction and α/β of 3 Gy).Results: From 39 studied patients, nine developed late grade 4 mucosal ulcers. The continuation to either smoke or drink alcohol after therapy was the factor that showed a strong (eight out of nine patients) association with the occurrence of grade 4 ulcers. Six of the patients who continued to smoke or/and drink had D1.75cc and NID21.75cc above 84 Gy and 95.5 Gy, respectively. Seven of the patients with grade 4 had the dose levels above these thresholds, but even if the D1.75cc threshold was significant in the prediction of late grade 4 ulcers, it could not be considered as the only contributing factor.Conclusions: The search for patterns provided strong reasons to apply a dosimetrical threshold for the peak-dose volume of 1.75 cm3 as a preventive measure for late grade 4 mucosal ulcers. Also, patients that continue to smoke or drink alcohol after therapy have increased risk to develop late mucosal ulcers.

Repeatability of dose painting by numbers treatment planning in prostate cancer radiotherapy based on multiparametric magnetic resonance imaging

Dose painting by numbers (DPBN) refers to a voxel-wise prescription of radiation dose modelled from functional image characteristics, in contrast to dose painting by contours which requires delineations to define the target for dose escalation. The direct relation between functional imaging characteristics and DPBN implies that random variations in images may propagate into the dose distribution. The stability of MR-only prostate cancer treatment planning based on DPBN with respect to these variations is as yet unknown. We conducted a test–retest study to investigate the stability of DPBN for prostate cancer in a semi-automated MR-only treatment planning workflow. Twelve patients received a multiparametric MRI on two separate days prior to prostatectomy. The tumor probability (TP) within the prostate was derived from image features with a logistic regression model. Dose mapping functions were applied to acquire a DPBN prescription map that served to generate an intensity modulated radiation therapy (IMRT) treatment plan. Dose calculations were done on a pseudo-CT derived from the MRI. The TP and DPBN map and the IMRT dose distribution were compared between both MRI sessions, using the intraclass correlation coefficient (ICC) to quantify repeatability of the planning pipeline. The quality of each treatment plan was measured with a quality factor (QF). Median ICC values for the TP and DPBN map and the IMRT dose distribution were 0.82, 0.82 and 0.88, respectively, for linear dose mapping and 0.82, 0.84 and 0.94 for square root dose mapping. A median QF of 3.4% was found among all treatment plans. We demonstrated the stability of DPBN radiotherapy treatment planning in prostate cancer, with excellent overall repeatability and acceptable treatment plan quality. Using validated tumor probability modelling and simple dose mapping techniques it was shown that despite day-to-day variations in imaging data still consistent treatment plans were obtained.

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

PurposeTo develop a new optimization algorithm to carry out true dose painting by numbers (DPBN) planning based on full Monte Carlo (MC) calculation. MethodsFour 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. ResultsBeamlet 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. ConclusionsLP optimization at voxel level without dose-volume restrictions can carry out true DPBN planning with the MC accuracy.

Intensity modulated arc therapy implementation in a three phase adaptive (18)F-FDG-PET voxel intensity-based planning strategy for head-and-neck cancer.

BackgroundThis study investigates the implementation of a new intensity modulated arc therapy (IMAT) class solution in comparison to a 6-static beam step-and-shoot intensity modulated radiotherapy (s-IMRT) for three-phase adaptive 18F-FDG-PET-voxel-based dose-painting-by-numbers (DPBN) for head-and-neck cancer.MethodsWe developed 18F-FDG-PET-voxel intensity-based IMAT employing multiple arcs and compared it to clinically used s-IMRT DPBN. Three IMAT plans using 18F-FDG-PET/CT acquired before treatment (phase I), after 8 fractions (phase II) and CT acquired after 18 fractions (phase III) were generated for each of 10 patients treated with 3 s-IMRT plans based on the same image sets. Based on deformable image registration (ABAS, version 0.41, Elekta CMS Software, Maryland Heights, MO), doses of the 3 plans were summed on the pretreatment CT using validated in-house developed software. Dosimetric indices in targets and organs-at-risk (OARs), biologic conformity of treatment plans set at ≤5 %, treatment quality and efficiency were compared between IMAT and s-IMRT for the whole group and for individual patients.ResultsDoses to most organs-at-risk (OARs) were significantly better in IMAT plans, while target levels were similar for both types of plans. On average, IMAT ipsilateral and contralateral parotid mean doses were 14.0 % (p = 0.001) and 12.7 % (p < 0.001) lower, respectively. Pharyngeal constrictors D50% levels were similar or reduced with up to 54.9 % for IMAT compared to s-IMRT for individual patient cases. IMAT significantly improved biologic conformity by 2.1 % for treatment phases I and II. 3D phantom measurements reported an agreement of ≥95 % for 3 % and 3 mm criteria for both treatment modalities. IMAT delivery time was significantly shortened on average by 41.1 %.ConclusionsIMAT implementation significantly improved the biologic conformity as compared to s-IMRT in adaptive dose-escalated DPBN treatments. The better OAR sparing and faster delivery highly improved the treatment efficiency.Electronic supplementary materialThe online version of this article (doi:10.1186/s13014-016-0629-3) contains supplementary material, which is available to authorized users.

A.113 - Dose painting by numbers (DPBN) guided by diffusion-weighted MRI applied to recurrent glioblastoma (GBM): An experimental protocol of phase I/II

A.113 - Dose painting by numbers (DPBN) guided by diffusion-weighted MRI applied to recurrent glioblastoma (GBM): An experimental protocol of phase I/II

Dose painting by numbers in a standard treatment planning system using inverted dose prescription maps

ABSTRACTBackground. Dose painting by numbers (DPBN) is a method to deliver an inhomogeneous tumor dose voxel-by-voxel with a prescription based on biological medical images. However, planning of DPBN is not supported by commercial treatment planning systems (TPS) today. Here, a straightforward method for DPBN with a standard TPS is presented.Material and methods. DPBN tumor dose prescription maps were generated from 18F-FDG-PET images applying a linear relationship between image voxel value and dose. An inverted DPBN prescription map was created and imported into a standard TPS where it was defined as a mock pre-treated dose. Using inverse optimization for the summed dose, a planned DPBN dose distribution was created. The procedure was tested in standard TPS for three different tumor cases; cervix, lung and head and neck. The treatment plans were compared to the prescribed DPBN dose distribution by three-dimensional (3D) gamma analysis and quality factors (QFs). Delivery of the DPBN plans was assessed with portal dosimetry (PD).Results. Maximum tumor doses of 149%, 140% and 151% relative to the minimum tumor dose were prescribed for the cervix, lung and head and neck case, respectively. DPBN distributions were well achieved within the tumor whilst normal tissue doses were within constraints. Generally, high gamma pass rates (> 89% at 2%/2 mm) and low QFs (< 2.6%) were found. PD showed that all DPBN plans could be successfully delivered.Conclusions. The presented methodology enables the use of currently available TPSs for DPBN planning and delivery and may therefore pave the way for clinical implementation.

Methodology for adaptive and robust FDG-PET escalated dose painting by numbers in head and neck tumors

Objective. To develop a methodology for using FDG PET/CT in adaptive dose painting by numbers (DPBN) in head and neck squamous cell carcinoma (HNSCC) patients. Issues related to noise in PET and treatment robustness against geometric errors are addressed.Methods. Five patients with locally advanced HNSCC scheduled for chemo-radiotherapy were imaged with FDG-PET/CT at baseline and 2–3 times during radiotherapy (RT). The GTVPET was segmented with a gradient-based method. A double median filter reduces the impact of noise in the PET uptake-to-dose conversion. Filtered FDG uptake values were linearly converted into a voxel-by-voxel prescription from 70 (median uptake) to 86 Gy (highest uptake). A PTVPET was obtained by applying a dilation of 2.5 mm to the entire prescription. Seven iso-uptake thresholds led to seven sub-levels compatible with the Tomotherapy HiArt® Treatment Planning System. Planning aimed to deliver a median dose of 56 Gy and 70 Gy in 35 fractions on the elective and therapeutic PTVs, respectively. Plan quality was assessed with quality volume histogram (QVH). At each time point, plans were generated with a total of 3–4 plans for each patient. Deformable image registration was used for automatic contour propagation and dose summation of the 3 or 4 treatment plans (MIMvista®).Results. GTVPET segmentations were performed successfully until week 2 of RT but failed in two patients at week 3. QVH analysis showed high conformity for all plans (mean VQ = 0.95 93%; mean VQ = 1.05 3.9%; mean QF 2.2%). Good OAR sparing was achieved while keeping high plan quality.Conclusion. Our results show that adaptive FDG-PET-based escalated dose painting in patients with locally advanced HNSCC is feasible while respecting strict dose constraints to organs at risk. Clinical studies must be conducted to evaluate toxicities and tumor response of such a strategy.

Biological 18[F]-FDG-PET image-guided dose painting by numbers for painful uncomplicated bone metastases: A 3-arm randomized phase II trial

BackgroundAntalgic radiotherapy for bone metastases might be improved by implementing biological information in the radiotherapy planning using 18F-FDG-PET-CT based dose painting by numbers (DPBN). Materials and methodsPatients with uncomplicated painful bone metastases were randomized (1:1:1) and blinded to receive either 8Gy in a single fraction with conventionally planned radiotherapy (arm A) or 8Gy in a single fraction with DPBN (dose range between 610Gy and 10Gy) (arm B) or 16Gy in a single fraction with DPBN (dose range between 1410Gy and 18Gy) (arm C). The primary endpoint was overall pain response at 1month. The phase II trial was designed to select the experimental arm with sufficient promise of efficacy to continue to a phase III trial. ResultsForty-five patients were randomized. Eight (53%), 12 (80%) and 9 patients (60%) had an overall response to treatment in arm A, B and C, respectively. The estimated odds ratio of overall response for arm B vs. A is 3.5 (95% CI: 0.44–17.71, p=0.12). The estimated odds ratio of arm C vs. A is 1.31 (95% CI: 0.31–5.58, p=0.71). ConclusionA single fraction of 8Gy with DPBN will be further evaluated in a phase III-trial.

PO-0646: Adaptive and robust FDG-PET-based dose painting by numbers (DPBN) in head and neck tumors: a methodological approach

PO-0646: Adaptive and robust FDG-PET-based dose painting by numbers (DPBN) in head and neck tumors: a methodological approach

Feasibility and robustness of dose painting by numbers in proton therapy with contour-driven plan optimization.

To prove the ability of protons to reproduce a dose gradient that matches a dose painting by numbers (DPBN) prescription in the presence of setup and range errors, by using contours and structure-based optimization in a commercial treatment planning system. For two patients with head and neck cancer, voxel-by-voxel prescription to the target volume (GTVPET) was calculated from (18)FDG-PET images and approximated with several discrete prescription subcontours. Treatments were planned with proton pencil beam scanning. In order to determine the optimal plan parameters to approach the DPBN prescription, the effects of the scanning pattern, number of fields, number of subcontours, and use of range shifter were separately tested on each patient. Different constant scanning grids (i.e., spot spacing = Δx = Δy = 3.5, 4, and 5 mm) and uniform energy layer separation [4 and 5 mm WED (water equivalent distance)] were analyzed versus a dynamic and automatic selection of the spots grid. The number of subcontours was increased from 3 to 11 while the number of beams was set to 3, 5, or 7. Conventional PTV-based and robust clinical target volumes (CTV)-based optimization strategies were considered and their robustness against range and setup errors assessed. Because of the nonuniform prescription, ensuring robustness for coverage of GTVPET inevitably leads to overdosing, which was compared for both optimization schemes. The optimal number of subcontours ranged from 5 to 7 for both patients. All considered scanning grids achieved accurate dose painting (1% average difference between the prescribed and planned doses). PTV-based plans led to nonrobust target coverage while robust-optimized plans improved it considerably (differences between worst-case CTV dose and the clinical constraint was up to 3 Gy for PTV-based plans and did not exceed 1 Gy for robust CTV-based plans). Also, only 15% of the points in the GTVPET (worst case) were above 5% of DPBN prescription for robust-optimized plans, while they were more than 50% for PTV plans. Low dose to organs at risk (OARs) could be achieved for both PTV and robust-optimized plans. DPBN in proton therapy is feasible with the use of a sufficient number subcontours, automatically generated scanning patterns, and no more than three beams are needed. Robust optimization ensured the required target coverage and minimal overdosing, while PTV-approach led to nonrobust plans with excessive overdose. Low dose to OARs can be achieved even in the presence of a high-dose escalation as in DPBN.

Probabilistic evaluation of target dose deterioration in dose painting by numbers for stage II/III lung cancer

PurposeNon-small cell lung cancer is typically irradiated with 60-66 Gy in 2-Gy fractions. Local control could be improved by increasing dose to the more radiation-resistant areas (eg, based on the standardized uptake values of a pretreatment [18F]fluoro-deoxyglucose positron emission tomography scan). Such dose painting approaches, however, are poorly suited for a conventional planning target volume margin expansion; therefore, typically no margins are used. This study investigates dose deterioration of a dose painting by numbers (DPBN) approach resulting from geometrical uncertainties. Methods and materialsFor 9 DPBN plans of stage II/III non-small cell lung cancer patients, the boost dose was escalated up to 130 Gy (in 33 fractions) or until a dose-limiting constraint was reached. Then, using Monte Carlo methods, a probabilistic evaluation of dose endpoints for 99%, 98%, and 2% of gross tumor volume at a 90% confidence level was performed considering 8 different combinations of systematic (∑) and random (σ) geometric error distributions. ResultsImportant underdosages, because of geometric uncertainties, of up to 38 Gy with minimal image guidance occur, reducing to 8 Gy with the highest level of image guidance, for a patient where a maximum dose of 119 Gy could be achieved. The evaluation showed that systematic errors had the largest influence. The effects of the uncertainties are most evident where the dose or its gradient is high. ConclusionsProbabilistic evaluation showed that the geometric uncertainties have a large effect and should be evaluated before approving DPBN plans.

Dose Rate and Dose Painting

Tumor heterogeneity is one of the most important factors in tumor progression and recurrence after therapy. In this situation, delivery of a non-uniform dose would be optimum. Dose painting as a nonuniform dose distribution is a feasible strategy in radiation oncology. It requires imaging biomarkers to determine treatment sites which should receive higher doses. There are two main strategies for dose painting: by numbers (DPBN) and by contours (DPBC). In DPBC, tumour sub volumes receive a boosted dose, whilst DPBN is a voxel based issue and each voxel of tumour volume receives an individual dose prescription [1]. Based on molecular imaging data, dose painting involves four distinct steps including: “determination of the correlation between the underlying tumour biology and molecular imaging; determination of dose prescription function based on molecular imaging data; planning of the treatment and dose delivery; and assessment of the clinical outcomes in comparison with standard treatments” [2]. Molecular imaging (more PET) plays a rigorous role to find more accurate target volume, called biological target volume (BTV). Bentzen et al. mentioned there are three evidence based causes of treatment failure in radiation oncology including tumor burden, tumor cell proliferation, and hypoxia. They concluded that molecular imaging of those phenotypes using specific PET tracer can lead to find ideal painted dose distribution [3]. In the other hand, by introduction of cancer stem cells (CSCs) hypothesis and their highly radiation resistance, the mentioned triplet treatment failure (tumor burden, proliferation, and hypoxia) can be correlated to CSCs. Also, the main heterogeneity of tumors is due to CSCs theoretically. Multiple studies have shown that CSCs are highly radioresistance because they are hypoxic, have strong DNA repair and radical scavenging systems and they repopulate by a fast manner [4].