Multileaf collimator characterization and modeling for a 1.5 T MR-linac using static synchronous and asynchronous sweeping gaps.

Objective.The Elekta unity MR-linac delivers step-and-shoot intensity modulated radiotherapy plans using a multileaf collimator (MLC) based on the Agility MLC used on conventional Elekta linacs. Currently, details of the physical Unity MLC and the computational model within its treatment planning system (TPS)Monacoare lacking in published literature. Recently, a novel approach to characterize the physical properties of MLCs was introduced using dynamic synchronous and asynchronous sweeping gap (aSG) tests. Our objective was to develop a step-and-shoot version of the dynamic aSG test to characterize the Unity MLC and the computational MLC models in theMonacoandRayStationTPSs.Approach.Dynamic aSG were discretized into a step-and-shoot aSG by investigating the number of segments/sweep and the minimal number of monitor units (MU) per segment. The step-and-shoot aSG tests were compared to the dynamic aSG tests on a conventional linac at a source-to-detector distance of 143.5 cm, mimicking the Unity configuration. the step-and-shoot aSG tests were used to characterize the Unity MLC through measurements and dose calculations in both TPSs.Main results.The step-and-shoot aSGs tests with 100 segments and 5 MU/segment gave results very similar to the dynamic aSG experiments. The effective tongue-and-groove width of the Unity gradually increased up to 1.4 cm from the leaf tip end. The MLC models inRayStationandMonacoagreed with experimental data within 2.0% and 10%, respectively. The largest discrepancies inMonacowere found for aSG tests with >10 mm leaf interdigitation, which are non-typical for clinical plans.Significance.The step-and-shoot aSG tests accurately characterize the MLC in step-and-shoot delivery mode. The MLC model inRayStation2023B accurately describes the tongue-and-groove and leaf tip effects whereasMonacooverestimates the tongue-and-groove shadowing further away from the leaf tip end.

Universal evaluation of MLC models in treatment planning systems based on a common set of dynamic tests.

PURPOSE: To demonstrate the feasibility of characterising MLCs and MLC models implemented in TPSs using a common set of dynamic beams. MATERIALS AND METHODS: A set of tests containing synchronous (SG) and asynchronous sweeping gaps (aSG) was distributed among twenty-five participating centres. Doses were measured with a Farmer-type ion chamber and computed in TPSs, which provided a dosimetric characterisation of the leaf tip, tongue-and-groove, and MLC transmission of each MLC, as well as an assessment of the MLC model in each TPS. Five MLC types and four TPSs were evaluated, covering the most frequent combinations used in radiotherapy departments. RESULTS: Measured differences within each MLC type were minimal, while large differences were found between MLC models implemented in clinical TPSs. This resulted in some concerning discrepancies, especially for the HD120 and Agility MLCs, for which differences between measured and calculated doses for some MLC-TPS combinations exceeded 10%. These large differences were particularly evident for small gap sizes (5 and 10 mm), as well as for larger gaps in the presence of tongue-and-groove effects. A much better agreement was found for the Millennium120 and Halcyon MLCs, differences being within ± 5% and ± 2.5%, respectively. CONCLUSIONS: The feasibility of using a common set of tests to assess MLC models in TPSs was demonstrated. Measurements within MLC types were very similar, but TPS dose calculations showed large variations. Standardisation of the MLC configuration in TPSs is necessary. The proposed procedure can be readily applied in radiotherapy departments and can be a valuable tool in IMRT and credentialing audits.

Automated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using Machine Learning.

PURPOSE: The aim of this study was to evaluate an automated treatment planning method for robustly optimized intensity modulated proton therapy (IMPT) plans for oropharyngeal carcinoma patients and to compare the results with manually optimized robust IMPT plans. METHODS AND MATERIALS: An atlas regression forest-based machine learning (ML) model for dose prediction was trained on CT scans, contours, and dose distributions of robust IMPT plans of 88 oropharyngeal cancer (OPC) patients. The ML model was combined with a robust voxel and dose volume histogram-based dose mimicking optimization algorithm for 21 perturbed scenarios to generate a machine-deliverable plan from the predicted dose distribution. Machine learning optimization (MLO) configuration was performed using a cross-validation approach with 3 × 8 tuning patients and comprised of adjustments to the mimicking optimization, to generate higher-quality MLO plans. Independent testing of the MLO algorithm was performed with another 25 patients. Plan quality of clinical and MLO plans was evaluated by the clinical target volume (D98% voxel-wise minimum dose >94%), organ at risk (OAR) doses, and the normal tissue complication probability (NTCP) (sum (Σ) of grade-2 and grade-3 dysphagia and xerostomia). RESULTS: Adequate robust target coverage was achieved in 24/25 clinical plans and in 23/25 MLO plans in the primary clinical target volume (CTV). In the elective CTV, 22/25 clinical plans and 24/25 MLO plans passed the robust target coverage evaluation threshold. The MLO average Σgrade 2 and Σgrade 3 NTCPs were comparable to the clinical plans (Σgrade 2 NTCPs: clinical 47.5% vs MLO 48.4%, Σgrade 3 NTCPs: clinical 11.9% vs MLO 12.3%). Significant increases in OAR average doses in MLO plans were found in the pharynx constrictor muscles (163 cGy, P = .002) and cervical esophagus (265 cGy, P = .002). The MLO plans were created within 45 minutes. CONCLUSION: This study showed that automated MLO planning can generate robustly optimized MLO plans with quality comparable to clinical plans in OPC patients.

Parotid Gland Stem Cell Sparing Radiation Therapy for Patients With Head and Neck Cancer: A Double-Blind Randomized Controlled Trial.

PURPOSE: Radiation therapy for head and neck cancer frequently leads to salivary gland damage and subsequent xerostomia. The radiation response of the parotid glands of rats, mice, and patients critically depends on dose to parotid gland stem cells, mainly located in the gland's main ducts (stem cell rich [SCR] region). Therefore, this double-blind randomized controlled trial aimed to test the hypothesis that parotid gland stem cell sparing radiation therapy preserves parotid gland function better than currently used whole parotid gland sparing radiation therapy. METHODS AND MATERIALS: Patients with head and neck cancer (n = 102) treated with definitive radiation therapy were randomized between standard parotid-sparing and stem cell sparing (SCS) techniques. The primary endpoint was >75% reduction in parotid gland saliva production compared with pretreatment production (FLOW12M). Secondary endpoints were several aspects of xerostomia 12 months after treatment. RESULTS: Fifty-four patients were assigned to the standard arm and 48 to the SCS arm. Only dose to the SCR regions (contralateral 16 and 11 Gy [P = .004] and ipsilateral 26 and 16 Gy [P = .001] in the standard and SCS arm, respectively) and pretreatment patient-rated daytime xerostomia (35% and 13% [P = .01] in the standard and SCS arm, respectively) differed significantly between the arms. In the SCS arm, 1 patient (2.8%) experienced FLOW12M compared with 2 (4.9%) in the standard arm (P = 1.00). However, a trend toward better relative parotid gland salivary function in favor of SCS radiation therapy was shown. Moreover, multivariable analysis showed that mean contralateral SCR region dose was the strongest dosimetric predictor for moderate-to-severe patient-rated daytime xerostomia and grade ≥2 physician-rated xerostomia, the latter including reported alteration in diet. CONCLUSIONS: No significantly better parotid function was observed in SCS radiation therapy. However, additional multivariable analysis showed that dose to the SCR region was more predictive of the development of parotid gland function-related xerostomia endpoints than dose to the entire parotid gland.

Validation of separate multi-atlases for auto segmentation of cardiac substructures in CT-scans acquired in deep inspiration breath hold and free breathing.

BACKGROUND AND PURPOSE: Developing NTCP-models for cardiac complications after breast cancer (BC) radiotherapy requires cardiac dose-volume parameters for many patients. These can be obtained by using multi-atlas based automatic segmentation (MABAS) of cardiac structures in planning CT scans. We investigated the relevance of separate multi-atlases for deep inspiration breath hold (DIBH) and free breathing (FB) CT scans. MATERIALS AND METHODS: BC patients scanned in DIBH (n = 10) and in FB (n = 20) were selected to create separate multi-atlases consisting of expert panel delineations of the whole heart, atria and ventricles. The accuracy of atlas-generated contours was validated with expert delineations in independent datasets (n = 10 for DIBH and FB) and reported as Dice coefficients, contour distances and dose-volume differences in relation to interobserver variability of manual contours. Dependency of MABAS contouring accuracy on breathing technique was assessed by validation of a FB atlas in DIBH patients and vice versa (cross-validation). RESULTS: For all structures the FB and DIBH atlases resulted in Dice coefficients with their respective reference contours ≥ 0.8 and average contour distances ≤ 2 mm smaller than slice thickness of (CTs). No significant differences were found for dose-volume parameters in volumes receiving relevant dose levels (WH, LV and RV). Accuracy of the DIBH atlas was at least similar to, and for the ventricles better than, the interobserver variation in manual delineation. Cross-validation between breathing techniques showed a reduced MABAS performance. CONCLUSION: Multi-atlas accuracy was at least similar to interobserver delineation variation. Separate atlases for scans made in DIBH and FB could benefit atlas performance because accuracy depends on breathing technique.

Head and neck IMPT probabilistic dose accumulation: Feasibility of a 2 mm setup uncertainty setting.

OBJECTIVE: To establish optimal robust optimization uncertainty settings for clinical head and neck cancer (HNC) patients undergoing 3D image-guided pencil beam scanning (PBS) proton therapy. METHODS: We analyzed ten consecutive HNC patients treated with 70 and 54.25 GyRBE to the primary and prophylactic clinical target volumes (CTV) respectively using intensity-modulated proton therapy (IMPT). Clinical plans were generated using robust optimization with 5 mm/3% setup/range uncertainties (RayStation v6.1). Additional plans were created for 4, 3, 2 and 1 mm setup and 3% range uncertainty and for 3 mm setup and 3%, 2% and 1% range uncertainty. Systematic and random error distributions were determined for setup and range uncertainties based on our quality assurance program. From these, 25 treatment scenarios were sampled for each plan, each consisting of a systematic setup and range error and daily random setup errors. Fraction doses were calculated on the weekly verification CT closest to the date of treatment as this was considered representative of the daily patient anatomy. RESULTS: Plans with a 2 mm/3% setup/range uncertainty setting adequately covered the primary and prophylactic CTV (V95 ≥ 99% in 98.8% and 90.8% of the treatment scenarios respectively). The average organ-at-risk dose decreased with 1.1 GyRBE/mm setup uncertainty reduction and 0.5 GyRBE/1% range uncertainty reduction. Normal tissue complication probabilities decreased by 2.0%/mm setup uncertainty reduction and by 0.9%/1% range uncertainty reduction. CONCLUSION: The results of this study indicate that margin reduction below 3 mm/3% is possible but requires a larger cohort to substantiate clinical introduction.

First experience with model-based selection of head and neck cancer patients for proton therapy.

PURPOSE: In the Netherlands, head and neck cancer (HNC) patients qualify for intensity modulated proton therapy (IMPT) based on model-based selection (MBS). The aim of this study was to evaluate the first experience in MBS of HNC patients. METHODS: Patients who were subjected to MBS (Jan 2018-Sep 2019) were evaluated. A VMAT plan was created for all patients with optimal sparing of organ at risks (OARs) in normal tissue complication probability (NTCP) models for a number of toxicities. An IMPT plan was created only for those with NTCP difference (ΔNTCP) between VMAT and best-case scenario for proton (assuming 0 Gy dose for all OARs in IMPT plan) that exceeded any ΔNTCP-thresholds defined in Dutch National Indication Protocol. These patients qualified for a robust IMPT-plan creation with similar target doses and subsequent plan comparison. RESULTS: Of 227 patients, 141 (62%) qualified for plan comparison, of which 80 (35%) were eventually selected for proton therapy. Most patients were selected based on the ΔNTCP for dysphagia-related toxicities. The selection rate was higher among patients with advanced disease, pharyngeal tumors, and/or baseline complaints. A significant reduction in all OAR doses and NTCP values was obtained with IMPT compared with VMAT in both selected and non-selected patients, but more pronounced in patients selected for protons. CONCLUSION: Model-based selection of patients with HNC for proton therapy is clinically feasible. Approximately one third of HNC patients qualify for protons and these patients have the highest probability to benefit from protons in terms of toxicity prevention.

Practical robustness evaluation in radiotherapy - A photon and proton-proof alternative to PTV-based plan evaluation.

BACKGROUND AND PURPOSE: A planning target volume (PTV) in photon treatments aims to ensure that the clinical target volume (CTV) receives adequate dose despite treatment uncertainties. The underlying static dose cloud approximation (the assumption that the dose distribution is invariant to errors) is problematic in intensity modulated proton treatments where range errors should be taken into account as well. The purpose of this work is to introduce a robustness evaluation method that is applicable to photon and proton treatments and is consistent with (historic) PTV-based treatment plan evaluations. MATERIALS AND METHODS: The limitation of the static dose cloud approximation was solved in a multi-scenario simulation by explicitly calculating doses for various treatment scenarios that describe possible errors in the treatment course. Setup errors were the same as the CTV-PTV margin and the underlying theory of 3D probability density distributions was extended to 4D to include range errors, maintaining a 90% confidence level. Scenario dose distributions were reduced to voxel-wise minimum and maximum dose distributions; the first to evaluate CTV coverage and the second for hot spots. Acceptance criteria for CTV D98 and D2 were calibrated against PTV-based criteria from historic photon treatment plans. RESULTS: CTV D98 in worst case scenario dose and voxel-wise minimum dose showed a very strong correlation with scenario average D98 (R2 > 0.99). The voxel-wise minimum dose visualised CTV dose conformity and coverage in 3D in agreement with PTV-based evaluation in photon therapy. Criteria for CTV D98 and D2 of the voxel-wise minimum and maximum dose showed very strong correlations to PTV D98 and D2 (R2 > 0.99) and on average needed corrections of -0.9% and +2.3%, respectively. CONCLUSIONS: A practical approach to robustness evaluation was provided and clinically implemented for PTV-less photon and proton treatment planning, consistent with PTV evaluations but without its static dose cloud approximation.

Composite minimax robust optimization of VMAT improves target coverage and reduces non-target dose in head and neck cancer patients.

BACKGROUND AND PURPOSE: To assess the potential of composite minimax robust optimization (CMRO) compared to planning target volume (PTV)-based optimization for head and neck cancer (HNC) patients treated with volumetric modulated arc therapy (VMAT). MATERIALS AND METHODS: Ten HNC patients previously treated with a PTV-based VMAT plan were studied. In addition to the PTV-plan a VMAT plan was created with CMRO. For both plans an adapted planning strategy was also investigated, including a plan adaptation during the third week of treatment. The PTV-plans and CMRO-plans (adapted and non-adapted) were evaluated by means of the estimated actually given dose (EAGD). Therefore, the dose was calculated on daily acquired CBCTs, mapped onto the planning CT and accumulated. The plans were compared by dosimetric parameters and normal tissue complication probabilities (NTCPs) for tube feeding dependence, grade 2-4 dysphagia and xerostomia. The accuracy of CBCT-based dose accumulation was further quantified by comparisons of dose accumulation on weekly verification CTs. RESULTS: On average, CMRO significantly increased (1.5 Gy) the D98% of the EAGD to the clinical target volume and significantly decreased the mean dose of the ipsilateral parotid (2.8 Gy), inferior pharynx constrictor muscle (0.7 Gy) and the oral cavity (0.8 Gy). This translated into significantly reduced NTCP of tube feeding dependence (0.9%) and xerostomia (2.8%). The differences in EAGD derived from evaluation CTs or CBCTs were minimal. CONCLUSION: Minimax robust optimization led to improved target coverage and dose reduction in organs at risk in HNC patients treated with VMAT.

Cardiac Function After Radiation Therapy for Breast Cancer.

PURPOSE: The main purpose of this study was to test the hypothesis that incidental cardiac irradiation is associated with changes in cardiac function in breast cancer (BC) survivors treated with radiation therapy (RT). METHODS AND MATERIALS: We conducted a cross-sectional study consisting of 109 BC survivors treated with RT between 2005 and 2011. The endpoint was cardiac function, assessed by echocardiography. Systolic function was assessed with the left ventricular ejection fraction (LVEF) (n = 107) and the global longitudinal strain (GLS) of the left ventricle (LV) (n = 52). LV diastolic dysfunction (n = 109) was defined by e' at the lateral and septal region, which represents the relaxation velocity of the myocardium. The individual calculated RT dose parameters of the LV and coronary arteries were collected from 3-dimensional computed tomography-based planning data. Univariable and multivariable analysis using forward selection was performed to identify the best predictors of cardiac function. Robustness of selection was assessed using bootstrapping. The resulting multivariable linear regression model was presented for the endpoints of systolic and diastolic function. RESULTS: The median time between BC diagnosis and echocardiography was 7 years. No relation between RT dose parameters and LVEF was found. In the multivariable analysis for the endpoint GLS of the LV, the maximum dose to the left main coronary artery was most often selected across bootstrap samples. For decreased diastolic function, the most often selected model across bootstrap samples included age at time of BC diagnosis and hypertension at baseline. Cardiac dose-volume histogram parameters were less frequently selected for this endpoint. CONCLUSIONS: This study shows an association between individual cardiac dose distributions and GLS of the LV after RT for BC. No relation between RT dose parameters and LVEF was found. Diastolic function was most associated with age and hypertension at time of BC diagnosis. Further research is needed to make definitive conclusions.

Automated Robust Proton Planning Using Dose-Volume Histogram-Based Mimicking of the Photon Reference Dose and Reducing Organ at Risk Dose Optimization.

PURPOSE: Patient selection for proton therapy is increasingly based on proton to photon plan comparisons. To improve efficient decision making, we developed a dose mimicking and reducing (DMR) algorithm to automatically generate a robust proton plan from a reference photon dose, as well as target and organ at risk (OAR) delineations. METHODS AND MATERIALS: The DMR algorithm was evaluated in 40 patients with head and neck cancer. The first step of the DMR algorithm comprises dose-volume histogram-based mimicking of the photon dose distribution in the clinical target volumes and OARs. Target robustness is included by mimicking the nominal photon dose in 21 perturbed scenarios. The second step of the optimization aims to reduce the OAR doses while retaining the robust target coverage as achieved in the first step. We evaluated each DMR plan against the manually robustly optimized reference proton plan in terms of plan robustness (voxel-wise minimum dose). Furthermore, the DMR plans were evaluated against the reference photon plan using normal tissue complication probability (NTCP) models of xerostomia, dysphagia, and tube feeding dependence. Consequently, ΔNTCPs were defined as the difference between the NTCPs of the photon and proton plans. RESULTS: The dose distributions of the DMR and reference proton plans were very similar in terms of target robustness and OAR dose values. Regardless of proton planning technique (ie, DMR or reference proton plan), the same treatment modality was selected in 80% (32 of 40) of cases based on the ∑ΔNTCPs. In 15% (6 of 40) of cases, a conflicting decision was made based on relatively small dose differences to the OARs (<2.0 Gy). CONCLUSIONS: The DMR algorithm automatically optimized robust proton plans from a photon reference dose that were comparable to the dosimetrist-optimized proton plans in patients with head and neck cancer. This algorithm has been successfully embedded into a framework to automatically select patients for proton therapy based on NTCPs.

External validation of a multifactorial normal tissue complication probability model for tube feeding dependence at 6 months after definitive radiotherapy for head and neck cancer.

BACKGROUND AND PURPOSE: The purpose of this study was to externally validate a previously published normal tissue complication probability (NTCP) model for tube feeding dependence at 6 months (TUBEM6) after completion of (chemo) radiotherapy. MATERIALS AND METHODS: This study evaluated 122 head and neck cancer patients treated by definitive (chemo) radiotherapy. The closed testing procedure was used to select the appropriate method for updating the NTCP model. In this procedure, the likelihood ratio test was used to compare the updated model against the original model. RESULTS: Mean predicted NTCP was 12.2% (95% CI: 9.9%-14.5%) when using the original NTCP model for TUBEM6. TUBEM6 at our institute was 5.7% (95% CI: 1.8-9.6%) for the 122 patients evaluated. The test for the model revision against the original NTCP model was statistically significant (p = 0.032). The test for the model revision against the model adjusting intercept only was not statistically significant (p = 0.240). According to the closed testing procedure, the model required adjusting the intercept only. CONCLUSIONS: TUBEM6 at our institute was lower than that predicted by the original NTCP model. The closed testing procedure indicated that only an adjustment of the intercept was needed indicating the importance of external validation.

Reproducibility of the lung anatomy under active breathing coordinator control: Dosimetric consequences for scanned proton treatments.

PURPOSE: The treatment of moving targets with scanned proton beams is challenging. For motion mitigation, an Active Breathing Coordinator (ABC) can be used to assist breath-holding. The delivery of pencil beam scanning fields often exceeds feasible breath-hold durations, requiring high breath-hold reproducibility. We evaluated the robustness of scanned proton therapy against anatomical uncertainties when treating nonsmall-cell lung cancer (NSCLC) patients during ABC controlled breath-hold. METHODS: Four subsequent MRIs of five healthy volunteers (3 male, 2 female, age: 25-58, BMI: 19-29) were acquired under ABC controlled breath-hold during two simulated treatment fractions, providing both intrafractional and interfractional information about breath-hold reproducibility. Deformation vector fields between these MRIs were used to deform CTs of five NSCLC patients. Per patient, four or five cases with different tumor locations were modeled, simulating a total of 23 NSCLC patients. Robustly optimized (3 and 5 mm setup uncertainty respectively and 3% density perturbation) intensity-modulated proton plans (IMPT) were created and split into subplans of 20 s duration (assumed breath-hold duration). A fully fractionated treatment was recalculated on the deformed CTs. For each treatment fraction the deformed CTs representing multiple breath-hold geometries were alternated to simulate repeated ABC breath-holding during irradiation. Also a worst-case scenario was simulated by recalculating the complete treatment plan on the deformed CT scan showing the largest deviation with the first deformed CT scan, introducing a systematic error. Both the fractionated breath-hold scenario and worst-case scenario were dosimetrically evaluated. RESULTS: Looking at the deformation vector fields between the MRIs of the volunteers, up to 8 mm median intra- and interfraction displacements (without outliers) were found for all lung segments. The dosimetric evaluation showed a median difference in D98% between the planned and breath-hold scenarios of -0.1 Gy (range: -4.1 Gy to 2.0 Gy). D98% target coverage was more than 57.0 Gy for 22/23 cases. The D1 cc of the CTV increased for 21/23 simulations, with a median difference of 0.9 Gy (range: -0.3 to 4.6 Gy). For 14/23 simulations the increment was beyond the allowed maximum dose of 63.0 Gy, though remained under 66.0 Gy (110% of the prescribed dose of 60.0 Gy). Organs at risk doses differed little compared to the planned doses (difference in mean doses <0.9 Gy for the heart and lungs, <1.4% difference in V35 [%] and V20 [%] to the esophagus and lung). CONCLUSIONS: When treating under ABC controlled breath-hold, robustly optimized IMPT plans show limited dosimetric consequences due to anatomical variations between repeated ABC breath-holds for most cases. Thus, the combination of robustly optimized IMPT plans and the delivery under ABC controlled breath-hold presents a safe approach for PBS lung treatments.

Multivariable normal tissue complication probability model-based treatment plan optimization for grade 2-4 dysphagia and tube feeding dependence in head and neck radiotherapy.

BACKGROUND AND PURPOSE: Radiotherapy of the head and neck is challenged by the relatively large number of organs-at-risk close to the tumor. Biologically-oriented objective functions (OF) could optimally distribute the dose among the organs-at-risk. We aimed to explore OFs based on multivariable normal tissue complication probability (NTCP) models for grade 2-4 dysphagia (DYS) and tube feeding dependence (TFD). MATERIALS AND METHODS: One hundred head and neck cancer patients were studied. Additional to the clinical plan, two more plans (an OFDYS and OFTFD-plan) were optimized per patient. The NTCP models included up to four dose-volume parameters and other non-dosimetric factors. A fully automatic plan optimization framework was used to optimize the OFNTCP-based plans. RESULTS: All OFNTCP-based plans were reviewed and classified as clinically acceptable. On average, the Δdose and ΔNTCP were small comparing the OFDYS-plan, OFTFD-plan, and clinical plan. For 5% of patients NTCPTFD reduced >5% using OFTFD-based planning compared to the OFDYS-plans. CONCLUSIONS: Plan optimization using NTCPDYS- and NTCPTFD-based objective functions resulted in clinically acceptable plans. For patients with considerable risk factors of TFD, the OFTFD steered the optimizer to dose distributions which directly led to slightly lower predicted NTCPTFD values as compared to the other studied plans.

Selection of head and neck cancer patients for adaptive radiotherapy to decrease xerostomia.

BACKGROUND AND PURPOSE: The aim of this study was to develop and validate a method to select head and neck cancer patients for adaptive radiotherapy (ART) pre-treatment. Potential pre-treatment selection criteria presented in recent literature were included in the analysis. MATERIALS AND METHODS: Deviations from the planned parotid gland mean dose (PG ΔDmean) were estimated for 113 head and neck cancer patients by re-calculating plans on repeat CT scans. Uni- and multivariable linear regression analyses were performed to select pre-treatment parameters, and ROC curve analysis was used to determine cut off values, for selecting patients with a PG dose deviation larger than 3Gy. The patient selection method was validated in a second patient cohort of 43 patients. RESULTS: After multivariable analysis, the planned PG Dmean remained the only significant parameter for PG ΔDmean. A sensitivity of 91% and 80% could be obtained using a threshold of PG Dmean of 22.2Gy, for the development and validation cohorts, respectively. This would spare 38% (development cohort) and 24% (validation cohort) of patients from the labour-intensive ART procedure. CONCLUSIONS: The presented method to select patients for ART pre-treatment reduces the labour of ART, contributing to a more effective allocation of the department resources.

Sparing the region of the salivary gland containing stem cells preserves saliva production after radiotherapy for head and neck cancer.

Each year, 500,000 patients are treated with radiotherapy for head and neck cancer, resulting in relatively high survival rates. However, in 40% of patients, quality of life is severely compromised because of radiation-induced impairment of salivary gland function and consequent xerostomia (dry mouth). New radiation treatment technologies enable sparing of parts of the salivary glands. We have determined the parts of the major salivary gland, the parotid gland, that need to be spared to ensure that the gland continues to produce saliva after irradiation treatment. In mice, rats, and humans, we showed that stem and progenitor cells reside in the region of the parotid gland containing the major ducts. We demonstrated in rats that inclusion of the ducts in the radiation field led to loss of regenerative capacity, resulting in long-term gland dysfunction with reduced saliva production. Then we showed in a cohort of patients with head and neck cancer that the radiation dose to the region of the salivary gland containing the stem/progenitor cells predicted the function of the salivary glands one year after radiotherapy. Finally, we showed that this region of the salivary gland could be spared during radiotherapy, thus reducing the risk of post-radiotherapy xerostomia.

Multicriteria optimization enables less experienced planners to efficiently produce high quality treatment plans in head and neck cancer radiotherapy.

OBJECTIVES: To demonstrate that novice dosimetry planners efficiently create clinically acceptable IMRT plans for head and neck cancer (HNC) patients using a commercially available multicriteria optimization (MCO) system. METHODS: Twenty HNC patients were enrolled in this in-silico comparative planning study. Per patient, novice planners with less experience in dosimetry planning created an IMRT plan using an MCO system (RayStation). Furthermore, a conventionally planned clinical IMRT plan was available (Pinnacle(3)). All conventional IMRT and MCO-plans were blind-rated by two expert radiation-oncologists in HNC, using a 5-point scale (1-5 with 5 the highest score) assessment form comprising 10 questions. Additionally, plan quality was reported in terms of planning time, dosimetric and normal tissue complication probability (NTCP) comparisons. Inter-rater reliability was derived using the intra-class correlation coefficient (ICC). RESULTS: In total, the radiation-oncologists rated 800 items on plan quality. The overall plan score indicated no differences between both planning techniques (conventional IMRT: 3.8 ± 1.2 vs. MCO: 3.6 ± 1.1, p = 0.29). The inter-rater reliability of all ratings was 0.65 (95% CI: 0.57-0.71), indicating substantial agreement between the radiation-oncologists. In 93% of cases, the scoring difference of the conventional IMRT and MCO-plans was one point or less. Furthermore, MCO-plans led to slightly higher dose uniformity in the therapeutic planning target volume, to a lower integral body dose (13.9 ± 4.5 Gy vs. 12.9 ± 4.0 Gy, p < 0.001), and to reduced dose to the contra-lateral parotid gland (28.1 ± 11.8 Gy vs. 23.0 ± 11.2 Gy, p < 0.002). Consequently, NTCP estimates for xerostomia reduced by 8.4 ± 7.4% (p < 0.003). The hands-on time of the conventional IMRT planning was approximately 205 min. The time to create an MCO-plan was on average 43 ± 12 min. CONCLUSIONS: MCO planning enables novice treatment planners to create high quality IMRT plans for HNC patients. Plans were created with vastly reduced planning times, requiring less resources and a short learning curve.