Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy.

BACKGROUND: Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations. PURPOSE: To compare daily CBCT-based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs in a scenario mimicking actual clinical practice. METHODS: Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D98 difference for target volumes and Dmean difference for organs-at-risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia. RESULTS: For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs. CONCLUSIONS: Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.

Proton arc therapy increases the benefit of proton therapy for oropharyngeal cancer patients in the model based clinic.

BACKGROUND AND PURPOSE: In the model-based approach, patients qualify for proton therapy when the reduction in risk of toxicity (ΔNTCP) obtained with IMPT relative to VMAT is larger than predefined thresholds as defined by the Dutch National Indication Protocol (NIPP). Proton arc therapy (PAT) is an emerging technology which has the potential to further decrease NTCPs compared to IMPT. The aim of this study was to investigate the potential impact of PAT on the number of oropharyngeal cancer (OPC) patients that qualify for proton therapy. MATERIALS AND METHODS: A prospective cohort of 223 OPC patients subjected to the model-based selection procedure was investigated. 33 (15%) patients were considered unsuitable for proton treatment before plan comparison. When IMPT was compared to VMAT for the remaining 190 patients, 148 (66%) patients qualified for protons and 42 (19%) patients did not. For these 42 patients treated with VMAT, robust PAT plans were generated. RESULTS: PAT plans provided better or similar target coverage compared to IMPT plans. In the PAT plans, integral dose was significantly reduced by 18% relative to IMPT plans and by 54% relative to VMAT plans. PAT decreased the mean dose to numerous organs-at-risk (OARs), further reducing NTCPs. The ΔNTCP for PAT relative to VMAT passed the NIPP thresholds for 32 out of the 42 patients treated with VMAT, resulting in 180 patients (81%) of the complete cohort qualifying for protons. CONCLUSION: PAT outperforms IMPT and VMAT, leading to a further reduction of NTCP-values and higher ΔNTCP-values, significantly increasing the percentage of OPC patients selected for proton therapy.

Evaluation of robustly optimised intensity modulated proton therapy for nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To evaluate the dosimetric changes occurring over the treatment course for nasopharyngeal carcinoma (NPC) patients treated with robustly optimised intensity modulated proton therapy (IMPT). MATERIALS AND METHODS: 25 NPC patients were treated to two dose levels (CTV1: 70 Gy, CTV2: 54.25 Gy) with robustly optimised IMPT plans. Robustness evaluation was performed over 28 error scenarios using voxel-wise minimum distributions to assess target coverage and voxel-wise maximum distributions to assess possible hotspots and critical organ doses. Daily CBCT was used for positioning and weekly repeat CTs (rCT) were taken, on which the plan dose was recalculated and robustly evaluated. Deformable image registration was used to warp and accumulate the nominal, voxel-wise minimum and maximum rCT dose distributions. Changes to target coverage, critical organ and normal tissue dose between the accumulated and planned doses were investigated. RESULTS: 2 patients required a plan adaptation due to reduced target coverage. The D98% in the accumulated voxel-wise minimum distribution was higher than planned for CTV1 in 24/25 patients and for CTV2 in 20/25 patients. Maximum doses to the critical organs remained acceptable in all patients. Other normal tissue doses showed some variation as a result of soft tissue deformations and weight change. Normal tissue complication probabilities for grade ≥ 2 dysphagia and grade ≥ 2 xerostomia remained similar to planned values. CONCLUSION: Robustly optimised IMPT plans, in combination with volumetric verification imaging and adaptive planning, provided robust target coverage and acceptable OAR dose variation in our NPC cohort when accumulated over longitudinal data.

A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy.

BACKGROUND AND PURPOSE: Accurate segmentation of organs-at-risk (OARs) is crucial but tedious and time-consuming in adaptive radiotherapy (ART). The purpose of this work was to automate head and neck OAR-segmentation on repeat CT (rCT) by an optimal combination of human and auto-segmentation for accurate prediction of Normal Tissue Complication Probability (NTCP). MATERIALS AND METHODS: Human segmentation (HS) of 3 observers, deformable image registration (DIR) based contour propagation and deep learning contouring (DLC) were carried out to segment 15 OARs on 15 rCTs. The original treatment plan was re-calculated on rCT to obtain mean dose (Dmean) and consequent NTCP-predictions. The average Dmean and NTCP-predictions of the three observers were referred to as the gold standard to calculate the absolute difference of Dmean and NTCP-predictions (|ΔDmean| and |ΔNTCP|). RESULTS: The average |ΔDmean| of parotid glands in HS was 1.40 Gy, lower than that obtained with DIR and DLC (3.64 Gy, p < 0.001 and 3.72 Gy, p < 0.001, respectively). DLC showed the highest |ΔDmean| in middle Pharyngeal Constrictor Muscle (PCM) (5.13 Gy, p = 0.01). DIR showed second highest |ΔDmean| in the cricopharyngeal inlet (2.85 Gy, p = 0.01). The semi auto-segmentation (SAS) adopted HS, DIR and DLC for segmentation of parotid glands, PCM and all other OARs, respectively. The 90th percentile |ΔNTCP|was 2.19%, 2.24%, 1.10% and 1.50% for DIR, DLC, HS and SAS respectively. CONCLUSIONS: Human segmentation of the parotid glands remains necessary for accurate interpretation of mean dose and NTCP during ART. Proposed semi auto-segmentation allows NTCP-predictions within 1.5% accuracy for 90% of the cases.

Development of advanced preselection tools to reduce redundant plan comparisons in model-based selection of head and neck cancer patients for proton therapy.

PURPOSE: In the Netherlands, head and neck cancer (HNC) patients are selected for proton therapy (PT) based on estimated normal tissue complication probability differences (ΔNTCP) between photons and protons, which requires a plan comparison (VMAT vs. IMPT). We aimed to develop tools to improve patient selection for plan comparisons. METHODS: This prospective study consisted of 141 consecutive patients in which a plan comparison was done. IMPT plans of patients not qualifying for PT were classified as 'redundant'. To prevent redundant IMPT planning, 5 methods that were primarily based on regression models were developed to predict IMPT Dmean to OARs, by using data from VMAT plans and volumetric data from delineated targets and OARs. Then, actual and predicted plan comparison outcomes were compared. The endpoint was being selected for proton therapy. RESULTS: Seventy out of 141 patients (49.6%) qualified for PT. Using the developed preselection tools, redundant IMPT planning could have been prevented in 49-68% of the remaining 71 patients not qualifying for PT (=specificity) when the sensitivity of all methods was fixed to 100%, i.e., no false negative cases (positive predictive value range: 57-68%, negative predictive value: 100%). CONCLUSION: The advanced preselection tools, which uses volume and VMAT dose data, prevented labour intensive creation of IMPT plans in up to 68% of non-qualifying patients for PT. No patients qualifying for PT would have been incorrectly denied a plan comparison. This method contributes significantly to a more cost-effective model-based selection of HNC patients for PT.

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.

Proton therapy for selected low grade glioma patients in the Netherlands.

Proton therapy offers an attractive alternative to conventional photon-based radiotherapy in low grade glioma patients, delivering radiotherapy with equivalent efficacy to the tumour with less radiation exposure to the brain. In the Netherlands, patients with favourable prognosis based on tumour and patient characteristics can be offered proton therapy. Radiation-induced neurocognitive function decline is a major concern in these long surviving patients. Although level 1 evidence of superior clinical outcome with proton therapy is lacking, the Dutch National Health Care Institute concluded that there is scientific evidence to assume that proton therapy can have clinical benefit by reducing radiation-induced brain damage. Based on this decision, proton therapy is standard insured care for selected low grade glioma patients. Patients with other intracranial tumours can also qualify for proton therapy, based on the same criteria. In this paper, the evidence and considerations that led to this decision are summarised. Additionally, the eligibility criteria for proton therapy and the steps taken to obtain high-quality data on treatment outcome are discussed.

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.

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.

A dosimetry technique for measuring kilovoltage cone-beam CT dose on a linear accelerator using radiotherapy equipment.

This work develops a technique for kilovoltage cone-beam CT (CBCT) dosimetry that incorporates both point dose and integral dose in the form of dose length product, and uses readily available radiotherapy equipment. The dose from imaging protocols for a range of imaging parameters and treatment sites was evaluated. Conventional CT dosimetry using 100 mm long pencil chambers has been shown to be inadequate for the large fields in CBCT and has been replaced in this work by a combination of point dose and integral dose. Absolute dose measurements were made with a small volume ion chamber at the central slice of a radiotherapy phantom. Beam profiles were measured using a linear diode array large enough to capture the entire imaging field. These profiles were normalized to absolute dose to form dose line integrals, which were then weighted with radial depth to form the DLPCBCT. This metric is analogous to the standard dose length product (DLP), but derived differently to suit the unique properties of CBCT. Imaging protocols for head and neck, chest, and prostate sites delivered absolute doses of 0.9, 2.2, and 2.9 cGy to the center of the phantom, and DLPCBCT of 28.2, 665.1, and 565.3mGy.cm, respectively. Results are displayed as dose per 100 mAs and as a function of key imaging parameters such as kVp, mAs, and collimator selection in a summary table. DLPCBCT was found to correlate closely with the dimension of the imaging region and provided a good indication of integral dose. It is important to assess integral dose when determining radiation doses to patients using CBCT. By incorporating measured beam profiles and DLP, this technique provides a CBCT dosimetry in radiotherapy phantoms and allows the prediction of imaging dose for new CBCT protocols.

Spun elliptically birefringent photonic crystal fibre.

Elliptically birefringent fibre has been fabricated by spinning the preform of a highly linearly birefringent photonic crystal fibre (PCF) during the drawing process. The resulting Spun Highly Birefringent (SHi-Bi) PCF offers intrinsic sensitivity to magnetic fields through the Faraday effect without the high inherent temperature sensitivities suffered by conventional spun stress birefringence fibres. The ellipticity of the birefringence has been measured and temperature independence has been demonstrated.