Robust Treatment Plans Research Articles

2311: Treatment plan robustness in IMPT for esophageal cancer: every millimeter counts.

2311: Treatment plan robustness in IMPT for esophageal cancer: every millimeter counts.

2210: Assessing the treatment plan robustness of dynamic trajectory radiotherapy for head and neck cancer

2210: Assessing the treatment plan robustness of dynamic trajectory radiotherapy for head and neck cancer

Balancing robustness and adaptation rate for proton therapy of lung cancer patients

IntroductionAn increase in plan robustness leads to a higher dose to organs-at-risk (OARs), and an increased chance of post-treatment toxicities. In contrast, more conformal plans lead to sparing of healthy surrounding tissue at the expense of a higher sensitivity to anatomical changes, requiring costly adaptations. In this study, we assess the trade-off and impact of treatment plan robustness on the adaptation rate. MethodTreatment planning was performed for 40 lung cancer patients, each having a planning 4DCT and up to eight weekly repeated 4DCTs (reCTs). For each patient, plans were made with three different levels of robustness based on setup uncertainty of 3, 6 and 9 mm. These plans were robustly re-evaluated on all reCTs to assess whether the clinical constraints were met. ResultsFor the 3, 6 and 9 mm robustness levels, adaptation rates of 87.5 %, 70.0 % and 57.5 %, respectively, were observed. A mean absolute normal tissue complication probability (NTCP) gain of 2.9 percentage points (pp) was calculated for pneumonitis grade ≥ 2 when transitioning from 9 mm plans to 3 mm plans, 7.6 pp for esophagitis grade ≥ 2, and 2.5 pp for mortality risk 2 years post-treatment. ConclusionThe lowered risk of post treatment toxicities at lower robustness levels is clinically relevant but comes at the expense of more treatment adaptations, particularly in cases where meeting our clinical goals is not compromised by having a dose that is more conformal to the target. The trade-off between workload and reduced NTCP needs to be individually assessed.

512 Length of Stay Prediction Based on an Artificial Neural Network

Abstract Introduction Hospital length of stay (LOS) is difficult to predict in patients with burn injuries due to the complexity of the injuries and the wide variability in patient outcomes. Predicting hospital LOS in patients with burn injuries can have numerous benefits for patients, physicians, and insurance companies. Artificial neural networks may offer insight into more accurate LOS predictions. Methods We obtained electronic health records (EHR) for burn patients from July 01, 2011 - July 01, 2021. An artificial neural network was used to model the relationship between the input features and LOS. The input features were preprocessed by normalizing each feature to the range [0, 1] and transformed using log normal. The preprocessed data was split into training (70%), validation (15%), and testing (15%) sets. The neural network consisted of one hidden layer with 13 neurons and a linear output layer. Results The neural network achieved an R-squared (R2) value of 0.72 on the training data, indicating that 72% of the variance in the data (n=1347). The validation and testing R2 values were 0.66 and 0.68, respectively. The overall R2 value, computed by combining the predictions on the training, validation, and testing data, was 0.70. The best performance, validation performance, and testing performance achieved by the neural network during the training process were 0.28, 0.27, and 0.30, respectively. These values indicate the average squared difference between the predicted output and the true output for each set, with lower values indicating better performance. Conclusions The results suggest that the neural network can model the relationship between the input features and LOS with reasonable accuracy. The R2 values indicate that the model explains a substantial amount of the variability in the data. Further testing and validation may be needed to assess the robustness and reliability of the model. Applicability of Research to Practice Patient outcomes are largely variable and determining LOS to a closer degree helps planning more robust treatment plans. Additionally, these models have the potential to serve as a more accurate gauge of whether they are following closely to their expected treatment timeline. As survival rates continue to improve, the improvement focus has and will continue to shift towards patient outcomes.

Robustness analysis of dynamic trajectory radiotherapy and volumetric modulated arc therapy plans for head and neck cancer

Background and purposeDynamic trajectory radiotherapy (DTRT) has been shown to improve healthy tissue sparing compared to volumetric arc therapy (VMAT). This study aimed to assess and compare the robustness of DTRT and VMAT treatment-plans for head and neck (H&N) cancer to patient-setup (PS) and machine-positioning uncertainties. Materials and methodsThe robustness of DTRT and VMAT plans previously created for 46 H&N cases, prescribed 50–70 Gy to 95 % of the planning-target-volume, was assessed. For this purpose, dose distributions were recalculated using Monte Carlo, including uncertainties in PS (translation and rotation) and machine-positioning (gantry-, table-, collimator-rotation and multi-leaf collimator (MLC)). Plan robustness was evaluated by the uncertainties’ impact on normal tissue complication probabilities (NTCP) for xerostomia and dysphagia and on dose-volume endpoints. Differences between DTRT and VMAT plan robustness were compared using Wilcoxon matched-pair signed-rank test (α = 5 %). ResultsAverage NTCP for moderate-to-severe xerostomia and grade ≥ II dysphagia was lower for DTRT than VMAT in the nominal scenario (0.5 %, p = 0.01; 2.1 %, p < 0.01) and for all investigated uncertainties, except MLC positioning, where the difference was not significant. Average differences compared to the nominal scenario were ≤ 3.5 Gy for rotational PS (≤ 3°) and machine-positioning (≤ 2°) uncertainties, <7 Gy for translational PS uncertainties (≤ 5 mm) and < 20 Gy for MLC-positioning uncertainties (≤ 5 mm). ConclusionsDTRT and VMAT plan robustness to the investigated uncertainties depended on uncertainty direction and location of the structure-of-interest to the target. NTCP remained on average lower for DTRT than VMAT even when considering uncertainties.

Improving Treatment Plan Robustness at Internal Tissue-Air Interfaces in Head and Neck Radiotherapy

Improving Treatment Plan Robustness at Internal Tissue-Air Interfaces in Head and Neck Radiotherapy

Robust, planning-based targeted locoregional tumour heating in small animals.

Objective.To improve hyperthermia in clinical practice, pre-clinical hyperthermia research is essential to investigate hyperthermia effects and assess novel treatment strategies. Translating pre-clinical hyperthermia findings into clinically viable protocols requires laboratory animal treatment techniques similar to clinical hyperthermia techniques. The ALBA micro8 electromagnetic heating system (Med-logix SRL, Rome, Italy) has recently been developed to provide the targeted locoregional tumour heating currently lacking for pre-clinical research. This study evaluates the heat focusing properties of this device and its ability to induce robust locoregional tumour heating under realistic physiological conditions using simulations.Approach.Simulations were performed using the Plan2Heat treatment planning package (Amsterdam UMC, the Netherlands). First, the specific absorption rate (SAR) focus was characterised using a homogeneous phantom. Hereafter, a digital mouse model was used for the characterisation of heating robustness in a mouse. Device settings were optimised for treatment of a pancreas tumour and tested for varying circumstances. The impact of uncertainties in tissue property and perfusion values was evaluated using polynomial chaos expansion. Treatment quality and robustness were evaluated based on SAR and temperature distributions.Main results.The SAR distributions within the phantom are well-focused and can be adjusted to target any specific location. The focus size (full-width half-maximum) is a spheroid with diameters 9 mm (radially) and 20 mm (axially). The mouse model simulations show strong robustness against respiratory motion and intestine and stomach filling (∆T90≤0.14°C).Mouse positioning errors in the cranial-caudal direction lead to∆T90≤0.23°C. Uncertainties in tissue property and perfusion values were found to impact the treatment plan up to 0.56 °C (SD), with a variation onT90of 0.32 °C (1 SD).Significance.Our work shows that the pre-clinical phased-array system can provide adequate and robust locoregional heating of deep-seated target regions in mice. Using our software, robust treatment plans can be generated for pre-clinical hyperthermia research.

Impact of setup errors on the robustness of linac-based single-isocenter coplanar and non-coplanar VMAT plans for multiple brain metastases.

Patient setup errors have been a primary concern impacting the dose delivery accuracy in radiation therapy. A robust treatment plan might mitigate the effects of patient setup errors. In this reported study, we aimed to evaluate the impact of translational and rotational errors on the robustness of linac-based, single-isocenter, coplanar, and non-coplanar volumetric modulated arc therapy treatment plans for multiple brain metastases. Fifteen patients were retrospectively selected for this study with a combined total of 49 gross tumor volumes (GTVs). Single-isocenter coplanar and non-coplanar plans were generated first with a prescribed dose of 40Gy in 5 fractions or 42Gy in 7 fractions to cover 95% of planning target volume (PTV). Next, four setup errors (+1 and +2mm translation, and +1° and +2° rotation) were applied individually to generate modified plans. Different plan quality evaluation metrics were compared between coplanar and non-coplanar plans. 3D gamma analysis (3%/2mm) was performed to compare the modified plans (+2mm and +2° only) and the original plans. Paired t-test was conducted for statistical analysis. After applying setup errors, variations of all plan evaluation metrics were similar (p>0.05). The worst case for V100% to GTV was 92.07%±6.13% in the case of +2mm translational error. 3D gamma pass rates were>90% for both coplanar (+2mm and +2°) and the +2mm non-coplanar groups but was 87.40%±6.89% for the +2° non-coplanar group. Translational errors have a greater impact on PTV and GTV dose coverage for both planning methods. Rotational errors have a greater negative impact on gamma pass rates of non-coplanar plans. Plan evaluation metrics after applying setup errors showed that both coplanar and non-coplanar plans were robust and clinically acceptable.

The dirty and clean dose concept: Towards creating proton therapy treatment plans with a photon-like dose response.

Applying tolerance doses for organs at risk (OAR) from photon therapy introduces uncertainties in proton therapy when assuming a constant relative biological effectiveness (RBE) of 1.1. This work introduces the novel dirty and clean dose concept, which allows for creating treatment plans with a more photon-like dose response for OAR and, thus, less uncertainties when applying photon-based tolerance doses. The concept divides the 1.1-weighted dose distribution into two parts: the clean and the dirty dose. The clean and dirty dose are deposited by protons with a linear energy transfer (LET) below and above a set LET threshold, respectively. For the former, a photon-like dose response is assumed, while for the latter, the RBE might exceed 1.1. To reduce the dirty dose in OAR, a MaxDirtyDose objective was added in treatment plan optimization. It requires setting two parameters: LET threshold and max dirty dose level. A simple geometry consisting of one target volume and one OAR in water was used to study the reduction in dirty dose in the OAR depending on the choice of the two MaxDirtyDose objective parameters during plan optimization. The best performing parameter combinations were used to create multiple dirty dose optimized (DDopt) treatment plans for two cranial patient cases. For each DDopt plan, 1.1-weighted dose, variable RBE-weighted dose using the Wedenberg RBE model and dose-average LETd distributions as well as resulting normal tissue complication probability (NTCP) values were calculated and compared to the reference plan (RefPlan) without MaxDirtyDose objectives. In the water phantom studies, LET thresholds between 1.5 and 2.5keV/µm yielded the best plans and were subsequently used. For the patient cases, nearly all DDopt plans led to a reduced Wedenberg dose in critical OAR. This reduction resulted from an LET reduction and translated into an NTCP reduction of up to 19 percentage points compared to the RefPlan. The 1.1-weighted dose in the OARs was slightly increased (patient 1: 0.45Gy(RBE), patient 2: 0.08Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1-weighted dose in healthy brain tissue was observed (patient 1: 0.81Gy(RBE), patient 2: 0.53Gy(RBE)). The variation of NTCP values due to variation of α/β from 2 to 3Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp). The novel dirty and clean dose concept allows for creating biologically more robust proton treatment plans with a more photon-like dose response. The reduced uncertainties in RBE can, therefore, mitigate uncertainties introduced by using photon-based tolerance doses for OAR.

A robust evaluation of 49 high-dose-rate prostate brachytherapy treatment plans including all major uncertainties.

Uncertainties in radiotherapy cause deviation from the planned dose distribution and may result in delivering a treatment that fails to meet clinical objectives. The impact of uncertainties is unique to the patient anatomy and the needle locations in HDR prostate brachytherapy. Evaluating this impact during treatment planning is not common practice, relying on margins around the target or organs-at-risk to account for uncertainties. A robust evaluation framework for HDR prostate brachytherapy treatment plans was evaluated on 49 patient plans, measuring the range of possible dosimetric outcomes to the patient due to 14 major uncertainties. Patient plans were evaluated for their robustness to uncertainties by simulating probable uncertainty scenarios. Five-thousand probabilistic and 1943 worst-case scenarios per patient were simulated by changing the position and size of structures and length of dwell times from their nominal values. For each uncertainty scenario, the prostate D90 and maximum doses to the urethra, D0.01cc , and rectum, D0.1cc , were calculated. The D90 was an average 1.16±0.51% (mean±SD) below nominal values for the probabilistic scenarios; the D0.01cc metric was 2.24±0.90% higher; and D0.1cc was greater by 0.48±0.30%. The D0.01cc and D90 metrics were more sensitive to uncertainties than D0.1cc , with a median of 79.0% and 84.9% of probabilistic scenarios passing the constraints, compared to 96.5%. The median pass-rate for scenarios that passed all three metrics simultaneously was 63.4%. Assessing treatment plan robustness improves plan quality assurance, is achievable in less than 1-min, and identifies treatment plans with poor robustness, allowing re-optimization before delivery.

Feasibility Study of Robust Treatment Planning in VMAT for Head and Neck Cancer

In general, patient positional uncertainty is considered by adding a geometrically expanded margin to clinical target volume (CTV) in photon radiation therapy. However, this method may not be suitable because image-guided radiotherapy is available. In intensity modulated proton beam therapy, robust treatment planning is currently common to take patient positional uncertainty into account in optimization rather than in margins. The purpose of this study is to assess the feasibility of clinical implementation of the method in volumetric modulated arc therapy (VMAT) for head and neck cancer. We quantitatively evaluated whether the plans with the robust optimization (Robust plans) can adequately cover CTV against patients' positional uncertainties and body shape change throughout a treatment course. Ten head and neck cancer patients were chosen, who were treated with PTV-based VMAT plans in our hospital between 2021.5-2022.4. RayStation V10A (RaySearch Laboratories, Stockholm, Sweden) was used for the robust optimization, which was applied to the CTVs with patient positional uncertainty of 5 mm in the 6-axis direction. Dose prescribed to the high- and low-risk CTVs were to 70 and 56 Gy in 35 fractions, respectively. To create the patients' CT images with residual set-up errors and body shape change at the treatment, pseudo simulation-CT images were created by deformable image registration with CBCT and simulation-CT. Dose distribution at the treatment was re-calculated by applying the plan to the pseudo simulation-CT images. The variation of D98 for the high-risk CTV from the time of treatment planning was evaluated on a weekly basis. For comparison, planning target volume (PTV) -based plans (5 mm margin circumference) were created and a similar evaluation was performed. D98 for the high-risk CTV varied between -3∼2% in the robust plan and between -5∼1% in the PTV-based plan during the treatment course. There was no significant difference in the amount of D98 variation between the two plans by t-test, except for one case with hypopharyngeal cancer. In this case, D98 for the high-risk CTV varied within ±1% with the PTV-based plan, whereas the value decreased up to 3% with the robust plan (p < 0.05). This case often had a residual setup error of approximately 5 mm at the sites related to the pitch rotation of head, suggesting that the dose distribution for the robust plan was affected by non-rigid positional errors. Patient weight loss during the treatment period was -3.5±2.4 kg, showing a weak correlation (r = -0.33) with the variation in D98 for the high-risk CTV. The robust treatment planning exhibits comparable CTV coverage to the conventional PTV-based planning against positional uncertainty and body shape change throughout a treatment period. In order to overcome set-up baseline shift by the non-rigid positional errors, re-planning should be recommended. Further planning studies will be conducted to promote clinical implementation of the method.

Dosimetric Impact of Intrafraction Prostate Motion and Interfraction Anatomical Changes in Dose-Escalated Linac-Based SBRT

The aim of this study was to investigate the impact of intrafraction prostate motion and interfraction anatomical changes on dose metrics and the effect of beam gating and motion correction in dose-escalated Linac-based SBRT. Thirteen patients (56 fractions) with organ-confined prostate cancer underwent dose-escalated FFF-VMAT SBRT. Accurate patient setup was ensured by CBCT, and real-time 3D prostate motion data were obtained using a novel electromagnetic tracking device. Treatment was interrupted when the signals exceeded a 2 mm threshold in any of the three spatial directions and couch position was corrected unless the offset was transient. Prostate trajectories with and without beam gating and motion correction events were reconstructed and analyzed. Rectum and bladder volumes on each daily CBCT were recorded and compared with volumes at simulation. The prostate motion observed for each fraction was incorporated into the patient original treatment plan with an isocenter shift method. Actually, delivered treatments were then obtained by recalculating the reconstructed motion-encoded plan on deformed CTs reflecting the patient CBCT-anatomy of the day. Non-gated treatments were also simulated using the prostate motion data assuming that no treatment interruptions have occurred. Treatment interruptions because of target motion trespassing the predefined threshold occurred in 25 fractions (45%). Rectum and bladder volume changes were relevant in most patients. The mean relative dose differences between actually delivered and planned treatments were -3.0% [-18.5 - 2.8] for CTV D99% and -2.6% [-17.8 - 1.0] for PTV D95% over all 56 fractions. However, the median cumulative CTV coverage with 93% of the prescribed dose turned out to be satisfactory. Urethra sparing was slightly degraded and a mean reduction of rectum and bladder dose was seen in all but two dose metrics: the maximum dose to rectum mucosa and the bladder D40%. Nevertheless, only 2 major deviations in rectum mucosa D0.035cc were observed at the end of the treatment. The greatest contribution to target missing and OARs doses came from the interfraction anatomical variations, while intrafraction prostate motion marginally contributed in gated treatments. In non-gated treatments, further deteriorations by 2.4 - 2.8% in minimum target coverage metrics and by 3.1 - 11.6% in bladder dose parameters would have occurred on average. The implemented motion management strategy and the strict patient preparation regimen, along with current PTV margins, robustness of original treatment plans, and fast FFF beam delivery, ensured no significant degradations of dose metrics due to both intrafraction motion and interfraction anatomical changes. Non-gated treatments would have more widely deteriorated the dosimetry of some individual fractions. Thus, continuous monitoring, beam gating and motion correction are recommended to safely deliver dose-escalated prostate SBRT.

Plan robustness analysis for threshold determination of SGRT-based intrafraction motion control in 3DCRT breast cancer radiation therapy

PurposeThe goal of this study was to obtain maximum allowed shift deviations from planning position in six degrees of freedom (DOF), that can serve as threshold values in surface guided radiation therapy (SGRT) of breast cancer patients.MethodsThe robustness of conformal treatment plans of 50 breast cancer patients against 6DOF shifts was investigated. For that, new dose distributions were calculated on shifted computed tomography scans and evaluated with respect to target volume and spinal cord dose. Maximum allowed shift values were identified by imposing dose constraints on the target volume dose coverage for 1DOF, and consecutively, for 6DOF shifts using an iterative approach and random sampling.ResultsSubstantial decreases in target dose coverage and increases of spinal cord dose were observed. Treatment plans showed highly differing robustness for different DOFs or treated area. The sensitivity was particularly high if clavicular lymph nodes were irradiated, for shifts in lateral, vertical, roll or yaw direction, and showed partly pronounced asymmetries. Threshold values showed similar properties with an absolute value range of 0.8 mm to 5 mm and 1.4° to 5°.ConclusionThe robustness analysis emphasized the necessity of taking differences between DOFs and asymmetrical sensitivities into account when evaluating the dosimetric impact of position deviations. It also highlighted the importance of rotational shifts, especially if clavicular lymph nodes were irradiated. A practical approach of determining 6DOF shift limits was introduced and a set of threshold values applicable for SGRT based patient motion control was identified.

PO96: Treatment Planning Dosimetry Predictive of Treatment Plan Robustness in Permanent Breast Seed Implant

PO96: Treatment Planning Dosimetry Predictive of Treatment Plan Robustness in Permanent Breast Seed Implant

Patient Breathing Motion and Delivery Specifics Influencing the Robustness of a Proton Pancreas Irradiation.

Motion compensation strategies in particle therapy depend on the anatomy, motion amplitude and underlying beam delivery technology. This retrospective study on pancreas patients with small moving tumours analysed existing treatment concepts and serves as a basis for future treatment strategies for patients with larger motion amplitudes as well as the transition towards carbon ion treatments. The dose distributions of 17 hypofractionated proton treatment plans were analysed using 4D dose tracking (4DDT). The recalculation of clinical treatment plans employing robust optimisation for mitigating different organ fillings was performed on phased-based 4D computed tomography (4DCT) data considering the accelerator (pulsed scanned pencil beams delivered by a synchrotron) and the breathing-time structure. The analysis confirmed the robustness of the included treatment plans concerning the interplay of beam and organ motion. The median deterioration of D50% (ΔD50%) for the clinical target volume (CTV) and the planning target volume (PTV) was below 2%, while the only outlier was observed for ΔD98% with -35.1%. The average gamma pass rate over all treatment plans (2%/ 2 mm) was 88.8% ± 8.3, while treatment plans for motion amplitudes larger than 1 mm performed worse. For organs at risk (OARs), the median ΔD2% was below 3%, but for single patients, essential changes, e.g., up to 160% for the stomach were observed. The hypofractionated proton treatment for pancreas patients based on robust treatment plan optimisation and 2 to 4 horizontal and vertical beams showed to be robust against intra-fractional movements up to 3.7 mm. It could be demonstrated that the patient's orientation did not influence the motion sensitivity. The identified outliers showed the need for continuous 4DDT calculations in clinical practice to identify patient cases with more significant deviations.

A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy

Objective. In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. To assess the need for adaptation, motion models can be used to simulate dominant motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same set of deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient. Approach. We propose a deep learning probabilistic framework that generates deformation vector fields warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs with prostate, bladder, and rectum delineations from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and ‘ground truth’ distributions of volume and center of mass changes. Results. With a DICE score of 0.86 ± 0.05 and a distance between prostate contours of 1.09 ± 0.93 mm, DAM matches and improves upon previously published PCA-based models, using as few as 8 latent variables. The overlap between distributions further indicates that DAM’s sampled movements match the range and frequency of clinically observed daily changes on repeat CTs. Significance. Conditioned only on planning CT values and organ contours of a new patient without any pre-processing, DAM can accurately deformations seen during following treatment sessions, enabling anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.

Clinical 3D/4D cumulative proton dose assessment methods for thoracic tumours with large motion

PurposeDespite the anticipated clinical benefits of intensity-modulated proton therapy (IMPT), plan robustness may be compromised due to its sensitivity to patient treatment uncertainties, especially for tumours with large motion. In this study, we investigated treatment course-wise plan robustness for intra-thoracic tumours with large motion comparing a 4D pre-clinical evaluation method (4DREM) to our clinical 3D/4D dose reconstruction and accumulation methods. Materials and methodsTwenty patients with large target motion (>10 mm) were treated with five times layered rescanned IMPT. The 3D-robust optimised plans were generated on the averaged planning 4DCT. Using multiple 4DCTs, treatment plan robustness was assessed on a weekly and treatment course-wise basis through the 3D robustness evaluation method (3DREM, based on averaged 4DCTs), the 4D robustness evaluation method (4DREM, including the time structure of treatment delivery and 4DCT phases) and 4D dose reconstruction and accumulation (4DREAL, based on fraction-wise information). ResultsBaseline target motion for all patients ranged from 11–17 mm. For the offline adapted course-wise dose assessment, adequate target dose coverage was found for all patients. The target volume receiving 95% of the prescription dose was consistent between methods with 16/20 patients showing differences < 1%. 4DREAL showed the highest target coverage (99.8 ± 0.6%, p < 0.001), while no differences were observed between 3DREM and 4DREM (99.3 ± 1.3% and 99.4 ± 1.1%, respectively). ConclusionOur results show that intra-thoracic tumours can be adequately treated with IMPT in free breathing for target motion amplitudes up to 17 mm employing any of the accumulation methods. Anatomical changes, setup and range errors demonstrated a more severe impact on target coverage than motion in these patients treated with fractionated proton radiotherapy.

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning.

The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1 106 delivered proton spots were used to train the ML model. The presented model was tested and used to predict the spot position as well as the monitor units (MU) per spot, based on the original planning parameters. Two patient plans (one accelerated partial breast irradiation [APBI] and one ependymoma) were recalculated with the predicted spot position/MUs by the ML model and then were re-analyzed for robustness. Plans with ML predicted spots were less robust than the original clinical plans. In the APBI plan, dosimetric changes to the left lung and heart were not clinically relevant. In the ependymoma plan, the hot spot in the brainstem decreased and the hot spot in the cervical cord increased. Despite these differences, after robustness analysis, both ML spot delivery error plans resulted in >95% of the CTV receiving >95% of the prescription dose. The presented workflow has the potential benefit of including realistic spots information for plan quality checks in IMPT. This work demonstrates that in the two example plans, the plans were still robust when accounting for spot delivery errors as predicted by the ML model.

Robust VMAT treatment planning for extremity soft tissue sarcomas.

Volumetric modulated arc therapy (VMAT) is a frequently employed and guideline-recommended radiotherapy (RT) modality for extremity soft tissue sarcomas (eSTS). Prior studies have demonstrated that significant tumor volume changes during treatment result in loss of target volume coverage with highly conformal techniques such as VMAT, but few solutions exist to these issues aside from adaptive replanning. Here, we describe a related but novel phenomenon in which relatively minor changes in surface volume contour (whether due to daily setup uncertainty, edema of peritumoral tissue, or progression or pseudo-progression of tumor volume itself) can result in unexpected subcutaneous hotspots. This phenomenon is of significant clinical concern given the known association between skin dose and major wound complications during preoperative RT for eSTS. By evaluating daily cone-beam CT (CBCT) images from thirteen eSTS patients treated with VMAT RT, we identify daily surface contour changes (range: 2 mm-15 mm, median: 8 mm) which are frequently below conventional adaptive replanning thresholds. When applied under experimental conditions, these external contour changes did not have major impacts on target volume coverage (range: 30.2%-91.2%, mean: 72.5%) but did result in unexpected hotspots of 125.8% on average (range: 110.0%-142.2%) in the subcutaneous tissues. To mitigate this issue, we develop a methodology for VMAT treatment planning using flash PTV and virtual bolus (VB) to produce robust treatment plans that are more resistant to target volume changes, surface contour changes, and setup uncertainties than conventional planning methods. With this methodology, robust plans were equivalent to standard plans at baseline, but, after incorporation of surface volume changes, both maintained target volume coverage (p < 0.001) and prevented development of subcutaneous hotspots (p < 0.001) better than standard plans. As such, this treatment planning methodology may facilitate development of robust VMAT treatment plans that minimize development of subcutaneous hotspots and preserve target volume coverage in the context of routine volumetric changes during preoperative RT.

Dosimetric impact of systematic spot position errors in spot scanning proton therapy of head and neck tumor.

The spot position is an important beam parameter in the quality assurance of scanning proton therapy. In this study, we investigated dosimetric impact of systematic 15 spot position errors (SSPE) in spot scanning proton therapy using three types of optimization methods of head and neck tumor. The planning simulation was performed with ± 2 mm model SSPE in the X and Y directions. Treatment plans were created using intensity-modulated proton therapy (IMPT) and single-field uniform dose (SFUD). IMPT plans were created by two optimization methods: with worst-case optimization (WCO-IMPT) and without (IMPT). For clinical target volume (CTV), D95%, D50%, and D2cc were used for analysis. For organs at risk (OAR), Dmean was used to analyze the brain, cochlea, and parotid, and Dmax was used to analyze brainsetem, chiasm, optic nerve, and cord. For CTV, the variation (1 standard deviation) of D95% was ± 0.88%, 0.97% and 0.97% to WCO-IMPT, IMPT, and SFUD plan. The variation of D50% and D2cc of CTV showed <0.5% variation in all plans. The dose variation due to SSPE was larger in OAR, and worst-case optimization reduced the dose variation, especially in Dmax. The analysis results showed that SSPE has little impact on SFUD. We clarified the impact of SSPE on dose distribution for three optimization methods. SFUD was shown to be a robust treatment plan for OARs, and the WCO can be used to increase robustness to SSPE in IMPT.