First Clinical Investigation of Cone Beam Computed Tomography and Deformable Registration for Adaptive Proton Therapy for Lung Cancer

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer

Purpose

Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.

Methods

A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors.

Results

On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%).

Conclusion

CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.

Simultaneous Multiple Liver Metastasis Treated with Pencil Beam Proton Stereotactic Body Radiotherapy (SBRT)

Compared with photon stereotactic body radiotherapy (SBRT) plans that may have to use many more penetrating x-ray beams for each isocenter, proton SBRT with ultrahypofractionated doses use fewer beam angles and offer significantly reduced low-dose radiation bath to normal liver tissue. We demonstrate techniques to deliver safe and effective proton SBRT, where planning and organ motion complexity further increased with multiple liver lesions. For treatment planning, we recommend robust and logical beam angles, avoiding devices and encouraging entry perpendicular to the dominant motion, as well as volumetric repainting to mitigate the interplay effect to clinically acceptable levels. This report highlights the significant technical challenges with ultrahypofractionated proton pencil beam scanning liver therapy, how they are managed, and the effectiveness of this treatment.

[The role of proton therapy in esophageal cancer]

Because of the physical properties of proton beam radiation therapy (PT), which allows energy to be deposited at a specific depth with a rapid energy fall-off beyond that depth, PT has several theoretical advantages over photon radiation therapy for esophageal cancer (EC). Protons have the potential to reduce the dose to healthy tissue and to more safely allow treatment of tumors near critical organs, dose escalation, trimodal treatment, and re-irradiation. In recent years, larger multicenter retrospective studies have been published showing excellent survival rates, lower than expected toxicities and even better outcomes with PT than with photon radiotherapy even using IMRT or VMAT techniques. Although PT was associated with reduced toxicities, postoperative complications, and hospital stays compared to photon radiation therapy, these studies all had inherent biases in relation with patient selection for PT. These observations were recently confirmed by a randomized phase II study in locally advanced EC that showed significantly reduced toxicities with protons compared with IMRT. Currently, two randomized phase III trials (NRG-GI006 in the US and PROTECT in Europe) are being conducted to confirm whether protons could become the standard of care in locally advanced and resectable esophageal cancers.

Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning

Purpose: Cone beam computed tomography (CBCT) is a standard solution for in‐room image guidance for radiation therapy. It is used to evaluate and compensate for anatomopathological changes between the dose delivery plan and the fraction delivery day. CBCT is a fast and versatile solution, but it suffers from drawbacks like low contrast and requires proper calibration to derive density values. Although these limitations are even more prominent with in‐room customized CBCT systems, strategies based on deep learning have shown potential in improving image quality. As such, this article presents a method based on a convolutional neural network and a novel two‐step supervised training based on the transfer learning paradigm for shading correction in CBCT volumes with narrow field of view (FOV) acquired with an ad hoc in‐room system.

Methods: We designed a U‐Net convolutional neural network, trained on axial slices of corresponding CT/CBCT couples. To improve the generalization capability of the network, we exploited two‐stage learning using two distinct data sets. At first, the network weights were trained using synthetic CBCT scans generated from a public data set, and then only the deepest layers of the network were trained again with real‐world clinical data to fine‐tune the weights. Synthetic data were generated according to real data acquisition parameters. The network takes a single grayscale volume as input and outputs the same volume with corrected shading and improved HU values.

Results: Evaluation was carried out with a leave‐one‐out cross‐validation, computed on 18 unique CT/CBCT pairs from six different patients from a real‐world dataset. Comparing original CBCT to CT and improved CBCT to CT, we obtained an average improvement of 6 dB on peak signal‐to‐noise ratio (PSNR), +2% on structural similarity index measure (SSIM). The median interquartile range (IQR) Hounsfield unit (HU) difference between CBCT and CT improved from 161.37 (162.54) HU to 49.41 (66.70) HU. Region of interest (ROI)‐based HU difference was narrowed by 75% in the spongy bone (femoral head), 89% in the bladder, 85% for fat, and 83% for muscle. The improvement in contrast‐to‐noise ratio for these ROIs was about 67%.

Conclusions: We demonstrated that shading correction obtaining CT‐compatible data from narrow‐FOV CBCTs acquired with a customized in‐room system is possible. Moreover, the transfer learning approach proved particularly beneficial for such a shading correction approach.

Validation of proton dose calculation on scatter corrected 4D cone beam computed tomography using a porcine lung phantom

Proton therapy treatment for lungs remains challenging as images enabling the detection of inter- and intra-fractional motion, which could be used for proton dose adaptation, are not readily available. 4D computed tomography (4DCT) provides high image quality but is rarely available in-room, while in-room 4D cone beam computed tomography (4DCBCT) suffers from image quality limitations stemming mostly from scatter detection. This study investigated the feasibility of using virtual 4D computed tomography (4DvCT) as a prior for a phase-per-phase scatter correction algorithm yielding a 4D scatter corrected cone beam computed tomography image (4DCBCT_cor), which can be used for proton dose calculation. 4DCT and 4DCBCT scans of a porcine lung phantom, which generated reproducible ventilation, were acquired with matching breathing patterns. Diffeomorphic Morphons, a deformable image registration algorithm, was used to register the mid-position 4DCT to the mid-position 4DCBCT and yield a 4DvCT. The 4DCBCT was reconstructed using motion-aware reconstruction based on spatial and temporal regularization (MA-ROOSTER). Successively for each phase, digitally reconstructed radiographs of the 4DvCT, simulated without scatter, were exploited to correct scatter in the corresponding CBCT projections. The 4DCBCT_corwas then reconstructed with MA-ROOSTER using the corrected CBCT projections and the same settings and deformation vector fields as those already used for reconstructing the 4DCBCT. The 4DCBCT_corand the 4DvCT were evaluated phase-by-phase, performing proton dose calculations and comparison to those of a ground truth 4DCT by means of dose-volume-histograms (DVH) and gamma pass-rates (PR). For accumulated doses, DVH parameters deviated by at most 1.7% in the 4DvCT and 2.0% in the 4DCBCT_corcase. The gamma PR for a (2%, 2 mm) criterion with 10% threshold were at least 93.2% (4DvCT) and 94.2% (4DCBCT_cor), respectively. The 4DCBCT_cortechnique enabled accurate proton dose calculation, which indicates the potential for applicability to clinical 4DCBCT scans.

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.

Applications of various range shifters for proton pencil beam scanning radiotherapy

Background

A range pull-back device, such as a machine-related range shifter (MRS) or a universal patient-related range shifter (UPRS), is needed in pencil beam scanning technique to treat shallow tumors.

Methods

Three UPRS made by QFix (Avondale, PA, USA) allow treating targets across the body: U-shaped bolus (UB), anterior lateral bolus (ALB), and couch top bolus. Head-and-neck (HN) patients who used the UPRS were tested. The in-air spot sizes were measured and compared in this study at air gaps: 6 cm, 16 cm, and 26 cm. Measurements were performed in a solid water phantom using a single-field optimization pencil beam scanning field with the ALB placed at 0, 10, and 20 cm air gaps. The two-dimensional dose maps at the middle of the spread-out Bragg peak were measured using ion chamber array MatriXX PT (IBA-Dosimetry, Schwarzenbruck, Germany) located at isocenter and compared with the treatment planning system.

Results

A UPRS can be consistently placed close to the patient and maintains a relatively small spot size resulting in improved dose distributions. However, when a UPRS is non-removable (e.g. thick couch top), the quality of volumetric imaging is degraded due to their high Z material construction, hindering the value of Image-Guided Radiation Therapy (IGRT). Limitations of using UPRS with small air gaps include reduced couch weight limit, potential collision with patient or immobilization devices, and challenges using non-coplanar fields with certain UPRS. Our experience showed the combination of a U-shaped bolus exclusively for an HN target and an MRS as the complimentary device for head-and-neck targets as well as for all other treatment sites may be ideal to preserve the dosimetric advantages of pencil beam scanning proton treatments across the body.

Conclusion

We have described how to implement UPRS and MRS for various clinical indications using the PBS technique, and comprehensively reviewed the advantage and disadvantages of UPRS and MRS. We recommend the removable UB only to be employed for the brain and HN treatments while an automated MRS is used for all proton beams that require RS but not convenient or feasible to use UB.

Normal Tissue Injury Induced by Photon and Proton Therapies: Gaps and Opportunities

Despite technological advances in radiation therapy (RT) and cancer treatment, patients still experience adverse effects. Proton therapy (PT) has emerged as a valuable RT modality that can improve treatment outcomes. Normal tissue injury is an important determinant of the outcome; therefore, for this review, we analyzed 2 databases: (1) clinical trials registered with ClinicalTrials.gov and (2) the literature on PT in PubMed, which shows a steady increase in the number of publications. Most studies in PT registered with ClinicalTrials.gov with results available are nonrandomized early phase studies with a relatively small number of patients enrolled. From the larger database of nonrandomized trials, we listed adverse events in specific organs/sites among patients with cancer who are treated with photons and protons to identify critical issues. The present data demonstrate dosimetric advantages of PT with favorable toxicity profiles and form the basis for comparative randomized prospective trials. A comparative analysis of 3 recently completed randomized trials for normal tissue toxicities suggests that for early stage non-small cell lung cancer, no meaningful comparison could be made between stereotactic body RT and stereotactic body PT due to low accrual (NCT01511081). In addition, for locally advanced non-small cell lung cancer, a comparison of intensity modulated RTwith passive scattering PT (now largely replaced by spot-scanned intensity modulated PT), PT did not provide any benefit in normal tissue toxicity or locoregional failure over photon therapy. Finally, for locally advanced esophageal cancer, proton beam therapy provided a lower total toxicity burden but did not improve progression-free survival and quality of life (NCT01512589). The purpose of this review is to inform the limitations of current trials looking at protons and photons, considering that advances in technology, physics, and biology are a continuum, and to advocate for future trials geared toward accurate precision RT that need to be viewed as an iterative process in a defined path toward delivering optimal radiation treatment. A foundational understanding of the radiobiologic differences between protons and photons in tumor and normal tissue responses is fundamental to, and necessary for, determining the suitability of a given type of biologically optimized RT to a patient or cohort.

A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT

Purpose

In recent years, cone-beam computed tomography (CBCT) is increasingly used in adaptive radiation therapy (ART). However, compared with planning computed tomography (PCT), CBCT image has much more noise and imaging artifacts. Therefore, it is necessary to improve the image quality and HU accuracy of CBCT. In this study, we developed an unsupervised deep learning network (CycleGAN) model to calibrate CBCT images for the pelvis to extend potential clinical applications in CBCT-guided ART.

Methods

To train CycleGAN to generate synthetic PCT (sPCT), we used CBCT and PCT images as inputs from 49 patients with unpaired data. Additional deformed PCT (dPCT) images attained as CBCT after deformable registration are utilized as the ground truth before evaluation. The trained uncorrected CBCT images are converted into sPCT images, and the obtained sPCT images have the characteristics of PCT images while keeping the anatomical structure of CBCT images unchanged. To demonstrate the effectiveness of the proposed CycleGAN, we use additional nine independent patients for testing.

Results

We compared the sPCT with dPCT images as the ground truth. The average mean absolute error (MAE) of the whole image on testing data decreased from 49.96 ± 7.21HU to 14.6 ± 2.39HU, the average MAE of fat and muscle ROIs decreased from 60.23 ± 7.3HU to 16.94 ± 7.5HU, and from 53.16 ± 9.1HU to 13.03 ± 2.63HU respectively.

Conclusion

We developed an unsupervised learning method to generate high-quality corrected CBCT images (sPCT). Through further evaluation and clinical implementation, it can replace CBCT in ART.

Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients

Purpose

Cone‐beam CT (CBCT)‐based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed.

This work evaluates the potential of using a range‐probing (RP)‐based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs.

Methods

A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images.

Results

Mean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs.

Conclusions

The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.

Measurement-based range evaluation for quality assurance of CBCT-based dose calculations in adaptive proton therapy

PURPOSE

The implementation of volumetric in-room imaging for online adaptive radiotherapy makes extensive testing of this image data for treatment planning necessary. Especially for proton beams the higher sensitivity to stopping power properties of the tissue results in more stringent requirements. Current approaches mainly focus on recalculation of the plans on the new image data, lacking experimental verification, and ignoring the impact on the plan re-optimization process. The aim of this study was to use gel and film dosimetry coupled with a three-dimensional (3D) printed head phantom (based on the planning CT of the patient) for 3D range verification of intensity-corrected cone beam computed tomography (CBCT) image data for adaptive proton therapy.

METHODS

Single field uniform dose pencil beam scanning proton plans were optimized for three different patients on the patients' planning CT (planCT) and the patients' intensity-corrected CBCT (scCBCT) for the same target volume using the same optimization constraints. The CBCTs were corrected on projection level using the planCT as a prior. The dose optimized on planCT and recalculated on scCBCT was compared in terms of proton range differences (80% distal fall-off, recalculation). Moreover, the dose distribution resulting from recalculation of the scCBCT-optimized plan on the planCT and the original planCT dose distribution were compared (simulation). Finally, the two plans of each patient were irradiated on the corresponding patient-specific 3D printed head phantom using gel dosimetry inserts for one patient and film dosimetry for all three patients. Range differences were extracted from the measured dose distributions. The measured and the simulated range differences were corrected for range differences originating from the initial plans and evaluated.

RESULTS

The simulation approach showed high agreement with the standard recalculation approach. The median values of the range differences of these two methods agreed within 0.1 mm and the interquartile ranges (IQRs) within 0.3 mm for all three patients. The range differences of the film measurement were accurately matching with the simulation approach in the film plane. The median values of these range differences deviated less than 0.1 mm and the IQRs less than 0.4 mm. For the full 3D evaluation of the gel range differences, the median value and IQR matched those of the simulation approach within 0.7 and 0.5 mm, respectively. scCBCT- and planCT-based dose distributions were found to have a range agreement better than 3 mm (median and IQR) for all considered scenarios (recalculation, simulation, and measurement).

CONCLUSIONS

The results of this initial study indicate that an online adaptive proton workflow based on scatter-corrected CBCT image data for head irradiations is feasible. The novel presented measurement- and simulation-based method was shown to be equivalent to the standard literature recalculation approach. Additionally, it has the capability to catch effects of image differences on the treatment plan optimization. This makes the measurement-based approach particularly interesting for quality assurance of CBCT-based online adaptive proton therapy. The observed uncertainties could be kept within those of the registration and positioning. The proposed validation could also be applied for other alternative in-room images, e.g. for MR-based pseudoCTs.

Training a deep neural network coping with diversities in abdominal and pelvic images of children and young adults for CBCT-based adaptive proton therapy

PURPOSE

To train a deep neural network for correcting abdominal and pelvic cone-beam computed tomography (CBCT) of children and young adults in the presence of diverse patient size, anatomic extent, and scan parameters.

MATERIALS AND METHODS

Pretreatment CBCT and planning/repeat CT image pairs from 64 children and young adults treated with proton therapy (aged 1-23 years) were analyzed. To evaluate the impact of anatomic extent in CBCT and data size in the training data, we compared the performance of three cycle-consistent generative adversarial network models that were separately trained by three datasets comprising abdominal (n = 21), pelvic (n = 29), and combined abdominal-pelvic image pairs (n = 50), respectively. The maximum body width of each patient was normalized to a fixed width before training and model application to reduce the impact of variations in body size. The corrected CBCT images by the three models were comparatively evaluated against the repeat CT closest in time to the CBCT (median gap, 0 days; range, 0-6 days) in HU accuracy, estimated dose distribution, and proton range.

RESULTS

The network model trained by the combined dataset significantly outperformed the abdomen and pelvis models in mean absolute HU error of the corrected CBCT from 14 testing patients (47 ± 7 HU versus 51 ± 8 HU; paired Wilcoxon signed-rank test, P < 0.01). The larger error (60 ± 7 HU) without the body-size normalization confirmed the efficacy of the preprocessing. The model trained with the combined dataset resulted in gamma passing rates of 98.5 ± 1.9% (2%/2 mm criterion) and the range (80% distal fall-off) differences from the reference within ±3 mm for 91.2 ± 11.5% beamlets.

CONCLUSION

Combining data from adjacent anatomic sites and normalizing age-dependent body sizes in children and young adults were beneficial in training a neural network to accurately estimate proton dose from CBCT despite limited training data size and anatomic diversities.

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning

Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been proposed, techniques that include estimation of uncertainty are limited. This study proposes a novel uncertainty-aware pCT image correction method using a Bayesian convolutional neural network (BCNN). A DenseNet-based BCNN was constructed to predict both a corrected SPR image and its uncertainty from a noisy SPR image. A total 432 noisy SPR images of 6 non-anthropomorphic and 3 head phantoms were collected with Monte Carlo simulations, while true noise-free images were calculated with known geometric and chemical components. Heteroscedastic loss and deep ensemble techniques were performed to estimate aleatoric and epistemic uncertainties by training 25 unique BCNN models. 200-epoch end-to-end training was performed for each model independently. Feasibility of the predicted uncertainty was demonstrated after applying two post-hoc calibrations and calculating spot-specific path length uncertainty distribution. For evaluation, accuracy of head SPR images and water-equivalent thickness (WET) corrected by the trained BCNN models was compared with a conventional method and non-Bayesian CNN model. BCNN-corrected SPR images represent noise-free images with high accuracy. Mean absolute error in test data was improved from 0.263 for uncorrected images to 0.0538 for BCNN-corrected images. Moreover, the calibrated uncertainty represents accurate confidence levels, and the BCNN-corrected calibrated WET was more accurate than non-Bayesian CNN with high statistical significance. Computation time for calculating one image and its uncertainties with 25 BCNN models is 0.7 s with a consumer grade GPU. Our model is able to predict accurate pCT images as well as two types of uncertainty. These uncertainties will be useful to identify potential cause of SPR errors and develop a spot-specific range margin criterion, toward elaboration of uncertainty-guided proton therapy.

Comparison of weekly and daily online adaptation for head and neck intensity-modulated proton therapy

The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OA_W), and daily online adaptation (OA_D). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo (MC) dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OA_Dmitigated random delivery errors more effectively than OA_Won a fraction per fraction basis, both OA_Wand OA_Dachieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values of D_98%ranged between 97.15% and 99.73% of the prescription dose for OA_D, with a median of 98.07%. For OA_W, values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OA_Wcould be used as an alternative approach to OA_Dfor most patients in order to reduce the clinical workload.

Anthropomorphic lung phantom based validation of in-room proton therapy 4D-CBCT image correction for dose calculation

PURPOSE

Ventilation-induced tumour motion remains a challenge for the accuracy of proton therapy treatments in lung patients. We investigated the feasibility of using a 4D virtual CT (4D-vCT) approach based on deformable image registration (DIR) and motion-aware 4D CBCT reconstruction (MA-ROOSTER) to enable accurate daily proton dose calculation using a gantry-mounted CBCT scanner tailored to proton therapy.

METHODS

Ventilation correlated data of 10 breathing phases were acquired from a porcine ex-vivo functional lung phantom using CT and CBCT. 4D-vCTs were generated by (1) DIR of the mid-position 4D-CT to the mid-position 4D-CBCT (reconstructed with the MA-ROOSTER) using a diffeomorphic Morphons algorithm and (2) subsequent propagation of the obtained mid-position vCT to the individual 4D-CBCT phases. Proton therapy treatment planning was performed to evaluate dose calculation accuracy of the 4D-vCTs. A robust treatment plan delivering a nominal dose of 60Gy was generated on the average intensity image of the 4D-CT for an approximated internal target volume (ITV). Dose distributions were then recalculated on individual phases of the 4D-CT and the 4D-vCT based on the optimized plan. Dose accumulation was performed for 4D-vCT and 4D-CT using DIR of each phase to the mid position, which was chosen as reference. Dose based on the 4D-vCT was then evaluated against the dose calculated on 4D-CT both, phase-by-phase as well as accumulated, by comparing dose volume histogram (DVH) values (D_mean, D_2%, D_98%, D_95%) for the ITV, and by a 3D-gamma index analysis (global, 3%/3mm, 5Gy, 20Gy and 30Gy dose thresholds).

RESULTS

Good agreement was found between the 4D-CT and 4D-vCT-based ITV-DVH curves. The relative differences ((CT-vCT)/CT) between accumulated values of ITV D_mean, D_2%, D_95% and D_98% for the 4D-CT and 4D-vCT-based dose distributions were -0.2%, 0.0%, -0.1% and -0.1%, respectively. Phase specific values varied between -0.5% and 0.2%, -0.2% and 0.5%, -3.5% and 1.5%, and -5.7% and 2.3%. The relative difference of accumulated D_mean over the lungs was 2.3% and D_mean for the phases varied between -5.4% and 5.8%. The gamma pass-rates with 5Gy, 20Gy and 30Gy thresholds for the accumulated doses were 96.7%, 99.6% and 99.9%, respectively. Phase-by-phase comparison yielded pass-rates between 86% and 97%, 88% and 98%, and 94% and 100%.

CONCLUSIONS

Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation.

Concurrent Chemo-Proton Therapy Using Adaptive Planning for Unresectable Stage 3 Non-Small Cell Lung Cancer: A Phase 2 Study

PURPOSE

This study prospectively evaluated the efficacy and safety of concurrent chemo-proton therapy (CCPT) using adaptive planning for unresectable stage III non-small cell lung cancer (NSCLC).

METHODS AND MATERIALS

The primary endpoint was overall survival (OS). Secondary endpoints were local control rate (LCR), progression-free survival (PFS), incidence of grade 3 or higher adverse events, and changes in quality of life (QOL). Patients received cisplatin (60 mg/m²) on day 1 and S-1 (∼40 mg/m² twice daily) on days 1 to 14, q4w, for up to 4 cycles, plus concurrent proton therapy at a total dose of 70 GyRBE for the primary lesion and 66 GyRBE for lymph node metastasis with 2 GyRBE per day. Proton therapy was performed using respiratory-gated and image guided techniques, and adaptive plans were implemented.

RESULTS

Forty-seven patients were enrolled between August 2013 and August 2018. Four cycles of cisplatin plus S-1 were completed in 34 patients. The mean number of cycles was 4 (range, 1-4). The median follow-up of all and surviving patients was 37 (range, 4-84) and 52 months (range, 26-84), respectively. The mean number of replanning sessions was 2.5 (range, 1-4). The 2- and 5-year OS, LCR, and PFS were 77% (95% confidence interval 64%-89%) and 59% (43%-76%), 84% (73%-95%) and 61% (44%-78%), and 43% (28%-57%) and 37% (22%-51%), respectively. The median OS was not reached. No grade 3 or higher radiation pneumonitis was observed. There was no significant deterioration in the QOL scores after 24 months except for alopecia.

CONCLUSIONS

CCPT with adaptive planning was well tolerated and yielded remarkable OS for unresectable stage III NSCLC.

Evaluation of an a priori scatter correction algorithm for cone-beam computed tomography based range and dose calculations in proton therapy

BACKGROUND AND PURPOSE

Scatter correction of cone-beam computed tomography (CBCT) projections may enable accurate online dose-delivery estimations in photon and proton-based radiotherapy. This study aimed to evaluate the impact of scatter correction in CBCT-based proton range/dose calculations, in scans acquired in both proton and photon gantries.

MATERIAL AND METHODS

CBCT projections of a Catphan and an Alderson phantom were acquired on both a proton and a photon gantry. The scatter corrected CBCTs (corrCBCTs) and the clinical reconstructions (stdCBCTs) were compared against CTs rigidly registered to the CBCTs (rigidCTs). The CBCTs of the Catphan phantom were segmented by materials for CT number analysis. Water equivalent path length (WEPL) maps were calculated through the Alderson phantom while proton plans optimized on the rigidCT and recalculated on all CBCTs were compared in a gamma analysis.

RESULTS

In medium and high-density materials, the corrCBCT CT numbers were much closer to those of the rigidCT than the stdCBCTs. E.g. in the 50% bone segmentations the differences were reduced from above 300 HU (with stdCBCT) to around 60-70 HU (with corrCBCT). Differences in WEPL from the rigidCT were typically well below 5 mm for the corrCBCTs, compared to well above 10 mm for the stdCBCTs with the largest deviations in the head and thorax regions. Gamma pass rates (2%/2mm) when comparing CBCT-based dose re-calculations to rigidCT calculations were improved from around 80% (with stdCBCT) to mostly above 90% (with corrCBCT).

CONCLUSION

Scatter correction leads to substantial artefact reductions, improving accuracy of CBCT-based proton range/dose calculations.

A Preliminary Simulation Study of Dose-Guided Adaptive Radiotherapy Based on Halcyon MV Cone-Beam CT Images With Retrospective Data From a Phase II Clinical Trial

Background and purpose: To evaluate the feasibility of dose-guided adaptive radiotherapy (ART) based on deformable image registration (DIR) using fractional megavoltage cone-beam CT (MVCBCT) images from Halcyon system that uses identical beams for treatment and imaging and to retrospectively investigate the influence of anatomic changes on target coverage and organ-at-risk (OAR) sparing across various tumor sites.

Materials and Methods: Four hundred twenty-two MVCBCT images from 16 patients (three head and neck, seven thoracic, three abdominal, and three pelvic cases) treated in a phase II clinical trial for Halcyon were selected. DIR between the planning CT and daily MVCBCT image was implemented by Velocity software to create pseudo CT. To investigate the accuracy of dose calculation on pseudo CT, three evaluation patients with rescanned CT and adaptive plans were selected. Dose distribution of adaptive plans calculated on pseudo CT was compared with that calculated on the rescanned planning CT on the three evaluation patients. To investigate the impact of inter-fractional anatomic changes on target dose coverage and dose to OARs of the 16 patients, fractional dose was calculated and accumulated incrementally based on deformable registration between planning CT and daily MVCBCT images.

Results: Passing rates using 3 mm/3%/10% threshold local gamma analysis were 93.04, 96.00, and 91.68%, respectively, for the three evaluation patients between the reconstructed dose on pseudo CT (MVCBCT) and rescanned CT, where accumulated dose deviations of over 97% voxels were smaller than 0.5 Gy. Planning target volume (PTV) D95% and D90% (the minimum dose received by at least 95/90% of the volume) of the accumulated dose could be as low as 93.8 and 94.5% of the planned dose, respectively. OAR overdose of various degrees were observed in the 16 patients relative to the planned dose. In most cases, OARs' dose volume histogram (DVH) lines of accumulated and planned dose were very close to each other if not overlapping. Among cases with visible deviations, the differences were bilateral without apparent patterns specific to tumor sites or organs.

Conclusion: As a confidence building measure, this simulation study suggested the possibility of ART for Halcyon based on DIR between planning CT and MVCBCT. Preliminary clinical data suggested the benefit of patient-specific dose reconstruction and ART to avoid unacceptable target underdosage and OAR overdosage.

Investigation of inter-fraction target motion variations in the context of pencil beam scanned proton therapy in non-small cell lung cancer patients

Purpose

For locally advanced‐stage non‐small cell lung cancer (NSCLC), inter‐fraction target motion variations during the whole time span of a fractionated treatment course are assessed in a large and representative patient cohort. The primary objective is to develop a suitable motion monitoring strategy for pencil beam scanning proton therapy (PBS‐PT) treatments of NSCLC patients during free breathing.

Methods

Weekly 4D computed tomography (4DCT; 41 patients) and daily 4D cone beam computed tomography (4DCBCT; 10 of 41 patients) scans were analyzed for a fully fractionated treatment course. Gross tumor volumes (GTVs) were contoured and the 3D displacement vectors of the centroid positions were compared for all scans. Furthermore, motion amplitude variations in different lung segments were statistically analyzed. The dosimetric impact of target motion variations and target motion assessment was investigated in exemplary patient cases.

Results

The median observed centroid motion was 3.4 mm (range: 0.2–12.4 mm) with an average variation of 2.2 mm (range: 0.1–8.8 mm). Ten of 32 patients (31.3%) with an initial motion <5 mm increased beyond a 5‐mm motion amplitude during the treatment course. Motion observed in the 4DCBCT scans deviated on average 1.5 mm (range: 0.0–6.0 mm) from the motion observed in the 4DCTs. Larger motion variations for one example patient compromised treatment plan robustness while no dosimetric influence was seen due to motion assessment biases in another example case.

Conclusions

Target motion variations were investigated during the course of radiotherapy for NSCLC patients. Patients with initial GTV motion amplitudes of < 2 mm can be assumed to be stable in motion during the treatment course. For treatments of NSCLC patients who exhibit motion amplitudes of > 2 mm, 4DCBCT should be considered for motion monitoring due to substantial motion variations observed.

Proton Reirradiation: Expert Recommendations for Reducing Toxicities and Offering New Chances of Cure in Patients With Challenging Recurrence Malignancies

Local and regional recurrences are common following an initial course of radiotherapy, yet management of these recurrences remains a challenge. Reirradiation may be an optimal treatment approach for providing durable tumor control and even offering select patients with locoregional recurrences or new primary tumors a chance of cure, but photon reirradiation can be associated with considerable risks of high grade acute and late toxicities. The high conformality and lack of exit dose with proton therapy offer significant advantages for reirradiation. By decreasing dose to adjacent normal tissues, proton therapy can more safely deliver definitive instead of palliative doses of reirradiation, more safely dose escalate reirradiation treatment, and more safely allow for concurrent systemic therapy in the reirradiation setting. In this case-based analysis, renowned experts in the fields of proton therapy and of reirradiation present cases for which they recently employed proton reirradiation. This manuscript focuses on case studies in patients with lung cancer, head and neck malignancies, and pelvic malignancies. Considerations for when to deliver proton therapy in the reirradiation setting and the pros and cons of proton therapy are discussed, and the existing literature supporting the use of proton reirradiation for these disease sites is assessed.

Online adaptive dose restoration in intensity modulated proton therapy of lung cancer to account for inter-fractional density changes

BACKGROUND AND PURPOSE

In proton therapy, inter-fractional density changes can severely compromise the effective delivery of the planned dose. Such dose distortion effects can be accounted for by treatment plan adaptation, that requires considerable automation for widespread implementation in clinics. In this study, the clinical benefit of an automatic online adaptive strategy called dose restoration (DR) was investigated. Our objective was to assess to what extent DR could replace the need for a comprehensive offline adaptive strategy.

MATERIALS AND METHODS

The fully automatic and robust DR workflow was evaluated in a cohort of 14 lung IMPT patients that had a planning-CT and two repeated 4D-CTs (rCT1,rCT2). Initial plans were generated using 4D-robust optimization (including breathing-motion, setup and range errors). DR relied on isodose contours generated from the initial dose and associated patient specific weighted objectives to mimic this initial dose in repeated-CTs. These isodose contours, with their corresponding objectives, were used during re-optimization to compensate proton range distortions disregarding re-contouring. Robustness evaluations were performed for the initial, not-adapted and restored (adapted) plans.

RESULTS

The resulting DVH-bands showed overall improvement in DVH metrics and robustness levels for restored plans, with respect to not-adapted plans. According to CTV coverage criteria (D95%>95%Dprescription) in not-adapted plans, 35% (5/14) of the cases needed offline adaptation. After DR, Median(D95%) was increased by 1.1 [IQR,0.4] Gy and only one patient out of 14 (7%) still needed offline adaptation because of important anatomical changes.

CONCLUSIONS

DR has the potential to improve CTV coverage and reduce offline adaptation rate.

Cone-beam CT-derived relative stopping power map generation via deep learning for proton radiotherapy

PURPOSE

In intensity-modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs-at-risk. However, due to uncertainties in both patient setup and relative stopping power (RSP) calculation, margins are added to the treatment volume during treatment planning, leading to higher doses to normal tissues. Cone-beam computed tomography (CBCT) images are taken daily before treatment; however, the poor image quality of CBCT limits the use of these images for online dose calculation. In this work, we use a deep-learning-based method to predict RSP maps from daily CBCT images, allowing for online dose calculation in a step toward adaptive radiation therapy.

METHODS

Twenty-three head-and-neck cancer patients were simulated using a Siemens TwinBeam dual-energy CT (DECT) scanner. Mixed-energy scans (equivalent to a 120 kVp single-energy CT scan) were converted to RSP maps for treatment planning. Cone-beam computed tomography images were taken on the first day of treatment, and the planning RSP maps were registered to these images. A deep learning network based on a cycle-GAN architecture, relying on a compound loss function designed for structural and contrast preservation, was then trained to create an RSP map from a CBCT image. Leave-one-out and holdout cross validations were used for evaluation, and mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used to quantify the differences between the CT-based and CBCT-based RSP maps. The proposed method was compared to a deformable image registration-based method which was taken as the ground truth and two other deep learning methods. For one patient who underwent resimulation, the new planning RSP maps and CBCT images were used for further evaluation and validation.

RESULTS

The CBCT-based RSP generation method was evaluated on 23 head-and-neck cancer patients. From leave-one-out testing, the MAE between CT-based and CBCT-based RSP was 0.06 ± 0.01 and the ME was -0.01 ± 0.01. The proposed method statistically outperformed the comparison DL methods in terms of MAE and ME when compared to the planning CT. In terms of dose comparison, the mean gamma passing rate at 3%/3 mm was 94% when three-dimensional (3D) gamma index was calculated per plan and 96% when gamma index was calculated per field.

CONCLUSIONS

The proposed method provides sufficiently accurate RSP map generation from CBCT images, allowing for evaluation of daily dose based on CBCT and possibly allowing for CBCT-guided adaptive treatment planning for IMPT.

The Role of Positron Emission Tomography Imaging in Radiotherapy Target Delineation

Positron emission tomography (PET) is an advanced functional imaging modality in oncology care for the diagnosis, staging, prognostication, and surveillance of numerous malignancies. PET can also offer considerable advantages for target volume delineation as part of radiation treatment planning. In this review, data and clinical practice from 6 general oncology disease sites are assessed to descriptively evaluate the role of PET in target volume delineation. Also highlighted are several specific and practical utilities for PET imaging in radiation treatment planning. Publication of several ongoing prospective trials in the future may further expand the utility of PET for target delineation and patient care.

A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer

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A deep learning network facilitated dose calculation from CBCT.

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A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.

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Generation of synthetic-CT can be achieved within 10 s, facilitating online adaptive radiotherapy scenarios.

Background and purpose Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasibility of applying a single convolutional network to facilitate dose calculation based on CBCT for head-and-neck, lung and breast cancer patients.

Materials and Methods Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. The CBCTs were registered to planning CT according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an unpaired manner on 15 patients per anatomical site generating synthetic-CTs (sCTs). Another network was trained with all the anatomical sites together. Performances of all four networks were compared and evaluated for image similarity against rescan CT (rCT). Clinical plans were recalculated on rCT and sCT and analysed through voxel-based dose differences and γ-analysis.

Results A sCT was generated in 10 s. Image similarity was comparable between models trained on different anatomical sites and a single model for all sites. Mean dose differences <0.5% were obtained in high-dose regions. Mean gamma (3%, 3 mm) pass-rates >95% were achieved for all sites.

Conclusion Cycle-GAN reduced CBCT artefacts and increased similarity to CT, enabling sCT-based dose calculations. A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.

Consensus Statement on Proton Therapy in Mesothelioma

PURPOSE

Radiation therapy for mesothelioma remains challenging, as normal tissue toxicity limits the amount of radiation that can be safely delivered to the pleural surfaces, especially radiation dose to the contralateral lung. The physical properties of proton therapy result in better sparing of normal tissues when treating the pleura, both in the postpneumonectomy setting and the lung-intact setting. Compared with photon radiation, there are dramatic reductions in dose to the contralateral lung, heart, liver, kidneys, and stomach. However, the tissue heterogeneity in the thorax, organ motion, and potential for changing anatomy during the treatment course all present challenges to optimal irradiation with protons.

METHODS

The clinical data underlying proton therapy in mesothelioma are reviewed here, including indications, advantages, and limitations.

RESULTS

The Particle Therapy Cooperative Group Thoracic Subcommittee task group provides specific guidelines for the use of proton therapy for mesothelioma.

CONCLUSIONS

This consensus report can be used to guide clinical practice, insurance approval, and future research.

Updating Photon-Based Normal Tissue Complication Probability Models for Pneumonitis in Patients With Lung Cancer Treated With Proton Beam Therapy

PURPOSE

No validated models for predicting the risk of radiation pneumonitis (RP) with proton beam therapy (PBT) currently exist. Our goal was to externally validate and recalibrate multiple established photon-based normal tissue complication probability models for RP in a cohort with locally advanced nonsmall cell lung cancer treated with contemporary doses of chemoradiation using PBT.

METHODS AND MATERIALS

The external validation cohort consisted of 99 consecutive patients with locally advanced nonsmall cell lung cancer treated with chemoradiation using PBT. RP was retrospectively scored at 3 and 6 months posttreatment. We evaluated the performance of the photon Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) pneumonitis model, the QUANTEC model adjusted for clinical risk factors, and the newer Netherlands updated QUANTEC model. A closed testing procedure was performed to test the need for model updating, either by recalibration-in-the-large (re-estimation of intercept), recalibration (re-estimation of intercept/slope), or model revision (re-estimation of all coefficients).

RESULTS

There were 21 events (21%) of ≥grade 2 RP. The closed testing procedure on the PBT data set did not detect major deviations between the models and the data and recommended adjustment of the intercept only for the photon-based Netherlands updated QUANTEC model (intercept update: -1.2). However, an update of the slope and revision of the model coefficients were not recommended by the closed testing procedure, as the deviations were not significant within the power of the data.

CONCLUSIONS

The similarity between the dose-response relationship for PBT and photons for normal tissue complications has been an assumption until now. We demonstrate that the preexisting, widely used photon based models fit our PBT data well with minor modifications. These now-validated and updated normal tissue complication probability models can aid in individualizing selection of the most optimal treatment technique for a particular patient.

Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy

In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of -8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and dose calculation accuracy was reduced compared to the other methods.

High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study

Purpose; For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam's-eye-view at native gantry angles. However, PPI has poor inherent contrast and spatial resolution. To deal with this issue, we propose a deep-learning-based method to use kV digitally reconstructed radiographs (DRR) to improve PPI image quality. Method; We used a residual generative adversarial network (GAN) framework to learn the nonlinear mapping between PPIs and DRRs. Residual blocks were used to force the model to focus on the structural differences between DRR and PPI. To assess the accuracy of our method, we used 149 images for training and 30 images for testing. PPIs were acquired using a double-scattered proton beam. The DRRs acquired from CT acted as learning targets in the training process and were used to evaluate results from the proposed method using a six-fold cross-validation scheme. Results; Qualitatively, the corrected PPIs showed enhanced spatial resolution and captured fine details present in the DRRs that are missed in the PPIs. The quantitative results for corrected PPIs show average normalized mean error (NME), normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index of -0.1%, 0.3%, 39.14 dB, and 0.987, respectively. Conclusion; The results indicate the proposed method can generate high quality corrected PPIs and this work shows the potential to use a deep-learning model to make PPI available in proton radiotherapy. This will allow for beam's-eye-view (BEV) imaging with the particle used for treatment, leading to a valuable alternative to orthogonal x-rays or cone-beam CT for patient position verification.

Online daily adaptive proton therapy

It is recognized that the use of a single plan calculated on an image acquired some time before the treatment is generally insufficient to accurately represent the daily dose to the target and to the organs at risk. This is particularly true for protons, due to the physical finite range. Although this characteristic enables the generation of steep dose gradients, which is essential for highly conformal radiotherapy, it also tightens the dependency of the delivered dose to the range accuracy. In particular, the use of an outdated patient anatomy is one of the most significant sources of range inaccuracy, thus affecting the quality of the planned dose distribution. A plan should be ideally adapted as soon as anatomical variations occur, ideally online. In this review, we describe in detail the different steps of the adaptive workflow and discuss the challenges and corresponding state-of-the art developments in particular for an online adaptive strategy.

Dosimetric consequences of image guidance techniques on robust optimized intensity-modulated proton therapy for treatment of breast Cancer

PURPOSE

To investigate the consequences of residual setup error on target dose distribution using various image registration strategies for breast cancer treated with intensity-modulated proton therapy (IMPT).

MATERIALS AND METHODS

Among 11 post-lumpectomy patients who received IMPT, 44 dose distributions were computed. For each patient, the original plan (Plan-O) and three verification plans were calculated using different alignments: bony anatomy (VPlan-B), breast tissue (VPlan-T), and skin (VPlan-S). The target coverage were evaluated for each alignment technique. Additionally, 2 subvolumes-BreastNearSkin (1-cm rim of anterior CTV) and BreastNearCW (1-cm rim of posterior CTV)-were created to help localize CTV underdosing. Furthermore, we divided the setup error into the posture error and breast error. Patients with a large posture error and those with good posture setup but a large breast error were identified to evaluate the effect of posture error and breast error.

RESULTS

For Plan-O, VPlan-B, VPlan-T, and VPlan-S, respectively, the median (interquartile range) breast CTV D95 was 95.7%(94.7-96.3%), 95.1% (93.9-95.7%), 95.2% (94.8-95.6%), and 95.2% (94.9-95.7%); BreastNearCW D95 was 96.9% (95.6-97.3%), 94.8% (93.5-97.0%), 95.6% (94.8-97.0%), 95.6% (94.8-97.1%); and BreastNearSkin D95 was 94.1% (92.7-94.4%), 93.6% (92.2-94.5%), 93.5% (92.4-94.5%), and 94.4% (92.2-94.5%) of the prescription dose. 4/11 patients had ≥1% decrease in breast CTV D95, 1 of whom developed breast edema while the other 3 all had a > 2^o posture error. The CTV D95 variation was within 1% for the patients with good posture setup but >2^o breast error.

CONCLUSION

Acceptable target coverage was achieved with all three alignment strategies. Breast tissue and skin alignment maintained the breast target coverage marginally better than bony alignment, with which the posterior CTV along the chest wall is the predominant area affected by under-dosing. For target dose distribution, posture error appears more influential than breast error.

Proton pencil beam scanning treatment with feedback based voluntary moderate breath hold

Introduction The aim of this article is to introduce a novel protocol for proton pencil beam scanning treatment with moderate deep inspiration breath hold (mDIBH) and report on our clinical implementation results. Methods Three computed tomography (CT) scannings to build the patient's anatomy model were performed during the patient's voluntary mDIBH. All 3 CT scans were used in the optimization during the treatment planning process. Both orthogonal kV imaging and cone-beam computed tomography (CBCT) were implemented for patient alignment with BH prior to the treatment. The BH CBCT images were analyzed for BH reproducibility and the virtual total dose (VTD) retrospectively. To find the VTD, a series of deformable image registrations (DIR) were performed between CBCT and pCT. The effect of the variation of lung density on the dose distribution was also analyzed in the study. Results The values of the mean, standard deviation, maximum, and minimum of the tumor location difference between the CBCT and pCT were 1.9, 1.6, 4.7, and 0.0 mm, respectively. The percentage difference in D99% of CTVs between VTD and the nominal plan was within 1.5%. Conclusions The feedback-based voluntary moderate BH proton PBS treatment was successfully performed in our clinic. This study shows that there is a potential to implement the BH treatment widely in proton centers.

"Dose of the day" based on cone beam computed tomography and deformable image registration for lung cancer radiotherapy

Purpose

Adaptive radiotherapy (ART) has potential to reduce toxicity and facilitate safe dose escalation. Dose calculations with the planning CT deformed to cone beam CT (CBCT) have shown promise for estimating the “dose of the day”. The purpose of this study is to investigate the “dose of the day” calculation accuracy based on CBCT and deformable image registration (DIR) for lung cancer radiotherapy.

Methods

A total of 12 lung cancer patients were identified, for which daily CBCT imaging was performed for treatment positioning. A re‐planning CT (rCT) was acquired after 20 Gy for all patients. A virtual CT (vCT) was created by deforming initial planning CT (pCT) to the simulated CBCT that was generated from deforming CBCT to rCT acquired on the same day. Treatment beams from the initial plan were copied to the vCT and rCT for dose calculation. Dosimetric agreement between vCT‐based and rCT‐based accumulated doses was evaluated using the Bland‐Altman analysis.

Results

Mean differences in dose‐volume metrics between vCT and rCT were smaller than 1.5%, and most discrepancies fell within the range of ± 5% for the target volume, lung, esophagus, and heart. For spinal cord D_max, a large mean difference of −5.55% was observed, which was largely attributed to very limited CBCT image quality (e.g., truncation artifacts).

Conclusion

This study demonstrated a reasonable agreement in dose‐volume metrics between dose accumulation based on vCT and rCT, with the exception for cases with poor CBCT image quality. These findings suggest potential utility of vCT for providing a reasonable estimate of the “dose of the day”, and thus facilitating the process of ART for lung cancer.

Beam angle optimization using angular dependency of range variation assessed via water equivalent path length (WEPL) calculation for head and neck proton therapy

PURPOSE

To investigate angular sensitivity of proton range variation due to anatomic change in patients and patient setup error via water equivalent path length (WEPL) calculations.

METHODS

Proton range was estimated by calculating WEPL to the distal edge of target volume using planning CT (pCT) and weekly scatter-corrected cone-beam CT (CBCT) images of 11 head and neck patients. Range variation was estimated as the difference between the distal WEPLs calculated on pCT and scatter-corrected CBCT (cCBCT). This WEPL analysis was performed every five degrees ipsilaterally to the target. Statistics of the distal WEPL difference were calculated over the distal area to compare between different beam angles. Physician-defined contours were used for the WEPL calculation on both pCT and cCBCT, not considering local deformation of target volume. It was also tested if a couch kick (10°) can mitigate the range variation due to anatomic change and patient setup error.

RESULTS

For most of the patients considered, median, 75% quantile, and 95% quantile of the distal WEPL difference were largest for posterior oblique angles, indicating a higher chance of overdosing normal tissues at distal edge with these angles. Using a couch kick resulted in decrease in the WEPL difference for some posterior oblique angles.

CONCLUSIONS

It was demonstrated that the WEPL change has angular dependency for the cohort of head and neck cancer patients. Selecting beam configuration robust to anatomic change in patient and patient setup error may improve the treatment outcome of head and neck proton therapy.

Evaluation of an Open Source Registration Package for Automatic Contour Propagation in Online Adaptive Intensity-Modulated Proton Therapy of Prostate Cancer

Objective: Our goal was to investigate the performance of an open source deformable image registration package, elastix, for fast and robust contour propagation in the context of online-adaptive intensity-modulated proton therapy (IMPT) for prostate cancer.

Methods: A planning and 7–10 repeat CT scans were available of 18 prostate cancer patients. Automatic contour propagation of repeat CT scans was performed using elastix and compared with manual delineations in terms of geometric accuracy and runtime. Dosimetric accuracy was quantified by generating IMPT plans using the propagated contours expanded with a 2 mm (prostate) and 3.5 mm margin (seminal vesicles and lymph nodes) and calculating dosimetric coverage based on the manual delineation. A coverage of V_95% ≥ 98% (at least 98% of the target volumes receive at least 95% of the prescribed dose) was considered clinically acceptable.

Results: Contour propagation runtime varied between 3 and 30 s for different registration settings. For the fastest setting, 83 in 93 (89.2%), 73 in 93 (78.5%), and 91 in 93 (97.9%) registrations yielded clinically acceptable dosimetric coverage of the prostate, seminal vesicles, and lymph nodes, respectively. For the prostate, seminal vesicles, and lymph nodes the Dice Similarity Coefficient (DSC) was 0.87 ± 0.05, 0.63 ± 0.18, and 0.89 ± 0.03 and the mean surface distance (MSD) was 1.4 ± 0.5 mm, 2.0 ± 1.2 mm, and 1.5 ± 0.4 mm, respectively.

Conclusion: With a dosimetric success rate of 78.5–97.9%, this software may facilitate online adaptive IMPT of prostate cancer using a fast, free and open implementation.

CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation

In presence of inter-fractional anatomical changes, clinical benefits are anticipated from image-guided adaptive radiotherapy. Nowadays, cone-beam CT (CBCT) imaging is mostly utilized during pre-treatment imaging for position verification. Due to various artifacts, image quality is typically not sufficient for photon or proton dose calculation, thus demanding accurate CBCT correction, as potentially provided by deep learning techniques. This work aimed at investigating the feasibility of utilizing a cycle-consistent generative adversarial network (cycleGAN) for prostate CBCT correction using unpaired training. Thirty-three patients were included. The network was trained to translate uncorrected, original CBCT images (CBCT_org) into planning CT equivalent images (CBCT_cycleGAN). HU accuracy was determined by comparison to a previously validated CBCT correction technique (CBCT_cor). Dosimetric accuracy was inferred for volumetric-modulated arc photon therapy (VMAT) and opposing single-field uniform dose (OSFUD) proton plans, optimized on CBCT_cor and recalculated on CBCT_cycleGAN. Single-sided SFUD proton plans were utilized to assess proton range accuracy. The mean HU error of CBCT_cycleGAN with respect to CBCT_cor decreased from 24 HU for CBCT_org to  -6 HU. Dose calculation accuracy was high for VMAT, with average pass-rates of 100%/89% for a 2%/1% dose difference criterion. For proton OSFUD plans, the average pass-rate for a 2% dose difference criterion was 80%. Using a (2%, 2 mm) gamma criterion, the pass-rate was 96%. 93% of all analyzed SFUD profiles had a range agreement better than 3 mm. CBCT correction time was reduced from 6-10 min for CBCT_cor to 10 s for CBCT_cycleGAN. Our study demonstrated the feasibility of utilizing a cycleGAN for CBCT correction, achieving high dose calculation accuracy for VMAT. For proton therapy, further improvements may be required. Due to unpaired training, the approach does not rely on anatomically consistent training data or potentially inaccurate deformable image registration. The substantial speed-up for CBCT correction renders the method particularly interesting for adaptive radiotherapy.

Characterization of the Megavoltage Cone-Beam Computed Tomography (MV-CBCT) System on HalcyonTM for IGRT: Image Quality Benchmark, Clinical Performance, and Organ Doses

Purpose: The Varian Halcyon includes an ultrafast 6 MV flattening filter free (FFF) cone-beam computed tomography (MV-CBCT). Although a kV-CBCT add-on is available, in the basic configuration MV is used for image guided radiotherapy (IGRT). We characterized the MV-CBCT imager in terms of reproducibility, linearity, field of view (FOV) dependence, detectability of soft-tissue, and the effect of metal implants. The performance of the MV-CBCT in the clinic, including resulting dose to organs, is also discussed herein.

Methods: A Gammex phantom was scanned using a Halcyon MV-CBCT and a 120 kVp Siemens Definition Edge CT. Mean and standard deviation of Hounsfield Units (HUs) for different electron density relative to water (ρeW) inserts were extracted. Doses to clinical patients due to MV-CBCT are calculated within Eclipse during treatment planning.

Results: A stable and near-linear HU-to-ρeW curve was obtained using the MV-CBCT. As the scan length increased from 10 to 28cm, the linearity of curve improved while the mean HUs decreased by 30%. All soft tissue inserts in the Gammex phantom were distinguishable. A crescent artifact affected HU measurements by up to 40 HUs. Soft-tissue contrast was sufficient for clinical online image-guidance in the low dose (5 MU) mode. Mean doses per fraction to organs-at-risk (OARs) were as high as 6 cGy for head and neck, 5 cGy for breast, and 4 cGy for pelvis patients. Metal rods did not affect HU values or introduce noticeable artifacts.

Conclusions: Halcyon's MV-CBCT has sufficient soft tissue contrast for IGRT and lacks metal-induced artifacts. Even though the absolute HU values vary with phantom size and scanning length, the HU-to-ρeW conversions are linear and stable day-to-day. In clinical cases, highest tissue doses from MV-CBCT ranged from 2-7cGy per fraction for various treatment sites, which could be significant for some organs at risk. Dose to out-of-treatment-field organs can be limited by reducing the scan length definition during planning and using the low dose mode. The high quality imaging mode did not provide material advantages over the low dose mode. Adequate IGRT was successfully delivered to multiple tumor sites using MV-CBCT.

The Use of Radiation Therapy for the Treatment of Malignant Pleural Mesothelioma: Expert Opinion from the National Cancer Institute Thoracic Malignancy Steering Committee, International Association for the Study of Lung Cancer, and Mesothelioma Applied Research Foundation

INTRODUCTION

Detailed guidelines regarding the use of radiation therapy for malignant pleural mesothelioma (MPM) are currently lacking because of the rarity of the disease, the wide spectrum of clinical presentations, and the paucity of high-level data on individual treatment approaches.

METHODS

In March 2017, a multidisciplinary meeting of mesothelioma experts was cosponsored by the U.S. National Cancer Institute, International Association for the Study of Lung Cancer Research, and Mesothelioma Applied Research Foundation. Among the outcomes of this conference was the foundation of detailed, multidisciplinary consensus guidelines.

RESULTS

Here we present consensus recommendations on the use of radiation therapy for MPM in three discrete scenarios: (1) hemithoracic radiation therapy to be used before or after extrapleural pneumonectomy; (2) hemithoracic radiation to be used as an adjuvant to lung-sparing procedures (i.e., without pneumonectomy); and (3) palliative radiation therapy for focal symptoms caused by the disease. We discuss appropriate simulation techniques, treatment volumes, dose fractionation regimens, and normal tissue constraints. We also assess the role of particle beam therapy, specifically, proton beam therapy, for MPM.

CONCLUSION

The recommendations provided in this consensus statement should serve as important guidelines for developing future clinical trials of treatment approaches for MPM.

Quantification of global lung inflammation using volumetric 18F-FDG PET/CT parameters in locally advanced non-small-cell lung cancer patients treated with concurrent chemoradiotherapy: a comparison of photon and proton radiation therapy

INTRODUCTION

Radiation pneumonitis is a major dose-limiting complication in thoracic radiation therapy (RT) and presents clinically in the first few months after RT. We evaluated the feasibility of quantifying pulmonary parenchymal glycolysis (PG) as a surrogate of global lung inflammation and radiation-induced pulmonary toxicity using a novel semiautomatic lung segmentation technique in non-small-cell lung cancer (NSCLC) patients and compared PG in patients treated with photon or proton RT.

PATIENTS AND METHODS

We evaluated 18 consecutive locally advanced NSCLC patients who underwent pretreatment and post-treatment F-FDG PET/CT treated with definitive (median: 66.6 Gy; 1.8 Gy fractions) photon or proton RT between 2010 and 2014. Lung volume segmentation was conducted using 3D Slicer by performing simple thresholding. Pulmonary PG was calculated by summing F-FDG uptake in the whole lung.

RESULTS

In nine patients treated with photon RT, significant increases in PG in both ipsilateral (mean difference: 1400±510; P=0.02) and contralateral (mean difference: 1200±450; P=0.03) lungs were noted. In nine patients treated with proton therapy, no increase in pulmonary PG was observed in either the ipsilateral (P=0.30) or contralateral lung (P=0.98).

CONCLUSION

We observed a significant increase in global lung inflammation bilaterally as measured by quantification of PG. However, no significant change in global lung inflammation was noted after proton therapy. Future larger studies are needed to determine whether this difference correlates with lower risks of radiation pneumonitis in NSCLC patients treated with proton therapy.