Deformable image registration uncertainty for inter-fractional dose accumulation of lung cancer proton therapy

Quantification of deformable image registration uncertainties for dose accumulation on head and neck cancer proton treatments

PURPOSE

Head and neck cancer (HNC) patients in radiotherapy require adaptive treatment plans due to anatomical changes. Deformable image registration (DIR) is used in adaptive radiotherapy, e.g. for deformable dose accumulation (DDA). However, DIR's ill-posedness necessitates addressing uncertainties, often overlooked in clinical implementations. DIR's further clinical implementation is hindered by missing quantitative commissioning and quality assurance tools. This study evaluates one pathway for more quantitative DDA uncertainties.

METHODS

For five HNC patients, each with multiple repeated CTs acquired during treatment, a simultaneous-integrated boost (SIB) plan was optimized. Recalculated doses were warped individually using multiple DIRs from repeated to reference CTs, and voxel-by-voxel dose ranges determined an error-bar for DDA. Followed by evaluating, a previously proposed early-stage DDA uncertainty estimation method tested for lung cancer, which combines geometric DIR uncertainties, dose gradients and their directional dependence, in the context of HNC.

RESULTS

Applying multiple DIRs show dose differences, pronounced in high dose gradient regions. The patient with largest anatomical changes (-13.1 % in ROI body volume), exhibited 33 % maximum uncertainty in contralateral parotid, with 54 % of voxels presenting an uncertainty >5 %. Accumulation over multiple CTs partially mitigated uncertainties. The estimation approach predicted 92.6 % of voxels within ±5 % to the reference dose uncertainty across all patients.

CONCLUSIONS

DIR variations impact accumulated doses, emphasizing DDA uncertainty quantification's importance for HNC patients. Multiple DIR dose warping aids in quantifying DDA uncertainties. An estimation approach previously described for lung cancer was successfully validated for HNC, for SIB plans, presenting different dose gradients, and for accumulated treatments.

Multi-centre evaluation of variation in cumulative dose assessment in reirradiation scenarios

BACKGROUND AND PURPOSE

Safe reirradiation relies on assessment of cumulative doses to organs at risk (OARs) across multiple treatments. Different clinical pathways can result in inconsistent estimates. Here, we quantified the consistency of cumulative dose to OARs across multi-centre clinical pathways.

MATERIAL AND METHODS

We provided DICOM planning CT, structures and doses for two reirradiation cases: head & neck (HN) and lung. Participants followed their standard pathway to assess the cumulative physical and EQD2 doses (with provided α/β values), and submitted DVH metrics and a description of their pathways. Participants could also submit physical dose distributions from Course 1 mapped onto the CT of Course 2 using their best available tools. To assess isolated impact of image registrations, a single observer accumulated each submitted spatially mapped physical dose for every participating centre.

RESULTS

Cumulative dose assessment was performed by 24 participants. Pathways included rigid (n = 15), or deformable (n = 5) image registration-based 3D dose summation, visual inspection of isodose line contours (n = 1), or summation of dose metrics extracted from each course (n = 3). Largest variations were observed in near-maximum cumulative doses (25.4 - 41.8 Gy for HN, 2.4 - 33.8 Gy for lung OARs), with lower variations in volume/dose metrics to large organs. A standardised process involving spatial mapping of the first course dose to the second course CT followed by summation improved consistency for most near-maximum dose metrics in both cases.

CONCLUSION

Large variations highlight the uncertainty in reporting cumulative doses in reirradiation scenarios, with implications for outcome analysis and understanding of published doses. Using a standardised workflow potentially including spatially mapped doses improves consistency in determination of accumulated dose in reirradiation scenarios.

Deformable image registration for composite planned doses during adaptive radiation therapy

INTRODUCTION

Some patients have significant anatomic changes during radiotherapy, necessitating an adaptive repeat CT-simulation and re-planning. This yields two unique planning datasets that introduce uncertainty into total dose records. This study explored the impact of using deformable image registration (DIR) to spatially align repeat CT-simulation images and calculate total planned dose distributions.

MATERIALS & METHODS

Data from 5 head-and-neck, 5 lung, and 5 sarcoma patients who had unanticipated re-planning during radiotherapy were analyzed in a treatment planning system (RayStation v6.1 RaySearch Laboratories). Total planned doses to normal tissues were calculated using two methods and the previously generated manual contours defined on each CT. The first method, termed 'parameter addition', simply sums the relevant DVH metrics from the initial and re-planned distributions without spatially registering the CTs. The second, termed 'dose accumulation', uses a validated hybrid contour/intensity-based DIR algorithm to deform initial CT and dose distribution onto the repeat CT and re-planning dose distribution. DVH metrics from the summed distribution on the repeat CT are then calculated. Dose differences for organs-at-risk between parameter addition and dose accumulation ≥100 cGy were assumed to be clinically relevant. To elucidate whether relevant differences were due to registration accuracy or contouring variability between CTs, the analysis was repeated using contours on the first CT and the same contours deformed to the repeat CT with DIR.

RESULTS

For all patients, high overall DIR accuracy was verified visually (qualitatively) and numerically (quantitatively) using image similarity and contour-based metrics. All head-and-neck and lung patients, and one sarcoma patient (11 of 15 total) had dose differences between parameter addition and dose accumulation ≥100 cGy, with absolute mean differences of 160 cGy (range 101-436 cGy) seen in 41 of 205 total DVH criteria. In 22 of these 41 criteria, these differences were attributed to contouring variability between CTs. After correcting for contouring variations using DIR, the mean absolute differences in 7 of these 22 criteria with a relevant result (across 6 patients) was 146 cGy (range 100-502 cGy). In only 4 DVH criteria, the DIR mapped contours had higher variations than the original contours. One lung patient had a DVH criteria exceeding the clinical dose constraint by 125 cGy with parameter addition, and with accurate DIR and dose accumulation, the criteria was actually 97 cGy lower than the constraint.

CONCLUSIONS

The use of DIR to generate total planned dose records revealed substantial dose differences in most cases compared to commonly used clinical methods (i.e. parameter addition), and altered the planned acceptance criteria in a minority. DIR is recommended to be used for future adaptive re-plans to generate total planned dose records and facilitate accurate re-contouring. More accurate dose records may also improve our understanding of clinical outcomes.

Characteristics of breathing-adapted gating using surface guidance for use in particle therapy: A phantom-based end-to-end test from CT simulation to dose delivery

To account for intra-fractional tumor motion during dose delivery in radiotherapy, various treatment strategies are clinically implemented such as breathing-adapted gating and irradiating the tumor during specific breathing phases. In this work, we present a comprehensive phantom-based end-to-end test of breathing-adapted gating utilizing surface guidance for use in particle therapy. A commercial dynamic thorax phantom was used to reproduce regular and irregular breathing patterns recorded by the GateRT respiratory monitoring system. The amplitudes and periods of recorded breathing patterns were analysed and compared to planned patterns (ground-truth). In addition, the mean absolute deviations (MAD) and Pearson correlation coefficients (PCC) between the measurements and ground-truth were assessed. Measurements of gated and non-gated irradiations were also analysed with respect to dosimetry and geometry, and compared to treatment planning system (TPS). Further, the latency time of beam on/off was evaluated. Compared to the ground-truth, measurements performed with GateRT showed amplitude differences between 0.03 ± 0.02 mm and 0.26 ± 0.03 mm for regular and irregular breathing patterns, whilst periods of both breathing patterns ranged with a standard deviation between 10 and 190 ms. Furthermore, the GateRT software precisely acquired breathing patterns with a maximum MAD of 0.30 ± 0.23 mm. The PCC constantly ranged between 0.998 and 1.000. Comparisons between TPS and measured dose profiles indicated absolute mean dose deviations within institutional tolerances of ±5%. Geometrical beam characteristics also varied within our institutional tolerances of 1.5 mm. The overall time delays were <60 ms and thus within both recommended tolerances published by ESTRO and AAPM of 200 and 100 ms, respectively. In this study, a non-invasive optical surface-guided workflow including image acquisition, treatment planning, patient positioning and gated irradiation at an ion-beam gantry was investigated, and shown to be clinically viable. Based on phantom measurements, our results show a clinically-appropriate spatial, temporal, and dosimetric accuracy when using surface guidance in the clinical setting, and the results comply with international and institutional guidelines and tolerances.

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Objective.Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics.Approach.Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty.Main results.The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods anexpected calibration errorof less than 3%. Additionally, theprobabilisticly accumulated dose volume histograms(DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining.Significance.Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Patient-specific quality assurance for deformable IMRT/IMPT dose accumulation: Proposition and validation of energy conservation based validation criterion

BACKGROUND

Deformable image registration (DIR)-based dose accumulation (DDA) is regularly used in adaptive radiotherapy research. However, the applicability and reliability of DDA for direct clinical usage are still being debated. One primary concern is the validity of DDA, particularly for scenarios with substantial anatomical changes, for which energy-conservation problems were observed in conceptual studies.

PURPOSE

We present and validate an energy-conservation (EC)-based DDA validation workflow and further investigate its usefulness for actual patient data, specifically for lung cancer cases.

METHODS

For five non-small cell lung cancer (NSCLC) patients, DDA based on five selective DIR methods were calculated for five different treatment plans, which include one intensity-modulated photon therapy (IMRT), two intensity-modulated proton therapy (IMPT), and two combined proton-photon therapy (CPPT) plans. All plans were optimized on the planning CT (planCT) acquired in deep inspiration breath-hold (DIBH) and were re-optimized on the repeated DIBH CTs of three later fractions. The resulting fractional doses were warped back to the planCT using each DIR. An EC-based validation of the accumulation process was implemented and applied to all DDA results. Correlations between relative organ mass/volume variations and the extent of EC violation were then studied using Bayesian linear regression (BLR).

RESULTS

For most OARs, EC violation within 10% is observed. However, for the PTVs and GTVs with substantial regression, severe overestimation of the fractional energy was found regardless of treatment type and applied DIR method. BLR results show that EC violation is linearly correlated to the relative mass variation (R^2 > 0.95) and volume variation (R^2 > 0.60).

CONCLUSION

DDA results should be used with caution in regions with high mass/volume variation for intensity-based DIRs. EC-based validation is a useful approach to provide patient-specific quality assurance of the validity of DDA in radiotherapy.

Deep learning-based lung image registration: A review

Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.

Dosimetric comparison of autocontouring techniques for online adaptive proton therapy

Objective.Anatomical and daily set-up uncertainties impede high precision delivery of proton therapy. With online adaptation, the daily plan is reoptimized on an image taken shortly before the treatment, reducing these uncertainties and, hence, allowing a more accurate delivery. This reoptimization requires target and organs-at-risk (OAR) contours on the daily image, which need to be delineated automatically since manual contouring is too slow. Whereas multiple methods for autocontouring exist, none of them are fully accurate, which affects the daily dose. This work aims to quantify the magnitude of this dosimetric effect for four contouring techniques.Approach.Plans reoptimized on automatic contours are compared with plans reoptimized on manual contours. The methods include rigid and deformable registration (DIR), deep-learning based segmentation and patient-specific segmentation.Main results.It was found that independently of the contouring method, the dosimetric influence of usingautomaticOARcontoursis small (<5% prescribed dose in most cases), with DIR yielding the best results. Contrarily, the dosimetric effect of using theautomatic target contourwas larger (>5% prescribed dose in most cases), indicating that manual verification of that contour remains necessary. However, when compared to non-adaptive therapy, the dose differences caused by automatically contouring the target were small and target coverage was improved, especially for DIR.Significance.The results show that manual adjustment of OARs is rarely necessary and that several autocontouring techniques are directly usable. Contrarily, manual adjustment of the target is important. This allows prioritizing tasks during time-critical online adaptive proton therapy and therefore supports its further clinical implementation.

Comparison of 3D and 4D robustly optimized proton treatment plans for non-small cell lung cancer patients with tumour motion amplitudes larger than 5 mm

Background and purpose

There is no consensus about an ideal robust optimization (RO) strategy for proton therapy of targets with large intrafractional motion. We investigated the plan robustness of 3D and different 4D RO strategies.

Materials and methods

For eight non-small cell lung cancer patients with clinical target volume (CTV) motion >5 mm, different RO approaches were investigated: 3DRO considering the average CT (AvgCT) with a target density override, 4DRO considering three/all 4DCT phases, and 4DRO considering the AvgCT and three/all 4DCT phases. Robustness against setup/range errors, interplay effects based on breathing and machine log file data for deliveries with/without rescanning, and interfractional anatomical changes were analyzed for target coverage and OAR sparing.

Results

All nominal plans fulfilled the clinical requirements with individual CTV coverage differences <2pp; 4DRO without AvgCT generated the most conformal dose distributions. Robustness against setup/range errors was best for 4DRO with AvgCT (18% more passed error scenarios than 3DRO). Interplay effects caused fraction-wise median CTV coverage loss of 3pp and missed maximum dose constraints for heart and esophagus in 18% of scenarios. CTV coverage and OAR sparing fulfilled requirements in all cases when accumulating four interplay scenarios. Interfractional changes caused less target misses for RO with AvgCT compared to 4DRO without AvgCT (≤42%/33% vs. ≥56%/44% failed single/accumulated scenarios).

Conclusions

All RO strategies provided acceptable plans with equally low robustness against interplay effects demanding other mitigation than rescanning to ensure fraction-wise target coverage. 4DRO considering three phases and the AvgCT provided best compromise on planning effort and robustness.

Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients

Background and purpose

Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients.

Materials and methods

Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs.

Results

There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods.

Conclusion

Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.

Parameter based 4D dose calculations for proton therapy

Background and purpose Retrospective log file-based analysis provides the actual dose delivered based on the patient's breathing and the daily beam-delivery dynamics. To predict the motion sensitivity of the treatment plan on a patient-specific basis before treatment start a prospective tool is required. Such a parameter-based tool has been investigated with the aim to be used in clinical routine. Materials and Methods 4D dose calculations (4DDC) were performed for seven cancer patients with small breathing motion treated with scanned pulsed proton beams. Validation of the parameter-based 4DDC (p-4DDC) method was performed with an anthropomorphic phantom and patient data employing measurements and a log file-based 4DDC tool. The dose volume histogram parameters (Dx%) were investigated for the target and the organs at risk, compared to static and the file-based approach. Results The difference between the measured and the p-4DDC dose was within the deviation of the measurements. The maximum deviation was 0.4Gy. For the planning target volume D98% varied up to 15% compared to the static scenario, while the results from the log file and p-4DDC agreed within 2%. For the liver patients, D33%liver deviated up to 35% compared to static and 10% comparing the two 4DDC tools, while for the pancreas patients the D1%stomach varied up to 45% and 11%, respectively. Conclusion The results showed that p-4DDC could be used prospectively. The next step will be the clinical implementation of the p-4DDC tool, which can support a decision to either adapt the treatment plan or apply motion mitigation strategies.

Applicability and usage of dose mapping/accumulation in radiotherapy

Dose mapping/accumulation (DMA) is a topic in radiotherapy (RT) for years, but has not yet found its widespread way into clinical RT routine. During the ESTRO Physics workshop 2021 on "commissioning and quality assurance of deformable image registration (DIR) for current and future RT applications", we built a working group on DMA from which we present the results of our discussions in this article. Our aim in this manuscript is to shed light on the current situation of DMA in RT and to highlight the issues that hinder consciously integrating it into clinical RT routine. As a first outcome of our discussions, we present a scheme where representative RT use cases are positioned, considering expected anatomical variations and the impact of dose mapping uncertainties on patient safety, which we have named the DMA landscape (DMAL). This tool is useful for future reference when DMA applications get closer to clinical day-to-day use. Secondly, we discussed current challenges, lightly touching on first-order effects (related to the impact of DIR uncertainties in dose mapping), and focusing in detail on second-order effects often dismissed in the current literature (as resampling and interpolation, quality assurance considerations, and radiobiological issues). Finally, we developed recommendations, and guidelines for vendors and users. Our main point include: Strive for context-driven DIR (by considering their impact on clinical decisions/judgements) rather than perfect DIR; be conscious of the limitations of the implemented DIR algorithm; and consider when dose mapping (with properly quantified uncertainties) is a better alternative than no mapping.

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 D_50% (ΔD_50%) for the clinical target volume (CTV) and the planning target volume (PTV) was below 2%, while the only outlier was observed for ΔD_98% 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 ΔD_2% 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.

Liver SBRT dose accumulation to assess the impact of anatomic variations on normal tissue doses and toxicity in patients treated with concurrent sorafenib

BACKGROUND AND PURPOSE

Unexpected liver volume reductions occurred during trials of liver SBRT and concurrent sorafenib. The aims were to accumulate liver SBRT doses to assess the impact of these anatomic variations on normal tissue dose parameters and toxicity.

MATERIALS AND METHODS

Thirty-two patients with hepatocellular carcinoma (HCC) or metastases treated on trials of liver SBRT (30-57 Gy, 6 fractions) and concurrent sorafenib were analyzed. SBRT doses were accumulated using biomechanical deformable registration of daily cone-beam CT. Dose deviations (accumulated-planned) for normal tissues were compared for patients with liver volume reductions > 100 cc versus stable volumes, and accumulated doses were reported for three patients with grade 3-5 luminal gastrointestinal toxicities.

RESULTS

Patients with reduced (N = 12) liver volumes had larger mean deviations of 0.4-1.3 Gy in normal tissues, versus -0.2-0.4 Gy for stable cases (N = 20), P > 0.05. Deviations > 5% of the prescribed dose occurred in both groups. Two HCC patients with toxicities to small and large bowel had liver volume reductions and deviations to the maximum dose of 4% (accumulated 36.9 Gy) and 3% (accumulated 33.4 Gy) to these organs respectively. Another HCC patient with a toxicity of unknown location plus tumor rupture, had stable liver volumes and deviations to luminal organs of -6% to 4.5% (accumulated < 30.5 Gy).

CONCLUSION

Liver volume reductions during SBRT and concurrent sorafenib were associated with larger increases in accumulated dose to normal tissues versus stable liver volumes. These dosimetric changes may have further contributed to toxicities in HCC patients who have higher baseline risks.

Inter- and intrafractional 4D dose accumulation for evaluating ΔNTCP robustness in lung cancer

BACKGROUND AND PURPOSE

Model-based selection of proton therapy patients relies on a predefined reduction in normal tissue complication probability (NTCP) with respect to photon therapy. The decision is necessarily made based on the treatment plan, but NTCP can be affected when the delivered treatment deviates from the plan due to delivery inaccuracies. Especially for proton therapy of lung cancer, this can be important because of tissue density changes and, with pencil beam scanning, the interplay effect between the proton beam and breathing motion.

MATERIALS AND METHODS

In this work, we verified whether the expected benefit of proton therapy is retained despite delivery inaccuracies by reconstructing the delivered treatment using log-file based dose reconstruction and inter- and intrafractional accumulation. Additionally, the importance of two uncertain parameters for treatment reconstruction, namely deformable image registration (DIR) algorithm and α/β ratio, was assessed.

RESULTS

The expected benefit or proton therapy was confirmed in 97% of all studied cases, despite regular differences up to 2 percent point (p.p.) NTCP between the delivered and planned treatments. The choice of DIR algorithm affected NTCP up to 1.6 p.p., an order of magnitude higher than the effect of α/β ratio.

CONCLUSION

For the patient population and treatment technique employed, the predicted clinical benefit for patients selected for proton therapy was confirmed for 97.0% percent of all cases, although the NTCP based proton selection was subject to 2 p.p. variations due to delivery inaccuracies.

Interfractional dose accumulation for MR-guided liver SBRT: Variation among algorithms is highly patient- and fraction-dependent

BACKGROUND AND PURPOSE

Daily plan adaptations could take the dose delivered in previous fractions into account. Due to high dose delivered per fraction, low number of fractions, steep dose gradients, and large interfractional organ deformations, this might be particularly important for liver SBRT. This study investigates inter-algorithm variation of interfractional dose accumulation for MR-guided liver SBRT.

MATERIALS AND METHODS

We assessed 27 consecutive MR-guided liver SBRT treatments of 67.5 Gy in three (n = 15) or 50 Gy in five fractions (n = 12), both prescribed to the GTV. We calculated fraction doses on daily patient anatomy, warped these doses to the simulation MRI using seven different algorithms, and accumulated the warped doses. Thus, we obtained differences in planned doses and warped or accumulated doses for each algorithm. This enabled us to calculate the inter-algorithm variations in warped doses per fraction and in accumulated doses per treatment course.

RESULTS

The four intensity-based algorithms were more consistent with planned PTV dose than affine or contour-based algorithms. The mean (range) variation of the dose difference for PTV D_95% due to dose warping by these intensity-based algorithms was 10.4 percentage points (0.3 to 43.7) between fractions and 8.6 (0.3 to 24.9) between accumulated treatment doses. As seen by these ranges, the variation was very dependent on the patient and the fraction being analyzed. Nevertheless, no correlations between patient or plan characteristics on the one hand and inter-algorithm dose warping variation on the other hand was found.

CONCLUSION

Inter-algorithm dose accumulation variation is highly patient- and fraction-dependent for MR-guided liver SBRT. We advise against trusting a single algorithm for dose accumulation in liver SBRT.

The impact of organ motion and the appliance of mitigation strategies on the effectiveness of hypoxia-guided proton therapy for non-small cell lung cancer

BACKGROUND AND PURPOSE

To investigate the impact of organ motion on hypoxia-guided proton therapy treatments for non-small cell lung cancer (NSCLC) patients.

MATERIALS AND METHODS

Hypoxia PET and 4D imaging data of six NSCLC patients were used to simulate hypoxia-guided proton therapy with different motion mitigation strategies including rescanning, breath-hold, respiratory gating and tumour tracking. Motion-induced dose degradation was estimated for treatment plans with dose painting of hypoxic tumour sub-volumes at escalated dose levels. Tumour control probability (TCP) and dosimetry indices were assessed to weigh the clinical benefit of dose escalation and motion mitigation. In addition, the difference in normal tissue complication probability (NTCP) between escalated proton and photon VMAT treatments has been assessed.

RESULTS

Motion-induced dose degradation was found for target coverage (CTV V_95% up to -4%) and quality of the dose-escalation-by-contour (Q_RMS up to 6%) as a function of motion amplitude and amount of dose escalation. The TCP benefit coming from dose escalation (+4-13%) outweighs the motion-induced losses (<2%). Significant average NTCP reductions of dose-escalated proton plans were found for lungs (-14%), oesophagus (-10%) and heart (-16%) compared to conventional VMAT plans. The best plan dosimetry was obtained with breath hold and respiratory gating with rescanning.

CONCLUSION

NSCLC affected by hypoxia appears to be a prime target for proton therapy which, by dose-escalation, allows to mitigate hypoxia-induced radio-resistance despite the sensitivity to organ motion. Furthermore, substantial reduction in normal tissue toxicity can be expected compared to conventional VMAT. Accessibility and standardization of hypoxia imaging and clinical trials are necessary to confirm these findings in a clinical setting.

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy

BACKGROUND

Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations.

PURPOSE

In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible.

METHODS

A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals.

RESULTS

4D-sCTs resulted in average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ± 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% ± 3.2% (single phase) and 94.4% ± 2.1% (average). The clinical target volume showed high agreement in D₉₈ between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues.

CONCLUSION

In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

Emergence of MR-Linac in Radiation Oncology: Successes and Challenges of Riding on the MRgRT Bandwagon

The special issue of JCM on “Advances of MRI in Radiation Oncology” provides a unique forum for scientific literature related to MR imaging in radiation oncology. This issue covered many aspects, such as MR technology, motion management, economics, soft-tissue–air interface issues, and disease sites such as the pancreas, spine, sarcoma, prostate, head and neck, and rectum from both camps—the Unity and MRIdian systems. This paper provides additional information on the success and challenges of the two systems. A challenging aspect of this technology is low throughput and the monumental task of education and training that hinders its use for the majority of therapy centers. Additionally, the cost of this technology is too high for most institutions, and hence widespread use is still limited. This article highlights some of the difficulties and how to resolve them.

Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept

PURPOSE

To demonstrate predictive anatomical modelling for improving the clinical workflow of adaptive intensity-modulated proton therapy (IMPT) for head and neck cancer.

METHODS

10 radiotherapy patients with nasopharyngeal cancer were included in this retrospective study. Each patient had a planning CT, weekly verification CTs during radiotherapy and predicted weekly CTs from our anatomical model. Predicted CTs were used to create predicted adaptive plans in advance with the aim of maintaining clinically acceptable dosimetry. Adaption was triggered when the increase in mean dose (Dmean) to the parotid glands exceeded 3 Gy(RBE). We compared the accumulated dose of two adaptive IMPT strategies: 1) Predicted plan adaption: One adaptive plan per patient was optimised on a predicted CT triggered by replan criteria. 2) Standard replan: One adaptive plan was created reactively in response to the triggering weekly CT.

RESULTS

Statistical analysis demonstrates that the accumulated dose differences between two adaptive strategies are not significant (p > 0.05) for CTVs and OARs. We observed no meaningful differences in D95 between the accumulated dose and the planned dose for the CTVs, with mean differences to the high-risk CTV of -1.20 %, -1.23 % and -1.25 % for no adaption, standard and predicted plan adaption, respectively. The accumulated parotid Dmean using predicted plan adaption is within 3 Gy(RBE) of the planned dose and 0.31 Gy(RBE) lower than the standard replan approach on average.

CONCLUSION

Prediction-based replanning could potentially enable adaptive therapy to be delivered without treatment gaps or sub-optimal fractions, as can occur during a standard replanning strategy, though the benefit of using predicted plan adaption over the standard replan was not shown to be statistically significant with respect to accumulated dose in this study. Nonetheless, a predictive replan approach can offer advantages in improving clinical workflow efficiency.

Ultra-fast pencil beam scanning proton therapy for locally advanced non-small-cell lung cancers: field delivery within a single breath-hold

PURPOSE

The use of motion mitigation techniques such as breath-hold can reduce the dosimetric uncertainty of lung cancer proton therapy. We studied the feasibility of pencil beam scanning (PBS) proton therapy field delivery within a single breath-hold at PSI's Gantry 2.

METHODS

In PBS proton therapy, the delivery time for a field is determined by the beam-on time and the dead time between proton spots (the time required to change the energy and/or lateral position). We studied ways to reduce beam-on and lateral scanning time, without sacrificing dosimetric plan quality, aiming at a single field delivery time of 15 seconds at maximum. We tested this approach on 10 lung cases with varying target volumes. To reduce the beam-on time, we increased the beam current at the isocenter by developing new beam optics for PSI's PROSCAN beamline and Gantry 2. To reduce the dead time between the spots, we used spot-reduced plan optimization.

RESULTS

We found that it is possible to achieve conventional fractionated (2 Gy(RBE)/fraction) and hypofractionated (6 Gy(RBE)/fraction) field delivery times within a single breath-hold (<15 sec) for a variety non-small-cell lung cancer cases.

CONCLUSION

In summary, the combination of spot reduction and improved beam line transmission is a promising approach for the treatment of mobile tumours within clinically achievable breath-hold durations.

The dose accumulation and the impact of deformable image registration on dose reporting parameters in a moving patient undergoing proton radiotherapy

INTRODUCTION

Potential changes in patient anatomy during proton radiotherapy may lead to a deviation of the delivered dose. A dose estimate can be computed through a deformable image registration (DIR) driven dose accumulation. The present study evaluates the accumulated dose uncertainties in a patient subject to an inadvertent breathing associated motion.

MATERIALS AND METHODS

A virtual lung tumour was inserted into a pair of single participant landmark annotated computed tomography images depicting opposite breathing phases, with the deep inspiration breath-hold the planning reference and the exhale the off-reference geometry. A novel Monte Carlo N-Particle, Version 6 (MCNP6) dose engine was developed, validated and used in treatment plan optimization. Three DIR methods were compared and used to transfer the exhale simulated dose to the reference geometry. Dose conformity and homogeneity measures from International Committee on Radioactivity Units and Measurements (ICRU) reports 78 and 83 were evaluated on simulated dose distributions registered with different DIR algorithms.

RESULTS

The MCNP6 dose engine handled patient-like geometries in reasonable dose calculation times. All registration methods were able to align image associated landmarks to distances, comparable to voxel sizes. A moderate deterioration of ICRU measures was encountered in comparing doses in on and off-reference anatomy. There were statistically significant DIR driven differences in ICRU measures, particularly a 10% difference in the relative D_98% for planning tumour volume and in the 3 mm/3% gamma passing rate.

CONCLUSIONS

T he dose accumulation over two anatomies resulted in a DIR driven uncertainty, important in reporting the associated ICRU measures for quality assurance.

Combined proton-photon therapy for non-small cell lung cancer

PURPOSE

Advanced non-small cell lung cancer (NSCLC) is still a challenging indication for conventional photon radiotherapy. Proton therapy has the potential to improve outcomes, but proton treatment slots remain a limited resource despite an increasing number of proton therapy facilities. This work investigates the potential benefits of optimally combined proton-photon therapy delivered using a fixed horizontal proton beam line in combination with a photon Linac, which could increase accessibility to proton therapy for such a patient cohort.

MATERIALS AND METHODS

A treatment planning study has been conducted on a patient cohort of seven advanced NSCLC patients. Each patient had a planning CT and multiple repeated CTs from three different days and for different breath-holds on each day. Treatment plans for combined proton-photon therapy (CPPT) were calculated for individual patients by optimizing the combined cumulative dose on the initial planning CT only (non-adapted) as well as on each daily CT respectively (adapted). The impact of inter-fractional changes and/or breath-hold variability was then assessed on the repeat breath-hold CTs. Results were compared to plans for IMRT or IMPT alone, as well as against combined treatments assuming a proton gantry. Plan quality was assessed in terms of dosimetric, robustness and NTCP metrics.

RESULTS

Combined treatment plans improved plan quality compared to IMRT treatments, especially in regard to reductions of low and medium doses to organs at risk (OARs), which translated into lower NTCP estimates for three side effects. For most patients, combined treatments achieved results close to IMPT-only plans. Inter-fractional changes impact mainly the target coverage of combined and IMPT treatments, while OARs doses were less affected by these changes. With plan adaptation however, target coverage of combined treatments remained high even when taking variability between breath-holds into account.

CONCLUSIONS

Optimally combined proton-photon plans improve treatment plan quality compared to IMRT only, potentially reducing the risk of toxicity while also allowing to potentially increase accessibility to proton therapy for NSCLC patients. This article is protected by copyright. All rights reserved.

Imaging Strategies in Proton Therapy for Thoracic Tumors: A Mini Review

Proton beam therapy (PBT) is often more attractive for its high gradient dose distributions than other treatment modalities with external photon beams. However, in thoracic lesions treated particularly with pencil beam scanning (PBS) proton beams, several dosimetric issues are addressed. The PBS approach may lead to large hot or cold spots in dose distributions delivered to the patients, potentially affecting the tumor control and/or increasing normal tissue side effects. This delivery method particularly benefits image-guided approaches. Our paper aims at reviewing imaging strategies and their technological trends for PBT in thoracic lesions. The focus is on the use of imaging strategies in simulation, planning, positioning, adaptation, monitoring, and delivery of treatment and how changes in the anatomy of thoracic tumors are handled with the available tools and devices in PBT. Starting from bibliographic research over the past 5 years, retrieving 174 papers, major key questions, and implemented solutions were identified and discussed; the results aggregated and presented following the methodology of analysis of expert interviews.

Experimental validation of 4D log file-based proton dose reconstruction for interplay assessment considering amplitude-sorted 4DCTs

PURPOSE

The unpredictable interplay between dynamic proton therapy delivery and target motion in the thorax can lead to severe dose distortions. A fraction-wise four-dimensional (4D) dose reconstruction workflow allows for the assessment of the applied dose after patient treatment while considering the actual beam delivery sequence extracted from machine log files, the recorded breathing pattern and the geometric information from a 4D computed tomography scan (4DCT). Such an algorithm capable of accounting for amplitude-sorted 4DCTs was implemented and its accuracy as well as its sensitivity to input parameter variations was experimentally evaluated.

METHODS

An anthropomorphic thorax phantom with a movable insert containing a target surrogate and a radiochromic film was irradiated with a monoenergetic field for various 1D target motion forms (sin, sin4) and peak-to-peak amplitudes (5/10/15/20/30 mm). The measured characteristic film dose distributions were compared to the respective sections in the 4D reconstructed doses using a 2D γ-analysis (3mm, 3%); γ-pass rates were derived for different dose grid resolutions (1mm/3mm) and deformable image registrations (DIR, automatic/manual) applied during the 4D dose reconstruction process. In an additional analysis, the sensitivity of reconstructed dose distributions against potential asynchronous timing of the motion and machine log files was investigated for both a monoenergetic field and more realistic 4D robustly optimized fields by artificially introduced offsets of ± 1/5/25/50/250 ms. The resulting dose distributions with asynchronized log files were compared to those with synchronized log files by means of a 3D γ-analysis (1mm, 1%) and the evaluation of absolute dose differences.

RESULTS

The induced characteristic interplay patterns on the films were well reproduced by the 4D dose reconstruction with 2D γ-pass rates ≥95% for almost all cases with motion magnitudes ≤15 mm. In general, the 2D γ-pass rates showed a significant decrease for larger motion amplitudes and increase when using a finer dose grid resolution but were not affected by the choice of motion form (sin, sin4). There was also a trend, though not statistically significant, towards the manually defined DIR for better quality of the reconstructed dose distributions in the area imaged by the film. The 4D dose reconstruction results for the monoenergetic as well as the 4D robustly optimized fields were robust against small asynchronies between motion and machine log files of up to 5 ms, which is in the order of potential network latencies.

CONCLUSIONS

We have implemented a 4D log file-based proton dose reconstruction that accounts for amplitude-sorted 4DCTs. Its accuracy was proven to be clinically acceptable for target motion magnitudes of up to 15 mm. Particular attention should be paid to the synchronization of the log file generating systems as the reconstructed dose distribution may vary with log file asynchronies larger than those caused by realistic network delays. This article is protected by copyright. All rights reserved.

Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations

Purpose

Respiratory motion is one of the major challenges in radiotherapy. In this work, a comprehensive and clinically plausible set of 4D numerical phantoms, together with their corresponding “ground truths,” have been developed and validated for 4D radiotherapy applications.

Methods

The phantoms are based on CTs providing density information and motion from multi‐breathing‐cycle 4D Magnetic Resonance imagings (MRIs). Deformable image registration (DIR) has been utilized to extract motion fields from 4DMRIs and to establish inter‐subject correspondence by registering binary lung masks between Computer Tomography (CT) and MRI. The established correspondence is then used to warp the CT according to the 4DMRI motion. The resulting synthetic 4DCTs are called 4DCT(MRI)s. Validation of the 4DCT(MRI) workflow was conducted by directly comparing conventional 4DCTs to derived synthetic 4D images using the motion of the 4DCTs themselves (referred to as 4DCT(CT)s). Digitally reconstructed radiographs (DRRs) as well as 4D pencil beam scanned (PBS) proton dose calculations were used for validation.

Results

Based on the CT image appearance of 13 lung cancer patients and deformable motion of five volunteer 4DMRIs, synthetic 4DCT(MRI)s with a total of 871 different breathing cycles have been generated. The 4DCT(MRI)s exhibit an average superior–inferior tumor motion amplitude of 7 ± 5 mm (min: 0.5 mm, max: 22.7 mm). The relative change of the DRR image intensities of the conventional 4DCTs and the corresponding synthetic 4DCT(CT)s inside the body is smaller than 5% for at least 81% of the pixels for all studied cases. Comparison of 4D dose distributions calculated on 4DCTs and the synthetic 4DCT(CT)s using the same motion achieved similar dose distributions with an average 2%/2 mm gamma pass rate of 90.8% (min: 77.8%, max: 97.2%).

Conclusion

We developed a series of numerical 4D lung phantoms based on real imaging and motion data, which give realistic representations of both anatomy and motion scenarios and the accessible “ground truth” deformation vector fields of each 4DCT(MRI). The open‐source code and motion data allow foreseen users to generate further 4D data by themselves. These numeric 4D phantoms can be used for the development of new 4D treatment strategies, 4D dose calculations, DIR algorithm validations, as well as simulations of motion mitigation and different online image guidance techniques for both proton and photon radiation therapy.

High-dose hypofractionated pencil beam scanning carbon ion radiotherapy for lung tumors: Dosimetric impact of different spot sizes and robustness to interfractional uncertainties

PURPOSE

The robustness against setup and motion uncertainties of gated four-dimensional restricted robust optimization (4DRRO) was investigated for hypofractionated carbon ion radiotherapy (CIRT) of lung tumors.

METHODS

CIRT plans of 9 patients were optimized using 4DRRO strategy with 3 mm setup errors, 3% density errors and 3 breathing phases related to the gate window. The prescription was 60 Gy(RBE) in 4 fractions. Standard spots (SS) were compared to big spots (BS). Plans were recalculated on multiple 4DCTs acquired within 3 weeks from treatment simulation and rigidly registered with planning images using bone matching. Warped dose distributions were generated using deformable image registration and accumulated on the planning 4DCTs. Target coverage (D98%, D95% and V95%) and dose to lung were evaluated in the recalculated and accumulated dose distributions.

RESULTS

Comparable target coverage was obtained with both spot sizes (p = 0.53 for D95%). The mean lung dose increased of 0.6 Gy(RBE) with BS (p = 0.0078), still respecting the dose constraint of a 4-fraction stereotactic treatment for the risk of radiation pneumonitis. Statistically significant differences were found in the recalculated and accumulated D95% (p = 0.048 and p = 0.024), with BS showing to be more robust. Using BS, the average degradations of the D98%, D95% and V95% in the accumulated doses were -2.7%, -1.6% and -1.5%.

CONCLUSIONS

Gated 4DRRO was highly robust against setup and motion uncertainties. BS increased the dose to healthy tissues but were more robust than SS. The selected optimization settings guaranteed adequate target coverage during the simulated treatment course with acceptable risk of toxicity.

An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer

Deformable image registration (DIR) is an important component for dose accumulation and associated clinical outcome evaluation in radiotherapy. However, the resulting deformation vector field (DVF) is subject to unavoidable discrepancies when different algorithms are applied, leading to dosimetric uncertainties of the accumulated dose. We propose here an approach for proton therapy to estimate dosimetric uncertainties as a consequence of modeled or estimated DVF uncertainties. A patient-specific DVF uncertainty model was built on the first treatment fraction, by correlating the magnitude differences of five DIR results at each voxel to the magnitude of any single reference DIR. In the following fractions, only the reference DIR needs to be applied, and DVF geometric uncertainties were estimated by this model. The associated dosimetric uncertainties were then derived by considering the estimated geometric DVF uncertainty, the dose gradient of fractional recalculated dose distribution and the direction factor from the applied reference DIR of this fraction. This estimated dose uncertainty was respectively compared to the reference dose uncertainty when different DIRs were applied individually for each dose warping. This approach was validated on seven NSCLC patients, each with nine repeated CTs. The proposed model-based method is able to achieve dose uncertainty distribution on a conservative voxel-to-voxel comparison within ±5% of the prescribed dose to the 'reference' dosimetric uncertainty, for 77% of the voxels in the body and 66%-98% of voxels in investigated structures. We propose a method to estimate DIR induced uncertainties in dose accumulation for proton therapy of lung tumor treatments.

Fractional Mathematical Oncology: On the potential of non-integer order calculus applied to interdisciplinary models

Mathematical Oncology investigates cancer-related phenomena through mathematical models as comprehensive as possible. Accordingly, an interdisciplinary approach involving concepts from biology to materials science can provide a deeper understanding of biological systems pertaining the disease. In this context, fractional calculus (also referred to as non-integer order) is a branch in mathematical analysis whose tools can describe complex phenomena comprising different time and space scales. Fractional-order models may allow a better description and understanding of oncological particularities, potentially contributing to decision-making in areas of interest such as tumor evolution, early diagnosis techniques and personalized treatment therapies. By following a phenomenological (i.e. mechanistic) approach, the present study surveys and explores different aspects of Fractional Mathematical Oncology, reviewing and discussing recent developments in view of their prospective applications.