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Nikou P, Thompson A, Nisbet A, Gulliford S, McClelland J. Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment. Phys Med Biol 2024; 69:155017. [PMID: 38981595 DOI: 10.1088/1361-6560/ad611b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024]
Abstract
Objective.Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes.Approach.Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models.Main results.Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons.Significance.In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.
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Affiliation(s)
- Poppy Nikou
- University College London, London, WC1E 6AE, United Kingdom
| | - Anna Thompson
- University College London Hospital, London, NW1 2BU, United Kingdom
| | - Andrew Nisbet
- University College London, London, WC1E 6AE, United Kingdom
| | - Sarah Gulliford
- University College London, London, WC1E 6AE, United Kingdom
- University College London Hospital, London, NW1 2BU, United Kingdom
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Smolders A, Rivetti L, Vatterodt N, Korreman S, Lomax A, Sharma M, Studen A, Weber DC, Jeraj R, Albetini F. DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy. Phys Med Biol 2024; 69:155016. [PMID: 38986481 DOI: 10.1088/1361-6560/ad61b7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.Approach. Denoising diffusion probabilistic models (DDPMs) were developed to generate fraction-specific anatomical changes based on a reference cone-beam CT (CBCT), the fraction number and the dose distribution delivered. Three distinct DDPMs were developed: (1) theimage modelwas trained to directly generate likely future CBCTs, (2) the deformable vector field (DVF) model was trained to generate DVFs that deform a reference CBCT and (3) thehybrid modelwas trained similarly to the DVF model, but without relying on an external deformable registration algorithm. The models were trained on 9 patients with longitudinal CBCT images (224 CBCTs) and evaluated on 5 patients (152 CBCTs).Results. The generated images mainly exhibited random positioning shifts and small anatomical changes for early fractions. For later fractions, all models predicted weight losses in accordance with the training data. The distributions of volume and position changes of the body, esophagus, and parotids generated with the image and hybrid models were more similar to the ground truth distribution than the DVF model, evident from the lower Wasserstein distance achieved with the image (0.33) and hybrid model (0.30) compared to the DVF model (0.36). Generating several images for the same fraction did not yield the expected variability since the ground truth anatomical changes were only in 76% of the fractions within the 95% bounds predicted with the best model. Using the generated images for robust optimization of simplified proton therapy plans improved the worst-case clinical target volume V95 with 7% compared to optimizing with 3 mm set-up robustness while maintaining a similar integral dose.Significance. The newly developed DDPMs generate distributions similar to the real anatomical changes and have the potential to be used for robust anatomical optimization.
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Affiliation(s)
- A Smolders
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - L Rivetti
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - N Vatterodt
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - S Korreman
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - A Lomax
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - M Sharma
- Department of Radiation Oncology, University of California, San Francisco, CA, United States of America
| | - A Studen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
| | - D C Weber
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - R Jeraj
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
- University of Wisconsin-Madison, Madison, WI, United States of America
| | - F Albetini
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
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Bobić M, Lalonde A, Nesteruk KP, Lee H, Nenoff L, Gorissen BL, Bertolet A, Busse PM, Chan AW, Winey BA, Sharp GC, Verburg JM, Lomax AJ, Paganetti H. Large anatomical changes in head-and-neck cancers – a dosimetric comparison of online and offline adaptive proton therapy. Clin Transl Radiat Oncol 2023; 40:100625. [PMID: 37090849 PMCID: PMC10120292 DOI: 10.1016/j.ctro.2023.100625] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Purpose This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.
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Zhang Y, Alshaikhi J, Tan W, Royle G, Bär E. A probability model for anatomical robust optimisation in head and neck cancer proton therapy. Phys Med Biol 2022; 68. [PMID: 36562611 DOI: 10.1088/1361-6560/aca877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/02/2022] [Indexed: 12/03/2022]
Abstract
Objective.Develop an anatomical model based on the statistics of the population data and evaluate the model for anatomical robust optimisation in head and neck cancer proton therapy.Approach.Deformable image registration was used to build the probability model (PM) that captured the major deformation from patient population data and quantified the probability of each deformation. A cohort of 20 nasopharynx patients was included in this retrospective study. Each patient had a planning CT and 6 weekly CTs during radiotherapy. We applied the model to 5 test patients. Each test patient used the remaining 19 training patients to build the PM and estimate the likelihood of a certain anatomical deformation to happen. For each test patient, a spot scanning proton plan was created. The PM was evaluated using proton spot location deviation and dose distribution.Main results. Using the proton spot range, the PM can simulate small non-rigid variations in the first treatment week within 0.21 ± 0.13 mm. For overall anatomical uncertainty prediction, the PM can reduce anatomical uncertainty from 4.47 ± 1.23 mm (no model) to 1.49 ± 1.08 mm at week 6. The 95% confidence interval (CI) of dose metric variations caused by actual anatomical deformations in the first week is -0.59% ∼ -0.31% for low-risk CTD95, and 0.84-3.04 Gy for parotidDmean. On the other hand, the 95% CI of dose metric variations simulated by the PM at the first week is -0.52 ∼ -0.34% for low-risk CTVD95, and 0.58 ∼ 2.22 Gy for parotidDmean.Significance.The PM improves the estimation accuracy of anatomical uncertainty compared to the previous models and does not depend on the acquisition of the weekly CTs during the treatment. We also provided a solution to quantify the probability of an anatomical deformation. The potential of the model for anatomical robust optimisation is discussed.
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Affiliation(s)
- Ying Zhang
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Jailan Alshaikhi
- Saudi Proton Therapy Center, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Wenyong Tan
- Department of Oncology, Shenzhen Hospital of Southern Medical University Shenzhen 518101, People's Republic of China
| | - Gary Royle
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Esther Bär
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, United Kingdom.,University College London Hospitals NHS Foundation Trust, Radiotherapy Physics, 250 Euston Road, London NW1 2PG, United Kingdom
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Zhang Y, Alshaikhi J, Amos RA, Tan W, Anaya VM, Pang Y, Royle G, Bär E. Pre-treatment analysis of non-rigid variations can assist robust intensity-modulated proton therapy plan selection for head and neck patients. Med Phys 2022; 49:7683-7693. [PMID: 36083223 PMCID: PMC10092578 DOI: 10.1002/mp.15971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/13/2022] [Accepted: 08/27/2022] [Indexed: 12/27/2022] Open
Abstract
PURPOSE To incorporate small non-rigid variations of head and neck patients into the robust evaluation of intensity-modulated proton therapy (IMPT) for the selection of robust treatment plans. METHODS A cohort of 20 nasopharynx cancer patients with weekly kilovoltage CT (kVCT) and 15 oropharynx cancer patients with weekly cone-beam CT (CBCT) were retrospectively included. Anatomical variations between week 0/week 1 of treatment were acquired using deformable image registration (DIR) for all 35 patients and then applied to the planning CT of four patients who have kVCT scanned each week to simulate potential small non-rigid variations (sNRVs). The robust evaluations were conducted on IMPT plans with: (1) different number of beam fields from 3-field to 5-field; (2) different beam angles. The robust evaluation before treatment, including the sNRVs and setup uncertainty, referred to as sNRV+R evaluation was compared with the conventional evaluation (without sNRVs) in terms of robustness consistency with the gold standard evaluation based on weekly CT. RESULTS Among four patients (490 scenarios), we observed a maximum difference in the sNRV+R evaluation to the nominal dose of: 9.37% dose degradation on D95 of clinical target volumes (CTVs), increase in mean dose (D mean $_{\text{mean}}$ ) of parotid 11.87 Gy, increase in max dose (D max $_{\text{max}}$ ) of brainstem 20.82 Gy. In contrast, in conventional evaluation, we observed a maximum difference to the nominal dose of: 7.58% dose degradation on D95 of the CTVs, increase in parotid D mean $_{\text{mean}}$ by 4.88 Gy, increase in brainstem D max $_{\text{max}}$ by 13.5 Gy. In the measurement of the robustness ranking consistency with the gold standard evaluation, the sNRV+R evaluation was better or equal to the conventional evaluation in 77% of cases, particularly, better on spinal cord, parotid glands, and low-risk CTV. CONCLUSION This study demonstrated the additional dose discrepancy that sNRVs can make. The inclusion of sNRVs can be beneficial to robust evaluation, providing information on clinical uncertainties additional to the conventional rigid isocenter shift.
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Affiliation(s)
- Ying Zhang
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, UK
| | - Jailan Alshaikhi
- Saudi Proton Therapy Center, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Richard A Amos
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, UK
| | - Wenyong Tan
- Department of Oncology, Shenzhen Hospital of Southern Medical University Shenzhen, Guangdong, China
| | - Virginia Marin Anaya
- University College London Hospitals NHS Foundation Trust, Radiotherapy Physics, London, UK
| | - Yaru Pang
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, UK
| | - Gary Royle
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, UK
| | - Esther Bär
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, UK.,University College London Hospitals NHS Foundation Trust, Radiotherapy Physics, London, UK
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Zhang Y, Alshaikhi J, Amos RA, Lowe M, Tan W, Bär E, Royle G. Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept. Radiother Oncol 2022; 173:93-101. [PMID: 35667573 DOI: 10.1016/j.radonc.2022.05.036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 10/18/2022]
Abstract
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.
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Affiliation(s)
- Ying Zhang
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom.
| | - Jailan Alshaikhi
- Saudi Proton Therapy Center, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Richard A Amos
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
| | - Matthew Lowe
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Wenyong Tan
- Department of Oncology, Shenzhen Hospital of Southern Medical University, China
| | - Esther Bär
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; University College London Hospitals NHS Foundation Trust, United Kingdom
| | - Gary Royle
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
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