Pub Date : 2025-01-01DOI: 10.1016/j.phro.2025.100736
Keeva Moran, Claire Poole, Sarah Barrett
Background and purpose
Delineation of target volumes (TVs) and organs at risk (OARs) is a resource intensive process in lung radiation therapy and, despite the introduction of some auto-contouring, inter-observer variability remains a challenge. Deep learning algorithms may prove an efficient alternative and this review aims to map the evidence base on the use of deep learning algorithms for TV and OAR delineation in the radiation therapy planning process for lung cancer patients.
Materials and methods
A literature search identified studies relating to deep learning. Manual contouring and deep learning auto-contours were evaluated against one another for accuracy, inter-observer variability, contouring time and dose-volume effects. A total of 40 studies were included for review.
Results
Thirty nine out of 40 studies investigated the accuracy of deep learning auto-contours and determined that they were of a comparable accuracy to manual contours. Inter-observer variability outcomes were heterogeneous in the seven relevant studies identified. Twenty-four studies analysed the time saving associated with deep learning auto-contours and reported a significant time reduction in comparison to manual contours. The eight studies that conducted a dose-volume metric evaluation on deep learning auto-contours identified negligible effect on treatment plans.
Conclusion
The accuracy and time-saving capacity of deep learning auto-contours in comparison to manual contours has been extensively studied. However, additional research is required in the areas of inter-observer variability and dose-volume metric evaluation to further substantiate its clinical use.
{"title":"Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk","authors":"Keeva Moran, Claire Poole, Sarah Barrett","doi":"10.1016/j.phro.2025.100736","DOIUrl":"10.1016/j.phro.2025.100736","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Delineation of target volumes (TVs) and organs at risk (OARs) is a resource intensive process in lung radiation therapy and, despite the introduction of some auto-contouring, inter-observer variability remains a challenge. Deep learning algorithms may prove an efficient alternative and this review aims to map the evidence base on the use of deep learning algorithms for TV and OAR delineation in the radiation therapy planning process for lung cancer patients.</div></div><div><h3>Materials and methods</h3><div>A literature search identified studies relating to deep learning. Manual contouring and deep learning auto-contours were evaluated against one another for accuracy, inter-observer variability, contouring time and dose-volume effects. A total of 40 studies were included for review.</div></div><div><h3>Results</h3><div>Thirty nine out of 40 studies investigated the accuracy of deep learning auto-contours and determined that they were of a comparable accuracy to manual contours. Inter-observer variability outcomes were heterogeneous in the seven relevant studies identified. Twenty-four studies analysed the time saving associated with deep learning auto-contours and reported a significant time reduction in comparison to manual contours. The eight studies that conducted a dose-volume metric evaluation on deep learning auto-contours identified negligible effect on treatment plans.</div></div><div><h3>Conclusion</h3><div>The accuracy and time-saving capacity of deep learning auto-contours in comparison to manual contours has been extensively studied. However, additional research is required in the areas of inter-observer variability and dose-volume metric evaluation to further substantiate its clinical use.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100736"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.phro.2025.100732
Fabio S. D’Andrea , Robert Chuter , Adam H. Aitkenhead , Ranald I. MacKay , Roger M. Jones
Background
Very High-Energy Electron (VHEE) beams offer potential advantages over current clinical radiotherapy modalities due to their precise dose targeting and minimal peripheral dose spread, which is ideal for treating deep-seated tumours. To aid the development of clinical VHEE machines, this study adressed the need to identify optimum VHEE beam characteristics for tumours across various anatomical sites.
Materials and methods
VHEE treatment planning employed matRad, an open-source treatment planning system, by adapting its proton pencil beam scanning implementation. VHEE beam characteristics were generated using TOPAS Monte Carlo simulations. A total of 820 plans were retrospectively created and analysed across 10 pelvic and 12 thoracic cases and compared against clinical photon VMAT plans to identify the most optimal VHEE beam configuration and energy requirement.
Results
VHEE plans outperformed photon VMAT in sparing organs-at-risk (OARs) while maintaining or improving target coverage. While 150 MeV served as the threshold for effectively treating deep-seated sites, 200 MeV was identified as a more optimal energy in the pelvis for achieving the best balance of penetration and sparing abutting OARs. Lower energies (70–110 MeV) also benefitted mid-to-superficial disease in the lung cohort. Typically, VHEE plans required 3–5 fields, and resulted in notable dose reductions to OARs across treatment sites, including: 22.5% reduction in rectal Dmean; 13.8% decrease in bladder Dmean; 8.2% reduction in heart Dmean; and a 24.4% decrease in lung V20Gy.
Conclusion
The study reinforces VHEE’s potential in clinical settings, emphasising the need for varied energy ranges to enhance treatment flexibility and effectiveness.
{"title":"Comparative treatment planning of very high-energy electrons and photon volumetric modulated arc therapy: Optimising energy and beam parameters","authors":"Fabio S. D’Andrea , Robert Chuter , Adam H. Aitkenhead , Ranald I. MacKay , Roger M. Jones","doi":"10.1016/j.phro.2025.100732","DOIUrl":"10.1016/j.phro.2025.100732","url":null,"abstract":"<div><h3>Background</h3><div>Very High-Energy Electron (VHEE) beams offer potential advantages over current clinical radiotherapy modalities due to their precise dose targeting and minimal peripheral dose spread, which is ideal for treating deep-seated tumours. To aid the development of clinical VHEE machines, this study adressed the need to identify optimum VHEE beam characteristics for tumours across various anatomical sites.</div></div><div><h3>Materials and methods</h3><div>VHEE treatment planning employed matRad, an open-source treatment planning system, by adapting its proton pencil beam scanning implementation. VHEE beam characteristics were generated using TOPAS Monte Carlo simulations. A total of 820 plans were retrospectively created and analysed across 10 pelvic and 12 thoracic cases and compared against clinical photon VMAT plans to identify the most optimal VHEE beam configuration and energy requirement.</div></div><div><h3>Results</h3><div>VHEE plans outperformed photon VMAT in sparing organs-at-risk (OARs) while maintaining or improving target coverage. While 150 MeV served as the threshold for effectively treating deep-seated sites, 200 MeV was identified as a more optimal energy in the pelvis for achieving the best balance of penetration and sparing abutting OARs. Lower energies (70–110 MeV) also benefitted mid-to-superficial disease in the lung cohort. Typically, VHEE plans required 3–5 fields, and resulted in notable dose reductions to OARs across treatment sites, including: 22.5% reduction in rectal D<sub>mean</sub>; 13.8% decrease in bladder D<sub>mean</sub>; 8.2% reduction in heart D<sub>mean</sub>; and a 24.4% decrease in lung V<sub>20Gy</sub>.</div></div><div><h3>Conclusion</h3><div>The study reinforces VHEE’s potential in clinical settings, emphasising the need for varied energy ranges to enhance treatment flexibility and effectiveness.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100732"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.phro.2024.100689
Michael J. Dubec , Michael Berks , James Price , Lisa McDaid , John Gaffney , Ross A. Little , Susan Cheung , Marcel van Herk , Ananya Choudhury , Julian C. Matthews , Andrew McPartlin , Geoff J.M. Parker , David L. Buckley , James P.B. O’Connor
Background and purpose
Magnetic resonance imaging – linear accelerator (MRI-linac) systems permit imaging of tumours to guide treatment. Dynamic contrast enhanced (DCE)-MRI allows investigation of tumour perfusion. We assessed the feasibility of performing DCE-MRI on a 1.5 T MRI-linac in patients with head and neck cancer (HNC) and measured biomarker repeatability and sensitivity to radiotherapy effects.
Materials and methods
Patients were imaged on a 1.5 T MRI-linac or a 1.5 T diagnostic MR system twice before treatment. DCE-MRI parameters including Ktrans were calculated, with the optimum pharmacokinetic model identified using corrected Akaike information criterion. Repeatability was assessed by within-subject coefficient of variation (wCV). Treatment effects were assessed as change measured at week 2 of radiotherapy.
Results
14 patients were recruited (6 scanned on diagnostic MR and 8 on MRI-linac), with a total of 24 lesions. Baseline Ktrans estimates were comparable on both MR systems; 0.13 [95 %CI: 0.10 to 0.16] min−1 (diagnostic MR) and 0.15 [0.12 to 0.18] min−1 (MRI-linac). wCV values were 22.6 % [95 % CI: 16.2 to 37.3 %] (diagnostic MR) and 11.7 % [8.4 to 19.3 %] (MRI-linac). Combined cohort increase in Ktrans was significant (p < 0.01). Similar results were seen for other DCE-MRI parameters.
Conclusions
DCE-MRI is feasible on a 1.5 T MRI-linac system in patients with HNC. Parameter estimates, repeatability, and sensitivity to treatment were similar to those measured on a conventional diagnostic MR system. These data support performing DCE-MRI in studies on the MRI-linac to assess treatment response and adaptive guidance based on tumour perfusion.
背景和目的：磁共振成像-线性加速器（MRI-linac）系统允许肿瘤成像指导治疗。动态对比增强(DCE)-MRI可以检查肿瘤灌注情况。我们评估了在头颈癌（HNC）患者的1.5 T mri直线上进行DCE-MRI的可行性，并测量了生物标志物的可重复性和对放疗效果的敏感性。材料和方法：患者在治疗前两次在1.5 T mri直线仪或1.5 T诊断MR系统上成像。计算包括Ktrans在内的DCE-MRI参数，并使用修正的赤池信息准则确定最佳药代动力学模型。用受试者内变异系数（wCV）评价重复性。以放射治疗第2周时测量的变化来评估治疗效果。结果：纳入14例患者（6例诊断MR扫描，8例mri直线扫描），共24个病灶。两种MR系统的基线Ktrans估计值具有可比性；0.13 [95% CI: 0.10至0.16]min-1（诊断MR）和0.15[0.12至0.18]min-1 （mri线性）。wCV值为22.6% (95% CI: 16.2 ~ 37.3%)（诊断MR）和11.7% （mri线性）（8.4 ~ 19.3%）。结论：DCE-MRI在1.5 T MRI-linac系统上对HNC患者是可行的。参数估计、可重复性和对治疗的敏感性与传统诊断MR系统测量的结果相似。这些数据支持在MRI-linac研究中使用DCE-MRI来评估治疗反应和基于肿瘤灌注的适应性指导。
{"title":"Translation of dynamic contrast-enhanced imaging onto a magnetic resonance-guided linear accelerator in patients with head and neck cancer","authors":"Michael J. Dubec , Michael Berks , James Price , Lisa McDaid , John Gaffney , Ross A. Little , Susan Cheung , Marcel van Herk , Ananya Choudhury , Julian C. Matthews , Andrew McPartlin , Geoff J.M. Parker , David L. Buckley , James P.B. O’Connor","doi":"10.1016/j.phro.2024.100689","DOIUrl":"10.1016/j.phro.2024.100689","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Magnetic resonance imaging – linear accelerator (MRI-linac) systems permit imaging of tumours to guide treatment. Dynamic contrast enhanced (DCE)-MRI allows investigation of tumour perfusion. We assessed the feasibility of performing DCE-MRI on a 1.5 T MRI-linac in patients with head and neck cancer (HNC) and measured biomarker repeatability and sensitivity to radiotherapy effects.</div></div><div><h3>Materials and methods</h3><div>Patients were imaged on a 1.5 T MRI-linac or a 1.5 T diagnostic MR system twice before treatment. DCE-MRI parameters including K<sup>trans</sup> were calculated, with the optimum pharmacokinetic model identified using corrected Akaike information criterion. Repeatability was assessed by within-subject coefficient of variation (wCV). Treatment effects were assessed as change measured at week 2 of radiotherapy.</div></div><div><h3>Results</h3><div>14 patients were recruited (6 scanned on diagnostic MR and 8 on MRI-linac), with a total of 24 lesions. Baseline K<sup>trans</sup> estimates were comparable on both MR systems; 0.13 [95 %CI: 0.10 to 0.16] min<sup>−1</sup> (diagnostic MR) and 0.15 [0.12 to 0.18] min<sup>−1</sup> (MRI-linac). wCV values were 22.6 % [95 % CI: 16.2 to 37.3 %] (diagnostic MR) and 11.7 % [8.4 to 19.3 %] (MRI-linac). Combined cohort increase in K<sup>trans</sup> was significant (p < 0.01). Similar results were seen for other DCE-MRI parameters.</div></div><div><h3>Conclusions</h3><div>DCE-MRI is feasible on a 1.5 T MRI-linac system in patients with HNC. Parameter estimates, repeatability, and sensitivity to treatment were similar to those measured on a conventional diagnostic MR system. These data support performing DCE-MRI in studies on the MRI-linac to assess treatment response and adaptive guidance based on tumour perfusion.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100689"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.phro.2025.100714
Roel C. Kwakernaak, Victor J. Brand, Jesús Rojo-Santiago, Femke E. Froklage, Mischa S. Hoogeman, Steven J.M. Habraken, Maaike T.W. Milder
Background and purpose
Erectile dysfunction is a common side effect of radiotherapy for prostate cancer. To mitigate this toxicity, it has been suggested to limit the dose to critical nerves and vessels. We investigated the feasibility of sparing the neuro-vascular bundles (NVBs) in stereotactic body radiotherapy under the impact of realistic treatment uncertainties.
Materials and methods
Non-sparing and sparing NVB treatment plans, delivered in 5 × 7.25 Gy, were automatically generated for 20 patients. Polynomial Chaos Expansion (PCE) was used to fast and accurately model the dose against treatment errors. PCE enabled a robustness evaluation of 100.000 treatment scenarios per plan, allowing to derive scenario distributions of clinically relevant dose volume histogram parameters and population dose histograms.
Results
An average decrease of 3.7 Gy and 4.4 Gy in the median of the NVB was achieved in the patient population in the presence of realistic treatment uncertainties for non-coplanar (NC) and coplanar (C) plans respectively. Sparing NVBs decreased planning target volume coverage by 2.1 % in on average, however clinical target volume (CTV) dose remained adequate. Population dose histograms showed that, while sparing does impact dose volume histogram parameters of organs at risk (OARs), the probability of a scenario exceeding planning constraints was limited.
Conclusion
NVB sparing was maintained in the presence of treatment uncertainties without compromising CTV coverage or OAR dose. There was no significant difference in the achieved NVB dose between NC and C plans. The clinical impact of the achieved sparing is subject of ongoing clinical trials.
{"title":"Neurovascular bundle sparing in hypofractionated radiotherapy maintained with realistic treatment uncertainties","authors":"Roel C. Kwakernaak, Victor J. Brand, Jesús Rojo-Santiago, Femke E. Froklage, Mischa S. Hoogeman, Steven J.M. Habraken, Maaike T.W. Milder","doi":"10.1016/j.phro.2025.100714","DOIUrl":"10.1016/j.phro.2025.100714","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Erectile dysfunction is a common side effect of radiotherapy for prostate cancer. To mitigate this toxicity, it has been suggested to limit the dose to critical nerves and vessels. We investigated the feasibility of sparing the neuro-vascular bundles (NVBs) in stereotactic body radiotherapy under the impact of realistic treatment uncertainties.</div></div><div><h3>Materials and methods</h3><div>Non-sparing and sparing NVB treatment plans, delivered in 5 × 7.25 Gy, were automatically generated for 20 patients. Polynomial Chaos Expansion (PCE) was used to fast and accurately model the dose against treatment errors. PCE enabled a robustness evaluation of 100.000 treatment scenarios per plan, allowing to derive scenario distributions of clinically relevant dose volume histogram parameters and population dose histograms.</div></div><div><h3>Results</h3><div>An average decrease of 3.7 Gy and 4.4 Gy in the median <span><math><mrow><msub><mi>D</mi><mrow><mn>0.1</mn><mi>c</mi><msup><mrow><mi>m</mi></mrow><mn>3</mn></msup></mrow></msub></mrow></math></span> of the NVB was achieved in the patient population in the presence of realistic treatment uncertainties for non-coplanar (NC) and coplanar (C) plans respectively. Sparing NVBs decreased planning target volume coverage by 2.1 % in <span><math><mrow><msub><mi>V</mi><mrow><mn>36.25</mn><mi>G</mi><mi>y</mi></mrow></msub></mrow></math></span> on average, however clinical target volume (CTV) dose remained adequate. Population dose histograms showed that, while sparing does impact dose volume histogram parameters of organs at risk (OARs), the probability of a scenario exceeding planning constraints was limited.</div></div><div><h3>Conclusion</h3><div>NVB sparing was maintained in the presence of treatment uncertainties without compromising CTV coverage or OAR dose. There was no significant difference in the achieved NVB dose between NC and C plans. The clinical impact of the achieved sparing is subject of ongoing clinical trials.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100714"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.phro.2025.100701
Artemis Bouzaki , Dylan Green , Marcel van Herk , Jane Shortall , Tanuj Puri , Sarah Kerns , David Azria , Marrie-Pierre Farcy-Jacquet , Jenny Chang-Claude , Ananya Choudhury , Alison Dunning , Maarten Lambrecht , Barbara Avuzzi , Dirk De Ruysscher , Petra Seibold , Elena Sperk , Christopher Talbot , Ana Vega , Liv Veldeman , Adam Webb , Alan McWilliam
Background and purpose
Growing evidence suggests that spatial dose variations across the rectal surface influence toxicity risk after radiotherapy. Existing methodologies employ a fixed, arbitrary physical extent for rectal dose mapping, limiting their analysis. We developed a method to standardise rectum contours, unfold them into 2D cylindrical surface maps, and identify subregions where higher doses increase rectal toxicities.
Materials and methods
Data of 1,048 patients with prostate cancer from the REQUITE study were used. Deep learning based automatic segmentations were generated to ensure consistency. Rectum length was standardised using linear transformations superior and inferior to the prostate. The automatic contours were validated against the manual contours through contour variation assessment with cylindrical mapping. Voxel-based analysis of the dose surface maps for the manual and automatic contours against individual rectal toxicities was performed using Student’s t permutation test and Cox Proportional Hazards Model (CPHM). Significance was defined by permutation testing.
Results
Our method enabled the analysis of 1,048 patients using automatic segmentation. Student’s t-test showed significance (p < 0.05) in the lower posterior for clinical-reported proctitis and patient-reported bowel urgency. Univariable CPHM identified a 3 % increased risk per Gy for clinician-reported proctitis and a 2 % increased risk per Gy for patient-reported bowel urgency in the lower posterior. No other endpoints were significant.
Conclusion
We developed a methodology that unfolds the rectum to a 2D surface map. The lower posterior was significant for clinician-reported proctitis and patient-reported bowel urgency, suggesting that reducing the dose in the region could decrease toxicity risk.
{"title":"New rectum dose surface mapping methodology to identify rectal subregions associated with toxicities following prostate cancer radiotherapy","authors":"Artemis Bouzaki , Dylan Green , Marcel van Herk , Jane Shortall , Tanuj Puri , Sarah Kerns , David Azria , Marrie-Pierre Farcy-Jacquet , Jenny Chang-Claude , Ananya Choudhury , Alison Dunning , Maarten Lambrecht , Barbara Avuzzi , Dirk De Ruysscher , Petra Seibold , Elena Sperk , Christopher Talbot , Ana Vega , Liv Veldeman , Adam Webb , Alan McWilliam","doi":"10.1016/j.phro.2025.100701","DOIUrl":"10.1016/j.phro.2025.100701","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Growing evidence suggests that spatial dose variations across the rectal surface influence toxicity risk after radiotherapy. Existing methodologies employ a fixed, arbitrary physical extent for rectal dose mapping, limiting their analysis. We developed a method to standardise rectum contours, unfold them into 2D cylindrical surface maps, and identify subregions where higher doses increase rectal toxicities.</div></div><div><h3>Materials and methods</h3><div>Data of 1,048 patients with prostate cancer from the REQUITE study were used. Deep learning based automatic segmentations were generated to ensure consistency. Rectum length was standardised using linear transformations superior and inferior to the prostate. The automatic contours were validated against the manual contours through contour variation assessment with cylindrical mapping. Voxel-based analysis of the dose surface maps for the manual and automatic contours against individual rectal toxicities was performed using Student’s t permutation test and Cox Proportional Hazards Model (CPHM). Significance was defined by permutation testing.</div></div><div><h3>Results</h3><div>Our method enabled the analysis of 1,048 patients using automatic segmentation. Student’s <em>t</em>-test showed significance (p < 0.05) in the lower posterior for clinical-reported proctitis and patient-reported bowel urgency. Univariable CPHM identified a 3 % increased risk per Gy for clinician-reported proctitis and a 2 % increased risk per Gy for patient-reported bowel urgency in the lower posterior. No other endpoints were significant.</div></div><div><h3>Conclusion</h3><div>We developed a methodology that unfolds the rectum to a 2D surface map. The lower posterior was significant for clinician-reported proctitis and patient-reported bowel urgency, suggesting that reducing the dose in the region could decrease toxicity risk.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100701"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.phro.2025.100702
Karen Chin Snyder, Salim M. Siddiqui, Parag Parikh, Kundan Thind
Background and Purpose
Online adaptive radiotherapy for fractionated intracranial stereotactic radiosurgery (FSRS) on a magnetic resonance linear accelerator (MR-L) has the potential to allow for real-time adjustments of anatomical changes during radiotherapy treatment. This study investigates the dosimetric improvements of an online-adaptive MR-L workflow and validates the dosimetry utilizing an MR-visible phantom.
Methods and materials
Twenty-six cases previously treated with a conventional C-arm linear accelerator (CA-L) were replanned to determine optimal optimization constraints and objectives for achieving comparable MR-L plans. The optimization methodology was subsequently applied to simulate an online adaptive workflow on an MR phantom, incorporating target volumes from five previously treated patients that required offline adaptation. Plan quality and normal brain dose statistics were evaluated and compared to the offline adapted CA-L plans.
Results
No significant difference was observed between the CA-L and MR-L target coverage. The normal brain dose for MR-L plans increased with target volume more rapidly than for CA-L plans. However, some outliers achieved equivalent normal brain doses, indicating potential benefits of MRIgRT for specific superficial volumes located in the frontal, occipital lobes, and cerebellum. End-to-end validation with simulated adaptive workflow on a MR phantom utilizing target volumes that previously required adaption showed acceptable difference of <2.5 % between measured and planned target dose.
Conclusion
The study shows promising results for an online adaptive workflow for the treatment of intracranial FSRS on a low-field MR-L.
{"title":"Adaptive treatment workflow and dosimetric evaluation of intracranial fractionated stereotactic radiosurgery on a low-field magnetic resonance-linear accelerator","authors":"Karen Chin Snyder, Salim M. Siddiqui, Parag Parikh, Kundan Thind","doi":"10.1016/j.phro.2025.100702","DOIUrl":"10.1016/j.phro.2025.100702","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Online adaptive radiotherapy for fractionated intracranial stereotactic radiosurgery (FSRS) on a magnetic resonance linear accelerator (MR-L) has the potential to allow for real-time adjustments of anatomical changes during radiotherapy treatment. This study investigates the dosimetric improvements of an online-adaptive MR-L workflow and validates the dosimetry utilizing an MR-visible phantom.</div></div><div><h3>Methods and materials</h3><div>Twenty-six cases previously treated with a conventional C-arm linear accelerator (CA-L) were replanned to determine optimal optimization constraints and objectives for achieving comparable MR-L plans. The optimization methodology was subsequently applied to simulate an online adaptive workflow on an MR phantom, incorporating target volumes from five previously treated patients that required offline adaptation. Plan quality and normal brain dose statistics were evaluated and compared to the offline adapted CA-L plans.</div></div><div><h3>Results</h3><div>No significant difference was observed between the CA-L and MR-L target coverage. The normal brain dose for MR-L plans increased with target volume more rapidly than for CA-L plans. However, some outliers achieved equivalent normal brain doses, indicating potential benefits of MRIgRT for specific superficial volumes located in the frontal, occipital lobes, and cerebellum. End-to-end validation with simulated adaptive workflow on a MR phantom utilizing target volumes that previously required adaption showed acceptable difference of <2.5 % between measured and planned target dose.</div></div><div><h3>Conclusion</h3><div>The study shows promising results for an online adaptive workflow for the treatment of intracranial FSRS on a low-field MR-L.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100702"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cone-beam computed tomography (CBCT) is essential in image-guided radiotherapy (RT) for patient positioning and daily dose calculation. However, CT numbers in CBCT fluctuate and differ from those in computed tomography (CT), requiring synthetic CT (sCT) generation to improve dose calculation accuracy. CBCT-to-sCT synthesis remains a challenging and uncertain task in clinical practice. This study aims to introduce a voxel-wise uncertainty estimator correlated with the error between sCT and CT.
Material and Methods:
Eighty-five head and neck (H&N) patients treated with photon RT from a single center were selected for developing and validating our uncertainty estimation method. To test the method’s robustness on out-of-distribution images, three additional patients from different centers were included. Our proposed uncertainty estimation method builds on established conventional techniques. Additionally, to explore potential error scenarios, we generated several ‘plausible’ sCTs representing variations in sCT generation caused by CBCT quality differences. This allowed us to quantify dose uncertainties.
Results:
The effectiveness of uncertainty maps was evaluated by correlating them with the absolute error map between sCT and CT, yielding a Pearson correlation coefficient between 0.65 and 0.72. Dose uncertainty was determined on the dose-volume histogram (DVH). For all patients except one, the reference CT DVH was included in the uncertainty interval defined by the sCT-derived DVH.
Conclusions:
Our proposed methods effectively predict uncertainty maps that aid in evaluating sCT quality. This approach also provides a novel method for estimating dose uncertainty by defining a confidence interval around the CT DVH using the estimated sCT uncertainty.
{"title":"Modeling dose uncertainty in cone-beam computed tomography: Predictive approach for deep learning-based synthetic computed tomography generation","authors":"Cédric Hémon, Lucía Cubero, Valentin Boussot, Romane-Alize Martin, Blanche Texier, Joël Castelli, Renaud de Crevoisier, Anaïs Barateau, Caroline Lafond, Jean-Claude Nunes","doi":"10.1016/j.phro.2025.100704","DOIUrl":"10.1016/j.phro.2025.100704","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>Cone-beam computed tomography (CBCT) is essential in image-guided radiotherapy (RT) for patient positioning and daily dose calculation. However, CT numbers in CBCT fluctuate and differ from those in computed tomography (CT), requiring synthetic CT (sCT) generation to improve dose calculation accuracy. CBCT-to-sCT synthesis remains a challenging and uncertain task in clinical practice. This study aims to introduce a voxel-wise uncertainty estimator correlated with the error between sCT and CT.</div></div><div><h3>Material and Methods:</h3><div>Eighty-five head and neck (H&N) patients treated with photon RT from a single center were selected for developing and validating our uncertainty estimation method. To test the method’s robustness on out-of-distribution images, three additional patients from different centers were included. Our proposed uncertainty estimation method builds on established conventional techniques. Additionally, to explore potential error scenarios, we generated several ‘plausible’ sCTs representing variations in sCT generation caused by CBCT quality differences. This allowed us to quantify dose uncertainties.</div></div><div><h3>Results:</h3><div>The effectiveness of uncertainty maps was evaluated by correlating them with the absolute error map between sCT and CT, yielding a Pearson correlation coefficient between 0.65 and 0.72. Dose uncertainty was determined on the dose-volume histogram (DVH). For all patients except one, the reference CT DVH was included in the uncertainty interval defined by the sCT-derived DVH.</div></div><div><h3>Conclusions:</h3><div>Our proposed methods effectively predict uncertainty maps that aid in evaluating sCT quality. This approach also provides a novel method for estimating dose uncertainty by defining a confidence interval around the CT DVH using the estimated sCT uncertainty.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100704"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.phro.2025.100718
Andreas Renner , Ingo Gulyas , Martin Buschmann , Gerd Heilemann , Barbara Knäusl , Martin Heilmann , Joachim Widder , Dietmar Georg , Petra Trnková
{"title":"Corrigendum to “Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data” [Phys. Imaging Radiat. Oncol. 30 (2024) 100594]","authors":"Andreas Renner , Ingo Gulyas , Martin Buschmann , Gerd Heilemann , Barbara Knäusl , Martin Heilmann , Joachim Widder , Dietmar Georg , Petra Trnková","doi":"10.1016/j.phro.2025.100718","DOIUrl":"10.1016/j.phro.2025.100718","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100718"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The repeatability of the apparent diffusion coefficient (ADC) during radiotherapy for rectal cancer on a 1.5 T MR-linac was investigated by acquiring two sequential diffusion-weighted imaging (DWI) sequences at each fraction. In 109 treatment sessions involving 22 patients, tumors were separately delineated on the b500 images. ADC maps were generated with all b-values (0, 30, 150, and 500 s/mm2) on the MR-linac, and the median ADC values were used in Bland-Altman analyses. A relative repeatability coefficient of 17.0 % was determined, providing a threshold to differentiate between measurement variability and true treatment response. This threshold can be used for potential response monitoring and personalized treatment adjustments.
{"title":"Repeatability of rectal cancer apparent diffusion coefficient measurements on a 1.5 T MR-linac","authors":"Hidde Eijkelenkamp , Guus Grimbergen , Brigid McDonald , Reijer Rutgers , Tim Schakel , Casper Beijst , Marielle Philippens , Gert Meijer , Martijn Intven","doi":"10.1016/j.phro.2025.100720","DOIUrl":"10.1016/j.phro.2025.100720","url":null,"abstract":"<div><div>The repeatability of the apparent diffusion coefficient (ADC) during radiotherapy for rectal cancer on a 1.5 T MR-linac was investigated by acquiring two sequential diffusion-weighted imaging (DWI) sequences at each fraction. In 109 treatment sessions involving 22 patients, tumors were separately delineated on the b500 images. ADC maps were generated with all b-values (0, 30, 150, and 500 s/mm<sup>2</sup>) on the MR-linac, and the median ADC values were used in Bland-Altman analyses. A relative repeatability coefficient of 17.0 % was determined, providing a threshold to differentiate between measurement variability and true treatment response. This threshold can be used for potential response monitoring and personalized treatment adjustments.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100720"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.phro.2025.100713
Joep C. Stroom , Sandra C. Vieira , Carlo Greco , Sebastiaan M.J.J.G. Nijsten
Background and purpose
Geometrical uncertainties in radiotherapy are generally accounted for by margins for tumors, but their effect on organs-at-risk (OARs) is often ignored. We developed a model that incorporates dose- and geometry-based uncertainties in OAR planning using dose constraints.
Materials and methods
Radiotherapy uncertainties cause real dose-volume histograms (DVHs) to spread around the planned DVH. With a published OAR dose constraint D(Vcrit) < Dcrit such that complication probability < Y%, real differences from planned Dcrit can be described by mean- (MDDcrit) and standard deviations (SDDcrit). Assuming complications are associated with the worst DVHs, New dose constraints that maintain complication probability can be derived for new treatments:Dcrit,New = Dcrit,publ + Φ−1(1 - Y%) * (SDDcrit,publ - SDDcrit,New) + (MDDcrit,publ - MDDcrit,New),with Φ−1(x) the inverse cumulative normal distribution function. Setting SDDcrit,New = MDDcrit,New = 0 in the recipe yields the “True” critical dose, and Dcrit,True - Dcrit,publ can be considered a dose-based safety margin (DSM).
As hypothetical example, we estimated MDDcrit and SDDcrit values by simulating geometric errors in our clinical treatment plans and adding dose-based uncertainty. Over 1000 OARs with 108 different regular- and hypo-fractionation constraints were simulated. We assumed accuracy SDs to change from 2.5mm/3% to 1.5mm/2%.
Results
Results varied per OAR, fractionation, and constraint-type. If our 2.5mm/3% MDDcrit and SDDcrit values approximated dose-constraint studies, on average the DSM would be 4.5 Gy (18%) and our dose constraints would increase with 1.2 Gy (5%).
Conclusions
We introduced a first model relating dose constraints and complication probabilities with treatment uncertainties and safety margins for OARs. Among other things, it quantified how higher constraints can be applied with increasing radiotherapy accuracy.
{"title":"Accuracy-dependent dose-constraints and dose-based safety margins for organs-at-risk in radiotherapy","authors":"Joep C. Stroom , Sandra C. Vieira , Carlo Greco , Sebastiaan M.J.J.G. Nijsten","doi":"10.1016/j.phro.2025.100713","DOIUrl":"10.1016/j.phro.2025.100713","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Geometrical uncertainties in radiotherapy are generally accounted for by margins for tumors, but their effect on organs-at-risk (OARs) is often ignored. We developed a model that incorporates dose- and geometry-based uncertainties in OAR planning using dose constraints.</div></div><div><h3>Materials and methods</h3><div>Radiotherapy uncertainties cause real dose-volume histograms (DVHs) to spread around the planned DVH. With a <em>published</em> OAR dose constraint D(V<sub>crit</sub>) < D<sub>crit</sub> such that complication probability < Y%, real differences from planned D<sub>crit</sub> can be described by mean- (MD<sub>Dcrit</sub>) and standard deviations (SD<sub>Dcrit</sub>). Assuming complications are associated with the worst DVHs, <em>New</em> dose constraints that maintain complication probability can be derived for new treatments:<span><span><span>D<sub>crit,New</sub> = D<sub>crit,publ</sub> + Φ<sup>−1</sup>(1 - Y%) * (SD<sub>Dcrit,publ</sub> - SD<sub>Dcrit,New</sub>) + (MD<sub>Dcrit,publ</sub> - MD<sub>Dcrit,New</sub>),</span></span></span>with Φ<sup>−1</sup>(x) the inverse cumulative normal distribution function. Setting SD<sub>Dcrit,New</sub> = MD<sub>Dcrit,New</sub> = 0 in the recipe yields the “True” critical dose, and D<sub>crit,True</sub> - D<sub>crit,publ</sub> can be considered a dose-based safety margin (DSM).</div><div>As hypothetical example, we estimated MD<sub>Dcrit</sub> and SD<sub>Dcrit</sub> values by simulating geometric errors in our clinical treatment plans and adding dose-based uncertainty. Over 1000 OARs with 108 different regular- and hypo-fractionation constraints were simulated. We assumed accuracy SDs to change from 2.5mm/3% to 1.5mm/2%.</div></div><div><h3>Results</h3><div>Results varied per OAR, fractionation, and constraint-type. If our 2.5mm/3% MD<sub>Dcrit</sub> and SD<sub>Dcrit</sub> values approximated dose-constraint studies, on average the DSM would be 4.5 Gy (18%) and our dose constraints would increase with 1.2 Gy (5%).</div></div><div><h3>Conclusions</h3><div>We introduced a first model relating dose constraints and complication probabilities with treatment uncertainties and safety margins for OARs. Among other things, it quantified how higher constraints can be applied with increasing radiotherapy accuracy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100713"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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