Pub Date : 2025-12-16DOI: 10.1016/j.phro.2025.100891
Bertrand Dewit , Ronald Peeters , Sandra Nuyts , Tom Depuydt
Accurate and reproducible immobilization is critical for effective radiotherapy in the head and neck region, but conventional thermoplastic masks are uncomfortable, labor-intensive and time-consuming to fabricate. A novel patient-specific 3D-printed immobilization device was developed using a digital CT-based workflow. The design targeted stable facial regions and was mechanically validated via simulations. Two nylon-based 3D-printed prototypes were preclinically evaluated on an anthropomorphic phantom, showing high dimensional accuracy, CT/MR compatibility, minimal target dose deviation (<2%), and submillimetric positional reproducibility. While the results demonstrate technical feasibility, clinical validations will be the next step to assess comfort, reproducibility, and workflow integration in radiotherapy.
{"title":"Development and validation of a novel patient-specific 3D-printed head and neck immobilization device for radiotherapy","authors":"Bertrand Dewit , Ronald Peeters , Sandra Nuyts , Tom Depuydt","doi":"10.1016/j.phro.2025.100891","DOIUrl":"10.1016/j.phro.2025.100891","url":null,"abstract":"<div><div>Accurate and reproducible immobilization is critical for effective radiotherapy in the head and neck region, but conventional thermoplastic masks are uncomfortable, labor-intensive and time-consuming to fabricate. A novel patient-specific 3D-printed immobilization device was developed using a digital CT-based workflow. The design targeted stable facial regions and was mechanically validated via simulations. Two nylon-based 3D-printed prototypes were preclinically evaluated on an anthropomorphic phantom, showing high dimensional accuracy, CT/MR compatibility, minimal target dose deviation (<2%), and submillimetric positional reproducibility. While the results demonstrate technical feasibility, clinical validations will be the next step to assess comfort, reproducibility, and workflow integration in radiotherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100891"},"PeriodicalIF":3.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.phro.2025.100894
Yoshiro Ieko , Noriyuki Kadoya , Hisanori Ariga
Background and purpose
Deformable image registration (DIR) is an important technique in radiation therapy. To improve DIR accuracy, we applied radiomics-based image filtering as a preprocessing step before DIR.
Materials and methods
Thoracic four-dimensional computed tomography (CT) images of 10 patients with lung or esophageal cancer were examined. Peak-inhale and peak-exhale images were used for DIR. Before DIR, these images were converted into 90-voxel-based radiomics-based filtered images using extracted local radiomics features, respectively. On each filtered image, DIR between the peak-inhale and peak-exhale filtered images was performed. After DIR, the peak-inhale CT images were deformed to peak-exhale CT images using the displacement vector fields obtained from the DIR. The registration errors obtained from each radiomics-based DIR were calculated using landmark pairs and compared with conventional CT-based DIR using the same DIR parameters.
Results
In radiomics-based DIR, the lowest registration errors (95th percentile) for intensity and texture features were 0.96 mm (right-left), 1.35–1.38 mm (anterior-posterior), 2.04–2.13 mm (superior-inferior), and 2.49–2.57 mm (three-dimensional). For CT-based DIR, the corresponding registration errors were 1.31 mm, 1.72 mm, 3.45 mm, and 3.98 mm.
Conclusions
By applying radiomics-based image filtering before DIR as a preprocessing, the registration error was lower than that of conventional CT-based DIR, suggesting that using radiomics may improve the accuracy of DIR.
{"title":"Application of radiomics-based image filtering to improve deformable image registration accuracy in thoracic images","authors":"Yoshiro Ieko , Noriyuki Kadoya , Hisanori Ariga","doi":"10.1016/j.phro.2025.100894","DOIUrl":"10.1016/j.phro.2025.100894","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Deformable image registration (DIR) is an important technique in radiation therapy. To improve DIR accuracy, we applied radiomics-based image filtering as a preprocessing step before DIR.</div></div><div><h3>Materials and methods</h3><div>Thoracic four-dimensional computed tomography (CT) images of 10 patients with lung or esophageal cancer were examined. Peak-inhale and peak-exhale images were used for DIR. Before DIR, these images were converted into 90-voxel-based radiomics-based filtered images using extracted local radiomics features, respectively. On each filtered image, DIR between the peak-inhale and peak-exhale filtered images was performed. After DIR, the peak-inhale CT images were deformed to peak-exhale CT images using the displacement vector fields obtained from the DIR. The registration errors obtained from each radiomics-based DIR were calculated using landmark pairs and compared with conventional CT-based DIR using the same DIR parameters.</div></div><div><h3>Results</h3><div>In radiomics-based DIR, the lowest registration errors (95th percentile) for intensity and texture features were 0.96 mm (right-left), 1.35–1.38 mm (anterior-posterior), 2.04–2.13 mm (superior-inferior), and 2.49–2.57 mm (three-dimensional). For CT-based DIR, the corresponding registration errors were 1.31 mm, 1.72 mm, 3.45 mm, and 3.98 mm.</div></div><div><h3>Conclusions</h3><div>By applying radiomics-based image filtering before DIR as a preprocessing, the registration error was lower than that of conventional CT-based DIR, suggesting that using radiomics may improve the accuracy of DIR.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100894"},"PeriodicalIF":3.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.phro.2025.100892
Sarah A. Mason , Bethany Williams , Sophie Alexander , Alex Dunlop , Alison Tree , Emma J. Harris , Helen McNair
Background and Purpose
: Online adaptive radiotherapy (oART) is underused as generating a treatment plan at every fraction is slow and resource intensive. One method to address this involves reusing plans generated online in previous fractions with similar anatomy. However, manually assessing the suitability of each pre-existing treatment plan is prohibitively time-consuming. To gauge potential impact and motivate the development of software to enable plan recycling, we assessed a strategy whereby all pre-existing plans were considered for subsequent fractions in nine hypofractionated prostate patients treated on the magnetic resonance (MR) linear accelerator.
Methods:
The verification MR was used to estimate the delivered dose after adaptation to establish a Current Clinical Practice Benchmark. Each structure from the daily MR was propagated backwards onto the reference and daily MRs from previous fractions to calculate the dose to each structure that would have been received had the corresponding plan been delivered. The resulting dose statistics were assessed against: (A) standard target and organ-at-risk objectives, (B) the Current Clinical Practice Benchmark, and (C) circumstances where a pre-existing plan would have matched or outperformed the online plan.
Results:
The median [interquartile range] percentage of fractions with at least one acceptable pre-existing plan was 25% [20%], 40% [35%], and 60% [20%] for criteria A, B, and C respectively. Reusing the reference plan was only acceptable in 0%–20% of fractions.
Conclusion:
Reusing pre-existing plans is feasible and could accelerate oART and reduce hospital resources in approximately 40% of fractions whilst achieving the same dose-volume metrics as current oART workflows.
{"title":"Feasibility of reusing online-generated treatment plans for adaptive radiotherapy in prostate cancer","authors":"Sarah A. Mason , Bethany Williams , Sophie Alexander , Alex Dunlop , Alison Tree , Emma J. Harris , Helen McNair","doi":"10.1016/j.phro.2025.100892","DOIUrl":"10.1016/j.phro.2025.100892","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>: Online adaptive radiotherapy (oART) is underused as generating a treatment plan at every fraction is slow and resource intensive. One method to address this involves reusing plans generated online in previous fractions with similar anatomy. However, manually assessing the suitability of each pre-existing treatment plan is prohibitively time-consuming. To gauge potential impact and motivate the development of software to enable plan recycling, we assessed a strategy whereby <em>all</em> pre-existing plans were considered for subsequent fractions in nine hypofractionated prostate patients treated on the magnetic resonance (MR) linear accelerator.</div></div><div><h3>Methods:</h3><div>The verification MR was used to estimate the delivered dose after adaptation to establish a Current Clinical Practice Benchmark. Each structure from the daily MR was propagated backwards onto the reference and daily MRs from previous fractions to calculate the dose to each structure that would have been received had the corresponding plan been delivered. The resulting dose statistics were assessed against: (A) standard target and organ-at-risk objectives, (B) the Current Clinical Practice Benchmark, and (C) circumstances where a pre-existing plan would have matched or outperformed the online plan.</div></div><div><h3>Results:</h3><div>The median [interquartile range] percentage of fractions with at least one acceptable pre-existing plan was 25% [20%], 40% [35%], and 60% [20%] for criteria A, B, and C respectively. Reusing the reference plan was only acceptable in 0%–20% of fractions.</div></div><div><h3>Conclusion:</h3><div>Reusing pre-existing plans is feasible and could accelerate oART and reduce hospital resources in approximately 40% of fractions whilst achieving the same dose-volume metrics as current oART workflows.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100892"},"PeriodicalIF":3.3,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1016/j.phro.2025.100889
Christopher J.H. Pagett , John Lilley , Christopher O’Hara , Ane Appelt , Louise Murray , Rasmus Bokrantz , Jakob Ödén , Stina Svensson , Mark Harrison , Philip Camilleri , Rebecca Muirhead , Maxwell Robinson , Christopher Thompson
Background and purpose
Reirradiation is clinically challenging, requiring a balance between delivery of dose to tumour while respecting cumulative organ at risk (OAR) dose constraints. Standard prescriptions are often conservative, ignoring patient variability in achievable OAR doses. Isotoxic radiotherapy individualises treatment by delivering the highest equieffective dose in 2 Gy per fraction (EQD2Gy) while meeting OAR constraints. This technical feasibility study assessed isotoxic pelvic reirradiation using cumulative OAR constraints, the original dose distribution as background, and voxel-by-voxel EQD2Gy optimisation.
Materials and methods
Data from 30 patients previously treated with pelvic stereotactic body radiotherapy (SBRT) at three UK centres were included. OARs were delineated on both previous and reirradiation image sets and deformably registered. Previous dose was mapped to the current image set and used as background dose for SBRT planning, following published methods. Initial 25 Gy in five fractions (25 Gy/5#) plans were generated for all patients, with further isotoxic dose escalation conducted up to a maximum of 50 Gy (fraction number fixed) until cumulative EQD2Gy constraints were reached.
Results
For 25 of 30 patients, clinically acceptable isotoxic plans were obtained, with 23 exceeding the standard UK reirradiation prescription dose of 30 Gy/5#. The median isotoxic prescription was 42 Gy/5#, with four patient plans reaching the upper evaluated limit of 50 Gy. Vessels and the sacral plexus were most frequently dose limiting.
Conclusion
This study highlighted the feasibility of isotoxic pelvic reirradiation and supports further investigation into automation and prediction models to streamline implementation in clinical practice.
{"title":"Isotoxic stereotactic reirradiation for recurrent pelvic cancers","authors":"Christopher J.H. Pagett , John Lilley , Christopher O’Hara , Ane Appelt , Louise Murray , Rasmus Bokrantz , Jakob Ödén , Stina Svensson , Mark Harrison , Philip Camilleri , Rebecca Muirhead , Maxwell Robinson , Christopher Thompson","doi":"10.1016/j.phro.2025.100889","DOIUrl":"10.1016/j.phro.2025.100889","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Reirradiation is clinically challenging, requiring a balance between delivery of dose to tumour while respecting cumulative organ at risk (OAR) dose constraints. Standard prescriptions are often conservative, ignoring patient variability in achievable OAR doses. Isotoxic radiotherapy individualises treatment by delivering the highest equieffective dose in 2 Gy per fraction (EQD2Gy) while meeting OAR constraints. This technical feasibility study assessed isotoxic pelvic reirradiation using cumulative OAR constraints, the original dose distribution as background, and voxel-by-voxel EQD2Gy optimisation.</div></div><div><h3>Materials and methods</h3><div>Data from 30 patients previously treated with pelvic stereotactic body radiotherapy (SBRT) at three UK centres were included. OARs were delineated on both previous and reirradiation image sets and deformably registered. Previous dose was mapped to the current image set and used as background dose for SBRT planning, following published methods. Initial 25 Gy in five fractions (25 Gy/5#) plans were generated for all patients, with further isotoxic dose escalation conducted up to a maximum of 50 Gy (fraction number fixed) until cumulative EQD2Gy constraints were reached.</div></div><div><h3>Results</h3><div>For 25 of 30 patients, clinically acceptable isotoxic plans were obtained, with 23 exceeding the standard UK reirradiation prescription dose of 30 Gy/5#. The median isotoxic prescription was 42 Gy/5#, with four patient plans reaching the upper evaluated limit of 50 Gy. Vessels and the sacral plexus were most frequently dose limiting.</div></div><div><h3>Conclusion</h3><div>This study highlighted the feasibility of isotoxic pelvic reirradiation and supports further investigation into automation and prediction models to streamline implementation in clinical practice.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100889"},"PeriodicalIF":3.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1016/j.phro.2025.100890
Mathieu Gaudreault , Lachlan McIntosh , Katrina Woodford , Jason Li , Susan Harden , Sandro Porceddu , Nicholas Hardcastle , Vanessa Panettieri
Background and purpose
The monitor units (MU) per control point (CP) control the necessary fine-tuned ablative dose for hypofractionated radiotherapy of oligometastatic cancer. We aimed to introduce strategies maximising the sample size to accurately predict the MU per CP with artificial intelligence (AI).
Materials and methods
The 40/68/88 treatment plans of consecutive patients treated between 01/2019 and 06/2024 at our institution for metastatic cancer to the liver/bone/lung were included. Two approaches were considered to maximise the sample size. In one approach, the samples of each anatomical site were extensively augmented to predict the MU per CP from the dose distribution per CP, providing the MU per beam and meterset weight per CP. In the other approach, all samples from all anatomical sites were combined for training. The number of achieved clinical goals based on dose-volume calculation metrics in AI radiotherapy plans (AI-RTPlan) was compared with the number of achieved clinical goals in the clinical plans.
Results
The mean absolute percentage error between predicted and clinical MU per beam/meterset weight per CP was less than 6.2%. All AI-RTPlans were generated in less than 5 s. At least 90%/5% of patients had the same, or more, achieved clinical goals with AI-RTPlans. Target coverage and dose to organs at risk metrics were within ± 2% and ± 2.3 Gy of the clinical value in all patients, respectively.
Conclusions
Augmenting data extensively and combining anatomical sites were equivalent and proficient strategies to predict machine settings for radiotherapy planning of oligometastatic cancer.
{"title":"A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer","authors":"Mathieu Gaudreault , Lachlan McIntosh , Katrina Woodford , Jason Li , Susan Harden , Sandro Porceddu , Nicholas Hardcastle , Vanessa Panettieri","doi":"10.1016/j.phro.2025.100890","DOIUrl":"10.1016/j.phro.2025.100890","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The monitor units (MU) per control point (CP) control the necessary fine-tuned ablative dose for hypofractionated radiotherapy of oligometastatic cancer. We aimed to introduce strategies maximising the sample size to accurately predict the MU per CP with artificial intelligence (AI).</div></div><div><h3>Materials and methods</h3><div>The 40/68/88 treatment plans of consecutive patients treated between 01/2019 and 06/2024 at our institution for metastatic cancer to the liver/bone/lung were included. Two approaches were considered to maximise the sample size. In one approach, the samples of each anatomical site were extensively augmented to predict the MU per CP from the dose distribution per CP, providing the MU per beam and meterset weight per CP. In the other approach, all samples from all anatomical sites were combined for training. The number of achieved clinical goals based on dose-volume calculation metrics in AI radiotherapy plans (AI-RTPlan) was compared with the number of achieved clinical goals in the clinical plans.</div></div><div><h3>Results</h3><div>The mean absolute percentage error between predicted and clinical MU per beam/meterset weight per CP was less than 6.2%. All AI-RTPlans were generated in less than 5 s. At least 90%/5% of patients had the same, or more, achieved clinical goals with AI-RTPlans. Target coverage and dose to organs at risk metrics were within ± 2% and ± 2.3 Gy of the clinical value in all patients, respectively.</div></div><div><h3>Conclusions</h3><div>Augmenting data extensively and combining anatomical sites were equivalent and proficient strategies to predict machine settings for radiotherapy planning of oligometastatic cancer.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100890"},"PeriodicalIF":3.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.phro.2025.100880
Anne L.H. Bisgaard , Chavelli M. Kensen , Marielle E.P. Philippens , Martijn P.W. Intven , Gert J. Meijer , Alice M. Couwenberg , Doenja M.J. Lambregts , Uulke A. van der Heide , Erik van der Bijl , Pètra M. Braam , Faisal Mahmood , Petra J. van Houdt
Background and purpose
The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI), a form of magnetic resonance imaging (MRI), has shown promise for predicting response to long course neoadjuvant chemoradiotherapy in rectal cancer. This study investigated whether ADC changes are detectable during short course radiotherapy in patients with rectal cancer.
Materials and methods
Across 3 centres, this study included 138 patients with primary tumours, who received neoadjuvant short course radiotherapy (5 fractions of 5 Gy) on a 1.5 T MRI linear accelerator (MRI-linac), without any prior oncological treatments. DWI was acquired at each fraction prior to beam-on. ADC maps were calculated centrally using a mono-exponential model using b-values between 150–800 s/mm2. Median scaling of ADC voxel values was performed between two identified groups of DWI sequences. Tumours were semi-automatically delineated on DWI, and median ADCs were extracted per fraction. ADC time-trends over the course of radiotherapy were extracted using linear fitting, with 95% confidence intervals (CI) estimated using bootstrapping.
Results
A scaling factor of 0.93 was used to account for ADC variation between the DWI sequence groups. The median (range) slope of the ADC time-trends was 0.05 (−0.18, 0.42) 10−3mm2/s/fraction. In 77 patients (56%), the 95% CI of the slope did not include zero.
Conclusions
ADC changes during short course radiotherapy were detectable in 56% of the patients. Furthermore, the limited ADC variation across DWI sequences supports feasibility of multicentre investigations of MRI-linac based DWI. These findings encourage future research linking ADC to clinical outcomes in rectal cancer for potential treatment personalization.
背景与目的磁共振成像（MRI）的一种形式——弥散加权成像（DWI）得出的表观扩散系数（ADC）有望预测直肠癌患者对长期新辅助放化疗的反应。本研究探讨了在直肠癌患者的短期放疗中是否可以检测到ADC的变化。材料和方法本研究纳入了3个中心的138例原发肿瘤患者，这些患者在1.5 T MRI直线加速器（MRI-linac）上接受了新辅助短期放疗（5 Gy的5个部分），之前没有任何肿瘤治疗。在光束照射前，在每个分数处获取DWI。ADC图使用单指数模型集中计算，b值在150-800 s/mm2之间。在确定的两组DWI序列之间进行ADC体素值的中位数缩放。在DWI上半自动划定肿瘤，并提取每个分数的中位adc。放疗过程中的ADC时间趋势采用线性拟合提取，95%置信区间（CI）采用自举法估计。结果DWI序列组间ADC差异的比例因子为0.93。ADC时间趋势的中位（范围）斜率为0.05 (- 0.18,0.42)10 - 3mm2/s/fraction。在77例（56%）患者中，斜率的95% CI不为零。结论56%的患者在短期放疗中可检测到sadc的改变。此外，DWI序列之间有限的ADC变化支持了基于MRI-linac的DWI多中心研究的可行性。这些发现鼓励未来的研究将ADC与直肠癌的临床结果联系起来，以实现潜在的个性化治疗。
{"title":"Apparent diffusion coefficient increases during short course radiotherapy in rectal tumours: Results from a multicentre longitudinal trial","authors":"Anne L.H. Bisgaard , Chavelli M. Kensen , Marielle E.P. Philippens , Martijn P.W. Intven , Gert J. Meijer , Alice M. Couwenberg , Doenja M.J. Lambregts , Uulke A. van der Heide , Erik van der Bijl , Pètra M. Braam , Faisal Mahmood , Petra J. van Houdt","doi":"10.1016/j.phro.2025.100880","DOIUrl":"10.1016/j.phro.2025.100880","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI), a form of magnetic resonance imaging (MRI), has shown promise for predicting response to long course neoadjuvant chemoradiotherapy in rectal cancer. This study investigated whether ADC changes are detectable during short course radiotherapy in patients with rectal cancer.</div></div><div><h3>Materials and methods</h3><div>Across 3 centres, this study included 138 patients with primary tumours, who received neoadjuvant short course radiotherapy (5 fractions of 5 Gy) on a 1.5 T MRI linear accelerator (MRI-linac), without any prior oncological treatments. DWI was acquired at each fraction prior to beam-on. ADC maps were calculated centrally using a mono-exponential model using b-values between 150–800 s/mm<sup>2</sup>. Median scaling of ADC voxel values was performed between two identified groups of DWI sequences. Tumours were semi-automatically delineated on DWI, and median ADCs were extracted per fraction. ADC time-trends over the course of radiotherapy were extracted using linear fitting, with 95% confidence intervals (CI) estimated using bootstrapping.</div></div><div><h3>Results</h3><div>A scaling factor of 0.93 was used to account for ADC variation between the DWI sequence groups. The median (range) slope of the ADC time-trends was 0.05 (−0.18, 0.42) 10<sup>−3</sup>mm<sup>2</sup>/s/fraction. In 77 patients (56%), the 95% CI of the slope did not include zero.</div></div><div><h3>Conclusions</h3><div>ADC changes during short course radiotherapy were detectable in 56% of the patients. Furthermore, the limited ADC variation across DWI sequences supports feasibility of multicentre investigations of MRI-linac based DWI. These findings encourage future research linking ADC to clinical outcomes in rectal cancer for potential treatment personalization.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100880"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.phro.2025.100879
José Antonio Baeza-Ortega , Lauren May , Mohammad Hussein , Sarah Porter , Alisha Moore , Peter B. Greer , Catharine H. Clark , Joerg Lehmann
Background and purpose
The role of dosimetry audits is well established in the development and verification of radiotherapy safety. Differences in planning and beam modelling make inter-centre comparisons challenging, which can be addressed through distribution of centrally created plans. This study developed a centralised planning approach applicable to multiple audit methodologies, using an example of remote patient specific quality assurance assessment, increasing the interpretability of results and facilitating automation and scalability.
Material and methods
Starting with an established plan which met all clinical goals, a commercial dose mimicking algorithm was used to replicate this plan to be suitable for multiple treatment machines. Beam and machine limitation data were collected from participating centres to develop universally acceptable beam models. The influence of variation in beam modelling parameters among centres was assessed by creating additional models using the 2.5th, 25th, 75th and 97.5th percentiles of previously reported data. Multi-leaf collimator angle and leaf position, gantry angle and output deviations were then introduced into copies of these plans.
Results
Introduced delivery errors caused consistent change in dose metrics across machine models (excluding outliers) with a median (range) standard deviation of 1.0 % (from 0.1 % to 1.7 %) demonstrating similar robustness. Beam model variation did not change whether simulated delivery errors were clinically impactful or not for 95 % of tested plans.
Conclusion
This study lays the foundation for future standardised methodology for dosimetry audits by providing a centralised planning approach that allows a more consistent assessment of centres.
{"title":"A proof of concept for improving comparability of dosimetry audits through centralised planning","authors":"José Antonio Baeza-Ortega , Lauren May , Mohammad Hussein , Sarah Porter , Alisha Moore , Peter B. Greer , Catharine H. Clark , Joerg Lehmann","doi":"10.1016/j.phro.2025.100879","DOIUrl":"10.1016/j.phro.2025.100879","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The role of dosimetry audits is well established in the development and verification of radiotherapy safety. Differences in planning and beam modelling make inter-centre comparisons challenging, which can be addressed through distribution of centrally created plans. This study developed a centralised planning approach applicable to multiple audit methodologies, using an example of remote patient specific quality assurance assessment, increasing the interpretability of results and facilitating automation and scalability.</div></div><div><h3>Material and methods</h3><div>Starting with an established plan which met all clinical goals, a commercial dose mimicking algorithm was used to replicate this plan to be suitable for multiple treatment machines. Beam and machine limitation data were collected from participating centres to develop universally acceptable beam models. The influence of variation in beam modelling parameters among centres was assessed by creating additional models using the 2.5th, 25th, 75th and 97.5th percentiles of previously reported data. Multi-leaf collimator angle and leaf position, gantry angle and output deviations were then introduced into copies of these plans.</div></div><div><h3>Results</h3><div>Introduced delivery errors caused consistent change in dose metrics across machine models (excluding outliers) with a median (range) standard deviation of 1.0 % (from 0.1 % to 1.7 %) demonstrating similar robustness. Beam model variation did not change whether simulated delivery errors were clinically impactful or not for 95 % of tested plans.</div></div><div><h3>Conclusion</h3><div>This study lays the foundation for future standardised methodology for dosimetry audits by providing a centralised planning approach that allows a more consistent assessment of centres.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100879"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.phro.2025.100862
Chunbo Tang , Houjin Zhang , Longqiu Wu , Minfeng Huang , Pengfei Wang , Jun Yuan , Junjie Zhang , Biaoshui Liu , Ji He
Hippocampal avoidance whole-brain radiotherapy (HA-WBRT) aims to preserve cognitive function during treatment for brain metastases. This study investigated the potential of Single-Target Longitudinal Segmentation Volumetric Modulated Arc Therapy (VMAT) in HA-WBRT, which segments the planning target volume (PTV) into sub-PTVs, using single or dual arcs to generate s-VMAT and d-VMAT strategies. For 20 patients, s-VMAT and d-VMAT achieved lower median Dmean values of 8.3 Gy and 8.1 Gy, and reduced the median Dmax to 13.5 Gy and 12.8 Gy, compared to traditional coplanar/non-coplanar VMAT plans. These strategies showed enhanced robustness but required more monitor units and greater delivery complexity.
{"title":"Positioning uncertainties in single-target longitudinal segmentation for hippocampal-avoidance whole brain radiotherapy using volumetric modulated arc therapy","authors":"Chunbo Tang , Houjin Zhang , Longqiu Wu , Minfeng Huang , Pengfei Wang , Jun Yuan , Junjie Zhang , Biaoshui Liu , Ji He","doi":"10.1016/j.phro.2025.100862","DOIUrl":"10.1016/j.phro.2025.100862","url":null,"abstract":"<div><div>Hippocampal avoidance whole-brain radiotherapy (HA-WBRT) aims to preserve cognitive function during treatment for brain metastases. This study investigated the potential of Single-Target Longitudinal Segmentation Volumetric Modulated Arc Therapy (VMAT) in HA-WBRT, which segments the planning target volume (PTV) into sub-PTVs, using single or dual arcs to generate s-VMAT and d-VMAT strategies. For 20 patients, s-VMAT and d-VMAT achieved lower median Dmean values of 8.3 Gy and 8.1 Gy, and reduced the median Dmax to 13.5 Gy and 12.8 Gy, compared to traditional coplanar/non-coplanar VMAT plans. These strategies showed enhanced robustness but required more monitor units and greater delivery complexity.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100862"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.phro.2025.100886
Libing Zhu , Yi Rong , Nathan Y. Yu , Jason M. Holmes , Carlos E. Vargas , Sarah E. James , Lu Shang , Jean-Claude M. Rwigema , Quan Chen
Background and purpose
Deep-learning auto-segmentation (DLAS) performance in radiotherapy may change over time due to data shift/drift or practice changes, yet guidance for quality assurance is lacking. This study developed a practical framework for prospective performance monitoring using retrospective data.
Methods
A total of 464 prostate cases over 20 months were retrospectively collected. Two commercial DLAS models were clinically used: model A (2D U-Net, January 2022–January 2023) and model B (3D U-Net, February–August 2023). The agreement between DLAS and clinical contours was assessed using Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Surface DSC with a 2 mm tolerance (SDSC). Statistical process control charts were created to monitor performance drift and model switching. The first 150 cases were used to define organ-specific control limits with two and three standard deviations of monthly mean values, .
Results
2 and 3-based control limits were established for the monthly average charts, ranging from DSC 0.82–0.97, HD95 1.4–10.5 mm, and SDSC 0.45–0.91 across organs. Model A showed stable performance, with 9–13 months per organ remaining within the 3 thresholds. In contrast, model B demonstrated a marked performance shift (p < 0.001), with all five organs exceeding both thresholds across all 7 months. The 2 thresholds were more sensitive in detecting mild deviations for model A, while both limits effectively identified the substantial drift of model B.
Conclusion
The monitoring system effectively detected out-of-distribution outliers and clinical practice changes, providing a reliable framework for early detection of monthly performance degradation.
{"title":"Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy","authors":"Libing Zhu , Yi Rong , Nathan Y. Yu , Jason M. Holmes , Carlos E. Vargas , Sarah E. James , Lu Shang , Jean-Claude M. Rwigema , Quan Chen","doi":"10.1016/j.phro.2025.100886","DOIUrl":"10.1016/j.phro.2025.100886","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Deep-learning auto-segmentation (DLAS) performance in radiotherapy may change over time due to data shift/drift or practice changes, yet guidance for quality assurance is lacking. This study developed a practical framework for prospective performance monitoring using retrospective data.</div></div><div><h3>Methods</h3><div>A total of 464 prostate cases over 20 months were retrospectively collected. Two commercial DLAS models were clinically used: model A (2D U-Net, January 2022–January 2023) and model B (3D U-Net, February–August 2023). The agreement between DLAS and clinical contours was assessed using Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Surface DSC with a 2 mm tolerance (SDSC). Statistical process control charts were created to monitor performance drift and model switching. The first 150 cases were used to define organ-specific control limits with two and three standard deviations of monthly mean values, <span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span>.</div></div><div><h3>Results</h3><div>2<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span> and 3<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span>-based control limits were established for the monthly average charts, ranging from DSC 0.82–0.97, HD95 1.4–10.5 mm, and SDSC 0.45–0.91 across organs. Model A showed stable performance, with 9–13 months per organ remaining within the 3<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span> thresholds. In contrast, model B demonstrated a marked performance shift (p < 0.001), with all five organs exceeding both thresholds across all 7 months. The 2<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span> thresholds were more sensitive in detecting mild deviations for model A, while both limits effectively identified the substantial drift of model B.</div></div><div><h3>Conclusion</h3><div>The monitoring system effectively detected out-of-distribution outliers and clinical practice changes, providing a reliable framework for early detection of monthly performance degradation.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100886"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oligometastatic disease represents limited metastatic burden, and local ablative therapies such as stereotactic body radiotherapy (SBRT) may improve survival. However, inter-institutional variability in target segmentation and treatment planning can compromise treatment quality. This study aimed to evaluate the segmentation variability and dose distribution quality of SBRT in oligometastatic settings using a multi-institutional dummy run approach.
Methods and materials
Sixty-nine institutions were provided with two anonymized cases of adrenal and spine metastases to delineate targets and organs at risk (OARs) and create intensity-modulated radiotherapy plans following a protocol. Variability was quantified using the Dice similarity coefficient (DSC), Hausdorff distance, and mean distance to agreement. Plan qualities were assessed using the Paddick conformity index, modified gradient index, and a new three-dimensional conformity–gradient index (3D-CGI). Knowledge-based planning (KBP) was applied to explore potential improvements in OAR sparing.
Results
All submitted plans met protocol dose constraints. However, substantial segmentation variability was observed, particularly for the spine case. Among 136 plans, 79% demonstrated acceptable conformity and dose gradients, with 3D-CGI < 6 correlating with favorable distributions. Mean DSC was 0.93 for the clinical target volume and 0.76 for the cauda equina, which showed the highest variability. KBP reduced OAR doses for the adrenal case but showed limited impact for the spine case.
Conclusions
Although dose constraints were achieved, segmentation variability remained substantial, particularly for the cauda equina in the spine case. These findings emphasize inter-institutional differences and the need for standardization and tools to improve SBRT consistency.
{"title":"A multi-institutional dummy run on segmentation variability and plan quality of stereotactic body radiotherapy for oligometastatic disease","authors":"Hideaki Hirashima , Yukinori Matsuo , Satoshi Ishikura , Mitsuhiro Nakamura , Ikuno Nishibuchi , Daisuke Kawahara , Yoshihisa Shimada , Yoshiro Nakahara , Teiji Nishio , Naoto Shikama , Shun-ichi Watanabe , Isamu Okamoto , Toshiyuki Ishiba , Fumikata Hara , Tadahiko Shien , Takashi Mizowaki","doi":"10.1016/j.phro.2025.100857","DOIUrl":"10.1016/j.phro.2025.100857","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Oligometastatic disease represents limited metastatic burden, and local ablative therapies such as stereotactic body radiotherapy (SBRT) may improve survival. However, inter-institutional variability in target segmentation and treatment planning can compromise treatment quality. This study aimed to evaluate the segmentation variability and dose distribution quality of SBRT in oligometastatic settings using a multi-institutional dummy run approach.</div></div><div><h3>Methods and materials</h3><div>Sixty-nine institutions were provided with two anonymized cases of adrenal and spine metastases to delineate targets and organs at risk (OARs) and create intensity-modulated radiotherapy plans following a protocol. Variability was quantified using the Dice similarity coefficient (DSC), Hausdorff distance, and mean distance to agreement. Plan qualities were assessed using the Paddick conformity index, modified gradient index, and a new three-dimensional conformity–gradient index (3D-CGI). Knowledge-based planning (KBP) was applied to explore potential improvements in OAR sparing.</div></div><div><h3>Results</h3><div>All submitted plans met protocol dose constraints. However, substantial segmentation variability was observed, particularly for the spine case. Among 136 plans, 79% demonstrated acceptable conformity and dose gradients, with 3D-CGI < 6 correlating with favorable distributions. Mean DSC was 0.93 for the clinical target volume and 0.76 for the cauda equina, which showed the highest variability. KBP reduced OAR doses for the adrenal case but showed limited impact for the spine case.</div></div><div><h3>Conclusions</h3><div>Although dose constraints were achieved, segmentation variability remained substantial, particularly for the cauda equina in the spine case. These findings emphasize inter-institutional differences and the need for standardization and tools to improve SBRT consistency.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100857"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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