Physics and Imaging in Radiation Oncology最新文献

{"title":"Dosimetric effect of abdominal compression in online adaptive planning for abdominal cancers treated with a 1.5 Tesla magnetic resonance-guided linear accelerator","authors":"Moghadaseh Khaleghibizaki ,&nbsp;Angela Sobremonte ,&nbsp;Luis Perles ,&nbsp;Surendra Prajapati ,&nbsp;Ergys Subashi ,&nbsp;Yao Ding ,&nbsp;Kristy Brock ,&nbsp;Roya Barati ,&nbsp;Eugene Koay ,&nbsp;Chad Tang ,&nbsp;Jinzhong Yang","doi":"10.1016/j.phro.2025.100899","DOIUrl":"10.1016/j.phro.2025.100899","url":null,"abstract":"<div><div>Compression belts (CBs) are sometimes used to reduce respiratory motion during stereotactic body radiotherapy of abdominal cancers with magnetic resonance (MR)-guided online adaptive planning. This study evaluated the dosimetric effects of overriding the relative electron density (ED) value of CBs in creating synthetic computed tomography (CT) scans for MR-guided adaptive planning. We evaluated plans for 12 patients with abdominal cancer and identified that ED values between 0.2 and 0.3 achieved the best approximation of CB ED in dose calculation. Our study presented an approach to estimate appropriate ED overrides for CBs in MR-guided online adaptive planning.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100899"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842007","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}

{"title":"Implementing a fully computed tomography-free online adaptive palliative radiotherapy: a one-visit workflow","authors":"Ashaya T. Jaglal ,&nbsp;Koen J. Nelissen ,&nbsp;Angelique R.W. van Vlaenderen ,&nbsp;Amy L. de la Fuente ,&nbsp;Famke L. Schneiders ,&nbsp;Peter S.N. van Rossum ,&nbsp;Jan Wiersma ,&nbsp;Wilko F.A.R. Verbakel ,&nbsp;Suresh Senan ,&nbsp;Jorrit Visser ,&nbsp;Eva Versteijne","doi":"10.1016/j.phro.2025.100896","DOIUrl":"10.1016/j.phro.2025.100896","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Same-day palliative radiotherapy requires rapid workflows, but conventional computed tomography (CT)-based workflows cause delays and strain resources. Advances in cone-beam CT (CBCT) enabled accurate dose calculation and planning without a planning CT. This study evaluated the feasibility and efficiency of a fully CT-free online adaptive workflow for same-day palliative radiotherapy using high-quality CBCT.</div></div><div><h3>Methods and materials</h3><div>This prospective study enrolled sixteen patients between January–May 2025, of whom fifteen completed same-day treatment. Eligible patients were referred for single-fraction palliative radiotherapy (8  Gy) to non-mobile target volumes. No planning CT was acquired; instead, a reference plan was generated on a phantom with standardized beam setups and planning objectives. On the treatment day, planning and delivery were performed on the Varian Ethos 2.0 platform using HyperSight CBCT, providing more accurate Hounsfield Unit imaging for automated organs at risk segmentation and target definition. Plans were adapted online and delivered while patients were on the couch. Workflow times, plan quality, and patient characteristics were studied.</div></div><div><h3>Results</h3><div>All fifteen treatments were delivered successfully. All plans met clinical objectives, with planning target volume coverage exceeding required thresholds. The CT-free workflow reduced median departmental time to 73 min, including 28 min in the treatment room, compared with 335 min in a conventional CT-based workflow. In one urgent case, referral-to-treatment time was 2.5 h. Repeated CBCTs were required in 7 patients.</div></div><div><h3>Conclusions</h3><div>A fully CT-free workflow for palliative radiotherapy is feasible and efficient, enabling same-day treatment, reduces departmental workload, and is well-suited for urgent cases requiring rapid intervention.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100896"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842009","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}

{"title":"Quality assurance of online adaptive radiotherapy workflows using film dosimetry in a 3D printed thorax anthropomorphic phantom","authors":"Daan Hoffmans ,&nbsp;Koen Nelissen ,&nbsp;Eva Versteijne ,&nbsp;Wilko Verbakel","doi":"10.1016/j.phro.2026.100909","DOIUrl":"10.1016/j.phro.2026.100909","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Quality Assurance for online adaptive radiotherapy (oART) can be challenging. Several tests can demonstrate the dosimetric and position accuracy, but commercial phantoms are often not anatomically representative. The aim of this study was to investigate the accuracy of cone-beam computed tomography guided oART palliative and breast cancer trials by using a 3D<!--> <!-->printed thorax anthropomorphic phantom.</div></div><div><h3>Materials and methods</h3><div>An anthropomorphic phantom was 3D<!--> <!-->printed for this study which accommodates film through the spine, breast, heart, and lungs. Dose was measured for spine and breast treatment plans, whilst variations were simulated which can occur during treatment. Measurements were compared to calculated dose on the planning (pCT) and synthetic computed tomography (sCT) using gamma pass rate criteria of minimal 95<!--> <!--> % (for gamma of 4<!--> <!--> %/2<!--> <!-->mm). Differences between the mean gamma were tested for significance.</div></div><div><h3>Results</h3><div>Measurements done with positional and target volume changes showed no significant difference between the gamma analyses for the pCT and sCT (p = 0.15), indicating a robust and safe workflow. For extreme variations, difference was found between gamma analyses for the pCT and sCT (p = 0.051). Pass rates were all &gt;95<!--> <!--> %, except for three measurements in which the sCT showed density errors up to 1000 Hounsfield<!--> <!-->Units.</div></div><div><h3>Conclusions</h3><div>This QA approach for oART, which used film measurements in a custom 3D-printed anthropomorphic phantom was able to validate the accuracy of the oART workflow when anatomical deviations arise and could be suitable as end-to-end test in the future.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100909"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078123","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}

{"title":"Deep learning and dual-radiomics model incorporating brachytherapy applicator type to predict radiation-induced acute rectal injury in cervical cancer patients","authors":"Boda Ning ,&nbsp;Zhengxian Li ,&nbsp;Deyang Yu ,&nbsp;Chenyu Li ,&nbsp;Qi Liu ,&nbsp;Yanling Bai","doi":"10.1016/j.phro.2026.100908","DOIUrl":"10.1016/j.phro.2026.100908","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Radiation-induced acute rectal injury (RARI) is a common early toxicity after radiotherapy for cervical cancer (CC) and remains difficult to predict before treatment, which can adversely affect life quality of patients. We aimed to develop a combined dual-radiomics and deep learning (DL) model to improve the prediction of RARI in CC patients treated with radiotherapy.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included 200 CC patients from one hospital, randomly divided into training (n = 160), internal validation (n = 40) cohorts and external validation (n = 40) from another hospital. Patients were classified as RARI (CTCAE v5.0 grade ≥ 2) or Non-RARI (grade &lt; 2). Radiomic and dosiomic features were extracted from CT images and dose distributions, and DL features were learned using 3D CNNs. The performance of radiomics, dosiomics, DL and hybrid features models for RARI prediction was compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC).</div></div><div><h3>Results</h3><div>For radiomics combining dosiomics, XGBoost achieved the best performance with AUCs of 0.786 and 0.755 in internal and external validation cohorts, respectively. For DL, Resnet_with_CBAM achieved the best performance in the input of combining CT and dose distribution with AUCs of 0.786 and 0.773 in internal and external validation cohorts, respectively. Nomogram integrating radiomics, dosiomics, DL features, and clinical factor improved the AUC to 0.810, 0.803 in internal and external validation cohorts, respectively.</div></div><div><h3>Conclusion</h3><div>The nomogram integrating radiomics, dosiomics, DL, and clinical factors can improve the predictive performance for RARI in CC patients followed by radiotherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100908"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038122","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}

{"title":"Feasibility of reusing online-generated treatment plans for adaptive radiotherapy in prostate cancer","authors":"Sarah A. Mason ,&nbsp;Bethany Williams ,&nbsp;Sophie Alexander ,&nbsp;Alex Dunlop ,&nbsp;Alison Tree ,&nbsp;Emma J. Harris ,&nbsp;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":"2026-01-01","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}

{"title":"Application of radiomics-based image filtering to improve deformable image registration accuracy in thoracic images","authors":"Yoshiro Ieko ,&nbsp;Noriyuki Kadoya ,&nbsp;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":"2026-01-01","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}

{"title":"Development and characterization of phantoms to investigate the Flash effect with Drosophila melanogaster at an ultra-high dose rate radiotherapy linac","authors":"Riccardo Dal Bello ,&nbsp;Marvin Kreuzer ,&nbsp;Irene Vetrugno ,&nbsp;Jamie C. Little ,&nbsp;Rafael Kranzer ,&nbsp;Stefan Schischke ,&nbsp;Lily Bossin ,&nbsp;Eduardo Gardenali Yukihara ,&nbsp;Matthias Guckenberger ,&nbsp;Martin Pruschy ,&nbsp;Stephanie Tanadini-Lang","doi":"10.1016/j.phro.2025.100835","DOIUrl":"10.1016/j.phro.2025.100835","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Ultra-high dose rate (UHDR) radiotherapy may widen the therapeutic window thanks to the Flash effect. Experimental linear accelerators have been converted to UHDR to collect pre-clinical evidence. Increasing the accessibility, throughput and investigating additional biological endpoints is key for deciphering the mechanism of the Flash effect. The aim of this study was to develop and characterise an experimental platform for UHDR experiments with <em>Drosophila melanogaster</em>, i.e. the fruit fly.</div></div><div><h3>Materials and methods</h3><div>A clinical linear accelerator was modified to deliver 16 MeV electron beams in UHDR and conventional (CONV) mode. Two phantoms were developed to irradiate <em>Drosophila melanogaster</em>. The characterization was based both on active (ultra-thin ion chamber prototype, scintillator) and passive detectors (radiochromic films, OSLD). Moreover, the UHDR capabilities for megavoltage photon were investigated with an additional dedicated phantom.</div></div><div><h3>Results</h3><div>The electron UHDR irradiations provided average dose rates in the range of 200–––7500 Gy/s. The beam spatial uniformity within a single vial was better than ± 5 %. The dose delivered to <em>Drosophila</em> melanogaster in different configurations and beam modalities was confirmed to the ± 5 % level. The average dose rate achieved with photon megavoltage UHDR radiation reached beyond 40 Gy/s.</div></div><div><h3>Conclusions</h3><div>This high-throughput experimental platform on a converted clinical linear accelerator could be used to compare CONV to UHDR for up to 500 animals per week for biological endpoints at up to 1000 Gy. The production of photon megavoltage UHDR radiation was also demonstrated for the first time at a converted clinical linac.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100835"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098016","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}

{"title":"Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy","authors":"Libing Zhu ,&nbsp;Yi Rong ,&nbsp;Nathan Y. Yu ,&nbsp;Jason M. Holmes ,&nbsp;Carlos E. Vargas ,&nbsp;Sarah E. James ,&nbsp;Lu Shang ,&nbsp;Jean-Claude M. Rwigema ,&nbsp;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 &lt; 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}

{"title":"Apparent diffusion coefficient increases during short course radiotherapy in rectal tumours: Results from a multicentre longitudinal trial","authors":"Anne L.H. Bisgaard ,&nbsp;Chavelli M. Kensen ,&nbsp;Marielle E.P. Philippens ,&nbsp;Martijn P.W. Intven ,&nbsp;Gert J. Meijer ,&nbsp;Alice M. Couwenberg ,&nbsp;Doenja M.J. Lambregts ,&nbsp;Uulke A. van der Heide ,&nbsp;Erik van der Bijl ,&nbsp;Pètra M. Braam ,&nbsp;Faisal Mahmood ,&nbsp;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/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.</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) 10⁻³mm²/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}

{"title":"A proof of concept for improving comparability of dosimetry audits through centralised planning","authors":"José Antonio Baeza-Ortega ,&nbsp;Lauren May ,&nbsp;Mohammad Hussein ,&nbsp;Sarah Porter ,&nbsp;Alisha Moore ,&nbsp;Peter B. Greer ,&nbsp;Catharine H. Clark ,&nbsp;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}