Physics and Imaging in Radiation Oncology最新文献

{"title":"New guidelines and recommendations to advance treatment planning in proton therapy","authors":"Barbara Knäusl, Anne Vestergaard, Marco Schwarz, Ludvig P. Muren","doi":"10.1016/j.phro.2024.100695","DOIUrl":"10.1016/j.phro.2024.100695","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100695"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11764265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047934","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}

{"title":"Potential of automated online adaptive proton therapy to reduce margins for oesophageal cancer","authors":"Pascal Herbst ,&nbsp;Camille Draguet ,&nbsp;Ana M. Barragán-Montero ,&nbsp;Elena Borderías Villarroel ,&nbsp;Macarena Chocan Vera ,&nbsp;Pieter Populaire ,&nbsp;Karin Haustermans ,&nbsp;Edmond Sterpin","doi":"10.1016/j.phro.2025.100712","DOIUrl":"10.1016/j.phro.2025.100712","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>Proton therapy for oesophageal cancer is administered over multiple fractions, based on a single pre-treatment image. However, anatomical changes can lead to the deterioration of the treatment plan, necessitating manual replanning. To keep this within limits, increased residual margins are employed. This study aimed to evaluate the proposed automated Online Adaptive Proton Therapy (OAPT) strategies on their capability to reduce the need for manual replanning, while also exploring the possibility of margin reduction.</div></div><div><h3>Materials and methods:</h3><div>Two automated OAPT methods were examined: Automated Dose Restoration (ADR) and Automated Full Adaptation (AFA). ADR makes use of dose restoration, restoring the original dose map based on the patient’s altered anatomy. AFA adapts the contours used for plan optimization by applying a deformation field, not only correcting for density changes, but also for the relative location of organs. A comparative analysis of OAPT strategies, evaluating <span><math><msub><mrow><mi>D</mi></mrow><mrow><mtext>98%</mtext></mrow></msub></math></span> tumour coverage on 17 patients, was conducted.</div></div><div><h3>Results:</h3><div>The nominal results of non-adapted plans with 7 mm residual margins required manual replanning for 18% of the patients. ADR reduced this to 6%, while AFA eliminated the need for manual replanning. With 2 mm margins, 47% of cases required manual replanning. ADR reduced this to 18%, and AFA further reduced it to 11%.</div></div><div><h3>Conclusions:</h3><div>The proposed OAPT strategies offered a marked improvement compared to a non-adaptive approach. ADR and AFA significantly reduced the necessity for manual replanning and facilitated the reduction of residual margins, enhancing dose conformity and reducing treatment toxicity.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100712"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387668","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}

{"title":"Investigation of 2D anti-scatter grid implementation in a gantry-mounted cone beam computed tomography system for proton therapy","authors":"Uttam Pyakurel ,&nbsp;Yawei Zhang ,&nbsp;Ryan Sabounchi ,&nbsp;Farhang Bayat ,&nbsp;Sébastien Brousmiche ,&nbsp;Curtis Bryant ,&nbsp;Nancy Mendenhall ,&nbsp;Perry Johnson ,&nbsp;Cem Altunbas","doi":"10.1016/j.phro.2025.100730","DOIUrl":"10.1016/j.phro.2025.100730","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Robust scatter mitigation by 2D anti-scatter grids (2D-ASG) in proton therapy cone beam computed tomography (CBCT) may improve target visualization and computed tomography (CT) number fidelity, allowing online dose verifications and plan adaptations. However, grid artifact-free implementation of 2D-ASG depends on the CBCT system characteristics. Thus, we investigated the feasibility of 2D-ASG implementation in a proton therapy gantry-mounted CBCT system and evaluated its impact on image quality.</div></div><div><h3>Materials and methods</h3><div>A prototype 2D-ASG and a grid support platform were developed for a proton therapy CBCT system with a 340 cm source to imager distance. The effect of gantry flex on 2D-ASG’s wall shadows and scan-to-scan reproducibility of 2D-ASG’s wall shadows were evaluated. Experiments were conducted to assess 2D-ASG’s wall shadow suppression and the effect of 2D-ASG on image quality.</div></div><div><h3>Results</h3><div>While maximum displacement in 2D-ASG wall shadows was 103 µm during gantry rotation, the drift from baseline over 3 months was 8 µm and 1 µm in the transverse and axial directions. 2D-ASG shadows were successfully suppressed in CBCT images. With 2D-ASG, maximum Hounsfield Unit (HU) nonuniformity decreased from 134 to 45 HU, contrast-to-noise ratio (CNR) increased by a factor of 2.5, and HU errors were reduced from 34 % to 5 %.</div></div><div><h3>Conclusions</h3><div>Proton therapy gantry flex was highly reproducible and did not noticeably affect 2D-ASG wall shadow suppression in CBCT images, supporting its feasibility in proton therapy CBCT system. Improved CT accuracy and artifact reduction with 2D-ASG could enhance CBCT-based proton therapy dose calculations.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100730"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419857","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}

{"title":"Towards faster plan adaptation for proton arc therapy using initial treatment plan information","authors":"Benjamin Roberfroid ,&nbsp;Margerie Huet-Dastarac ,&nbsp;Elena Borderías-Villarroel ,&nbsp;Rodin Koffeing ,&nbsp;John A. Lee ,&nbsp;Ana M. Barragán-Montero ,&nbsp;Edmond Sterpin","doi":"10.1016/j.phro.2025.100705","DOIUrl":"10.1016/j.phro.2025.100705","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Proton arc therapy (PAT) is an emerging modality delivering continuously rotating proton beams. Current PAT planning approaches are time-consuming, making them unsuitable for online adaptation. This study proposes an accelerated workflow for adapting PAT plans.</div></div><div><h3>Materials and Methods</h3><div>The proposed workflow transfers spots from initial computed tomography (CT) to the CT of the day, updates energy layers considering the initial pattern, and re-optimizes selected transferred spots based on their initial weights and impact on the objective function.</div><div>A retrospective study was conducted on five head and neck patients who underwent plan adaptation on a repeated CT. PAT plans were generated with two different methods on the repeated CT: <em>reference</em>, created de novo, and <em>smart-adapted</em>, generated with the proposed adaptive workflow. Robust optimization was performed for all plans.</div></div><div><h3>Results</h3><div><em>Smart-adapted</em> plans achieved similar mean dose to organs at risk as the <em>reference</em>: the largest median increase of mean dose was 1.9 Gy to the mandible; the median of maximum dose to spinal cord was 0.5 Gy lower for the <em>smart-adapted</em> plans. The median target coverage, i.e. D₉₈, to primary tumor and nodes of <em>smart-adapted</em> plans decreased by 0.2 and 0.4 Gy for the nominal case, and 0.4 and 0.6 Gy for the worst-case scenario; all <em>smart-adapted</em> plans met clinical objectives. The smart-adaptation method reduced average planning time from 19184 s to 5626 s, a 3.4-fold improvement.</div></div><div><h3>Conclusions</h3><div><em>Smart-adapted</em> plans achieve similar plan quality to the reference method, while significantly reducing plan generation time for new patient anatomy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100705"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143270412","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}

{"title":"A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging","authors":"Luca Vellini ,&nbsp;Flaviovincenzo Quaranta ,&nbsp;Sebastiano Menna ,&nbsp;Elisa Pilloni ,&nbsp;Francesco Catucci ,&nbsp;Jacopo Lenkowicz ,&nbsp;Claudio Votta ,&nbsp;Michele Aquilano ,&nbsp;Andrea D’Aviero ,&nbsp;Martina Iezzi ,&nbsp;Francesco Preziosi ,&nbsp;Alessia Re ,&nbsp;Althea Boschetti ,&nbsp;Danila Piccari ,&nbsp;Antonio Piras ,&nbsp;Carmela Di Dio ,&nbsp;Alessandro Bombini ,&nbsp;Gian Carlo Mattiucci ,&nbsp;Davide Cusumano","doi":"10.1016/j.phro.2025.100708","DOIUrl":"10.1016/j.phro.2025.100708","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>The development of Magnetic Resonance Imaging (MRI)-only Radiotherapy (RT) represents a significant advancement in the field. This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored.</div></div><div><h3>Methods</h3><div>Fifty-six patients were divided into training (32), validation (8), and test (16) groups. A conditional Generative Adversarial Network (cGAN) was trained on pre-processed axial paired images. sCTs were validated using mean absolute error (MAE) and mean error (ME) calculated within the patient body. Intensity Modulated Radiation Therapy (IMRT) plans were optimised on simulation MRI and calculated considering sCT and original CT as electron density (ED) map. Dose distributions using sCT and CT were compared using global gamma analysis at different tolerance criteria (2 %/2mm and 3 %/3mm) and evaluating the difference in estimating different Dose Volume Histogram (DVH) parameters for target and organs at risk (OARs).</div></div><div><h3>Results</h3><div>The network generated sCTs of each single patient in less than two minutes (mean time = 103 ± 41 s). For test patients, the MAE was 62.1 ± 17.7 HU, and the ME was −7.3 ± 13.4 HU. Dose parameters on sCTs were within 0.5 Gy of those on original CTs. Gamma passing rates 2 %/2mm, and 3 %/3mm criteria were 99.5 %±0.5 %, and 99.7 %±0.3 %, respectively.</div></div><div><h3>Conclusion</h3><div>The proposed DL algorithm generates in less than 2 min accurate sCT images in the brain for online adaptive radiotherapy, potentially eliminating the need for CT simulation in MR-only workflows for brain treatments.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100708"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130582","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}

{"title":"Updating historical normal tissue dose/volume constraints to current levels of treatment precision and accuracy","authors":"Tomas Kron,&nbsp;Marnix Witte,&nbsp;Ludvig P. Muren","doi":"10.1016/j.phro.2025.100725","DOIUrl":"10.1016/j.phro.2025.100725","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100725"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394827","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}

{"title":"Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system","authors":"Gerd Heilemann ,&nbsp;Lukas Zimmermann ,&nbsp;Tufve Nyholm ,&nbsp;Attila Simkó ,&nbsp;Joachim Widder ,&nbsp;Gregor Goldner ,&nbsp;Dietmar Georg ,&nbsp;Peter Kuess","doi":"10.1016/j.phro.2025.100724","DOIUrl":"10.1016/j.phro.2025.100724","url":null,"abstract":"<div><div>We present an automated radiation oncology treatment planning pipeline that operates between segmentation and plan review, minimizing manual interaction and reliance on traditional planning systems. Two AI models work in sequence: the first generates a dose distribution, and the second creates a deliverable DICOM-RT plan. Trained and validated on 276 plans, and tested on 151 datasets, the system produced clinically deliverable plans—complete with all VMAT parameters—in about 38 s. These plans met target coverage and most organ-at-risk constraints. This proof-of-concept demonstrates the feasibility of generating high-quality, deliverable DICOM plans within seconds.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100724"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387667","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}

{"title":"Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patients","authors":"Kim Hochreuter ,&nbsp;Gregory Buti ,&nbsp;Ali Ajdari ,&nbsp;Christopher P. Bridge ,&nbsp;Gregory C. Sharp ,&nbsp;Sune Jespersen ,&nbsp;Slávka Lukacova ,&nbsp;Thomas Bortfeld ,&nbsp;Jesper F. Kallehauge","doi":"10.1016/j.phro.2024.100688","DOIUrl":"10.1016/j.phro.2024.100688","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>Diffusion tensor imaging (DTI) has been proposed to guide the anisotropic expansion from gross tumor volume to clinical target volume (CTV), aiming to integrate known tumor spread patterns into the CTV. This study investigate the potential of using a DTI atlas as an alternative to patient-specific DTI for generating anisotropic CTVs.</div></div><div><h3>Materials and Methods:</h3><div>The dataset consisted of twenty-eight newly diagnosed glioblastoma patients from a Danish national DTI protocol with post-operative T1-contrast and DTI imaging. Three different DTI atlases, spatially aligned to the patient images using deformable image registration, were considered as alternatives. Anisotropic CTVs were constructed to match the volume of a 15 mm isotropic expansion by generating 3D distance maps using either patient- or atlas-DTI as input to the shortest path solver. The degree of CTV anisotropy was controlled by the migration ratio, modeling tumor cell migration along the dominant white matter fiber direction extracted from DTI. The similarity between patient- and atlas-DTI CTVs was analyzed using the Dice Similarity Coefficient (DSC), with significance testing according to a Wilcoxon test.</div></div><div><h3>Results:</h3><div>The median (range) DSC between anisotropic CTVs generated using patient-specific and atlas-based DTI was 0.96 (0.93–0.97), 0.96 (0.93–0.97), and 0.95 (0.93–0.97) for the three atlases, respectively (p <span><math><mo>&gt;</mo></math></span> 0.01), for a migration ratio of 10. The results remained consistent over the range of studied migration ratios (2 to 100).</div></div><div><h3>Conclusion:</h3><div>The high degree of similarity between all anisotropic CTVs indicates that atlas-DTI is a viable replacement for patient-specific DTI for incorporating fiber direction into the CTV.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100688"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11758580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047930","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}

{"title":"Effect of end expiration breath hold on target volumes and organ at risk doses for oesophageal cancer radiotherapy","authors":"Christopher Mayhew ,&nbsp;Jeyaanth Venkatasai ,&nbsp;Marina Khan ,&nbsp;Victoria Butterworth ,&nbsp;Kasia Owczarczyk ,&nbsp;Georgios Ntentas","doi":"10.1016/j.phro.2025.100726","DOIUrl":"10.1016/j.phro.2025.100726","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>The end expiration breath hold (EEBH) technique has the potential to reduce tumour motion during radiotherapy treatment of lower oesophageal cancer, and therefore, motion artefacts, target volumes and dose to surrounding organs at risk (OAR). EEBH is an emerging technique and clinical data on its use in oesophageal cancer is scarce.</div></div><div><h3>Methods and Materials</h3><div>A comparison of 20 lower oesophageal cancer patients was performed for radiotherapy treatment plans in both EEBH and free breathing (FB). EEBH and FB plans were evaluated and compared in terms of motion artefacts, target volumes and dose-volume metrics.</div></div><div><h3>Results</h3><div>EEBH was effective in reducing the observed motion artefacts seen in planning CTs compared to FB. EEBH also significantly reduced the average PTV size between EEBH and FB (ΔV = -48 ± 55 cm³; p &lt; 0.001). OAR-PTV overlap volumes were also effectively reduced in EEBH compared to FB, including for lung-PTV overlaps (ΔV = -13 ± 13 cm³; p &lt; 0.001) and for heart-PTV overlaps (ΔV = -8 ± 14 cm³; p = 0.02). Mean heart doses were lower on average by −1.2 ± 2.0 Gy with EEBH (p = 0.02), and mean lung doses by −1.0 ± 1.0 Gy (p &lt; 0.001). Mean liver doses were on average reduced with EEBH by −0.6 ± 1.5 Gy, whereas spinal D_2cm3 increased in EEBH compared to FB by 1.8 ± 6.3 Gy, but neither were statistically significant.</div></div><div><h3>Conclusion</h3><div>Use of EEBH for oesophageal cancer radiotherapy reduced motion artefacts and increased confidence in contouring volumes. Additionally, planning target volumes and doses to key OARs were reduced with EEBH compared to FB plans.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100726"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446139","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}

{"title":"A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer","authors":"Edoardo Mastella ,&nbsp;Francesca Calderoni ,&nbsp;Luigi Manco ,&nbsp;Martina Ferioli ,&nbsp;Serena Medoro ,&nbsp;Alessandro Turra ,&nbsp;Melchiore Giganti ,&nbsp;Antonio Stefanelli","doi":"10.1016/j.phro.2025.100731","DOIUrl":"10.1016/j.phro.2025.100731","url":null,"abstract":"<div><h3>Purpose</h3><div>Adaptive radiotherapy (ART) may improve treatment quality by monitoring variations in patient anatomy and incorporating them into the treatment plan. This systematic review investigated the role of artificial intelligence (AI) in computed tomography (CT)-based ART for head and neck (H&amp;N) cancer.</div></div><div><h3>Methods</h3><div>A comprehensive search of main electronic databases was conducted until April 2024. Titles and abstracts were reviewed to evaluate the compliance with inclusion criteria: CT-based imaging for photon ART of H&amp;N patients and AI applications. 17 original retrospective studies with samples sizes ranging from 37 to 239 patients were included. The quality of the studies was evaluated with the Quality Assessment of Diagnostic Accuracy Studies-2 and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. Key metrics were examined to evaluate the performances of the proposed AI-methods.</div></div><div><h3>Results</h3><div>Overall, the risk of bias was low. The average CLAIM score was 70%. A major finding was that generated synthetic CTs improved similarity metrics with planning CT compared to original cone-beam CTs, with average mean absolute error up to 39 HU and maximum improvement of 80%. Auto-segmentation provided an efficient and accurate option for organ-at-risk delineation, with average Dice similarity coefficient ranging from 80 to 87%. Finally, AI models could be trained using clinical and radiomic features to predict the effectiveness of ART with accuracy above 80%.</div></div><div><h3>Conclusions</h3><div>Automation of processes in ART for H&amp;N cancer is very promising throughout the entire chain, from the generation of synthetic CTs and auto-segmentation to predict the effectiveness of ART.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100731"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446137","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}