Physics and Imaging in Radiation Oncology最新文献

{"title":"Feasibility and time gain of implementing artificial intelligence-based delineation tools in daily magnetic resonance image-guided adaptive prostate cancer radiotherapy","authors":"Maximilian Lukas Konrad ,&nbsp;Carsten Brink ,&nbsp;Anders Smedegaard Bertelsen ,&nbsp;Ebbe Laugaard Lorenzen ,&nbsp;Bahar Celik ,&nbsp;Christina Junker Nyborg ,&nbsp;Lars Dysager ,&nbsp;Tine Schytte ,&nbsp;Uffe Bernchou","doi":"10.1016/j.phro.2024.100694","DOIUrl":"10.1016/j.phro.2024.100694","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Daily magnetic resonance image (MRI)-guided radiotherapy plan adaptation requires time-consuming manual contour edits of targets and organs at risk in the online workflow. Recent advances in auto-segmentation promise to deliver high-quality delineations within a short time frame. However, the actual time benefit in a clinical setting is unknown. The current study investigated the feasibility and time gain of implementing online artificial intelligence (AI)-based delineations at a 1.5 T MRI-Linac.</div></div><div><h3>Materials and methods</h3><div>Fifteen consecutive prostate cancer patients, treated to 60 Gy in 20 fractions at a 1.5 T MRI-Linac, were included in the study. The first 5 patients (Group 1) were treated using the standard contouring workflow for all fractions. The last 10 patients (Group 2) were treated with the standard workflow for fractions 1 up to 3 (Group 2 – Standard) and an AI-based workflow for the remaining fractions (Group 2 – AI). AI delineations were delivered using an in-house developed AI inference service and an in-house trained nnU-Net.</div></div><div><h3>Results</h3><div>The AI-based workflow reduced delineation time from 9.8 to 5.3 min. The variance in delineation time seemed to increase during the treatment course for Group 1, while the delineation time for the AI-based workflow was constant (Group 2 – AI). Fewer occurrences of readaptation due to target movement occurred with the AI-based workflow.</div></div><div><h3>Conclusion</h3><div>Implementing an AI-based workflow at the 1.5 T MRI-Linac is feasible and reduces the delineation time. Lower variance in delineation duration supports a better ability to plan daily treatment schedules and avoids delays.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100694"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068424","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":"Micro silica bead scintillators for the relative dosimetry of a stereotactic radiosurgery unit","authors":"Chris J. Stepanek ,&nbsp;Jack D. Aylward ,&nbsp;Ronald Hartley-Davies ,&nbsp;Lucy Winch","doi":"10.1016/j.phro.2025.100709","DOIUrl":"10.1016/j.phro.2025.100709","url":null,"abstract":"<div><div>This work describes the procedure of using Micro Silica Beads (MSBs) to verify the output factors and profiles of a stereotactic radiosurgery unit. MSBs have shown acceptable dosimetric accuracy for measurement of Detector Output Ratios (DORs) and shot profiles, down to a full width at half maximum (FWHM) of 5 mm. DORs measured with MSBs were within 1.5 % of radiochromic film, and 3 % of a microdiamond detector. Measured FWHM were within 0.2 mm of planning system and radiochromic film. MSBs can be used as an effective substitute to radiochromic film for measurement of shot profiles and DORs.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100709"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130455","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":"Linear approximation of variable relative biological effectiveness models for proton therapy","authors":"Dirk Wagenaar,&nbsp;Johannes A. Langendijk,&nbsp;Stefan Both","doi":"10.1016/j.phro.2024.100691","DOIUrl":"10.1016/j.phro.2024.100691","url":null,"abstract":"<div><div>The McNamara (MCN) and Wedenberg (WED) RBE weighted dose (D_RBE), dose and dose-weighted average LET (LET_d) were calculated in twenty brain cancer patients. A linear approximation was made for each RBE model to give best agreement to clinically relevant dosimetric parameters. Additional evaluations were done on twenty head and neck and twenty breast cancer patients.The R² of the fits was ≥0.94 and ≥0.91 for MCN and WED respectively for α/β values ≥1.0 Gy. The graphs derived in this work can be used to convert RBE-LET slopes derived from clinical data to α/β values in the MCN or WED models.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100691"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068554","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":"Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy","authors":"Josh Mason,&nbsp;Jack Doherty,&nbsp;Sarah Robinson,&nbsp;Meagan de la Bastide,&nbsp;Jack Miskell,&nbsp;Ruth McLauchlan","doi":"10.1016/j.phro.2025.100716","DOIUrl":"10.1016/j.phro.2025.100716","url":null,"abstract":"<div><div>For 18 months following clinical introduction of deep-learning auto-segmentation (DLAS), an audit of organ at risk (OAR) contour editing was performed, including 1255 patients from a single institution and the majority of tumour sites. Mean surface-Dice similarity coefficient increased from 0.87 to 0.97, the number of unedited OARs increased from 21.5 % to 40 %. The audit identified changes in editing corresponding to vendor model changes, adaption of local contouring practice and reduced editing in areas of no clinical significance. The audit allowed assessment of the level and frequency of editing and identification of outlier cases.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100716"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130571","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":"Comparative treatment planning of very high-energy electrons and photon volumetric modulated arc therapy: Optimising energy and beam parameters","authors":"Fabio S. D’Andrea ,&nbsp;Robert Chuter ,&nbsp;Adam H. Aitkenhead ,&nbsp;Ranald I. MacKay ,&nbsp;Roger M. Jones","doi":"10.1016/j.phro.2025.100732","DOIUrl":"10.1016/j.phro.2025.100732","url":null,"abstract":"<div><h3>Background</h3><div>Very High-Energy Electron (VHEE) beams offer potential advantages over current clinical radiotherapy modalities due to their precise dose targeting and minimal peripheral dose spread, which is ideal for treating deep-seated tumours. To aid the development of clinical VHEE machines, this study adressed the need to identify optimum VHEE beam characteristics for tumours across various anatomical sites.</div></div><div><h3>Materials and methods</h3><div>VHEE treatment planning employed matRad, an open-source treatment planning system, by adapting its proton pencil beam scanning implementation. VHEE beam characteristics were generated using TOPAS Monte Carlo simulations. A total of 820 plans were retrospectively created and analysed across 10 pelvic and 12 thoracic cases and compared against clinical photon VMAT plans to identify the most optimal VHEE beam configuration and energy requirement.</div></div><div><h3>Results</h3><div>VHEE plans outperformed photon VMAT in sparing organs-at-risk (OARs) while maintaining or improving target coverage. While 150 MeV served as the threshold for effectively treating deep-seated sites, 200 MeV was identified as a more optimal energy in the pelvis for achieving the best balance of penetration and sparing abutting OARs. Lower energies (70–110 MeV) also benefitted mid-to-superficial disease in the lung cohort. Typically, VHEE plans required 3–5 fields, and resulted in notable dose reductions to OARs across treatment sites, including: 22.5% reduction in rectal D_mean; 13.8% decrease in bladder D_mean; 8.2% reduction in heart D_mean; and a 24.4% decrease in lung V_20Gy.</div></div><div><h3>Conclusion</h3><div>The study reinforces VHEE’s potential in clinical settings, emphasising the need for varied energy ranges to enhance treatment flexibility and effectiveness.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100732"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients","authors":"Baoqiang Ma ,&nbsp;Alessia De Biase ,&nbsp;Jiapan Guo ,&nbsp;Lisanne V. van Dijk ,&nbsp;Johannes A. Langendijk ,&nbsp;Stefan Both ,&nbsp;Peter M.A. van Ooijen ,&nbsp;Nanna M. Sijtsema","doi":"10.1016/j.phro.2025.100733","DOIUrl":"10.1016/j.phro.2025.100733","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Deep learning (DL) models can extract prognostic image features from pre-treatment PET/CT scans. The study objective was to explore the potential benefits of incorporating pathologic lymph node (PL) spatial information in addition to that of the primary tumor (PT) in DL-based models for predicting local control (LC), regional control (RC), distant-metastasis-free survival (DMFS), and overall survival (OS) in oropharyngeal cancer (OPC) patients.</div></div><div><h3>Materials and methods</h3><div>The study included 409 OPC patients treated with definitive (chemo)radiotherapy between 2010 and 2022. Patient data, including PET/CT scans, manually contoured PT (GTVp) and PL (GTVln) structures, clinical variables, and endpoints, were collected. Firstly, a DL-based method was employed to segment tumours in PET/CT, resulting in predicted probability maps for PT (TPMp) and PL (TPMln). Secondly, different combinations of CT, PET, manual contours and probability maps from 300 patients were used to train DL-based outcome prediction models for each endpoint through 5-fold cross validation. Model performance, assessed by concordance index (C-index), was evaluated using a test set of 100 patients.</div></div><div><h3>Results</h3><div>Including PL improved the C-index results for all endpoints except LC. For LC, comparable C-indices (around 0.66) were observed between models trained using only PT and those incorporating PL as additional structure. Models trained using PT and PL combined into a single structure achieved the highest C-index of 0.65 and 0.80 for RC and DMFS prediction, respectively. Models trained using these target structures as separate entities achieved the highest C-index of 0.70 for OS.</div></div><div><h3>Conclusion</h3><div>Incorporating lymph node spatial information improved the prediction performance for RC, DMFS, and OS.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100733"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437653","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}

{"title":"Isolating the impact of tissue heterogeneities in high dose rate brachytherapy treatment of the breast","authors":"Jules Faucher ,&nbsp;Vincent Turgeon ,&nbsp;Boris Bahoric ,&nbsp;Shirin A. Enger ,&nbsp;Peter G.F. Watson","doi":"10.1016/j.phro.2025.100737","DOIUrl":"10.1016/j.phro.2025.100737","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Clinical brachytherapy treatment planning is performed assuming the patient is composed entirely of water and infinite in size. In this work, the effects of this assumption on calculated dose were investigated by comparing dose to water in water (D_w,w) in an unbound phantom mimicking TG-43 conditions, and dose to medium in medium (D_m,m) for breast cancer patients treated with high dose rate brachytherapy.</div></div><div><h3>Materials and methods</h3><div>Treatment plans for 123 breast cancer patients were recalculated with a Monte Carlo-based treatment planning software. The dwell times and dwell positions were imported from the clinical treatment planning system. The dose was computed and reported as D_w,w and D_m,m. Dose-volume histogram (DVH) metrics were evaluated for target volumes and organs at risk.</div></div><div><h3>Results</h3><div>D_w,w overestimated the dose for most studied DVH metrics. The largest median overestimations between D_m,m and D_w,w were seen for the planning target volume (PTV) V_200% (5.8%), lung D_{0.1 cm}³ (6.0%) and skin D_{0.1 cm}³ (4.2%). The differences between D_m,m and D_w,w were statistically significant for all investigated DVH metrics_. The PTV V_90% had the smallest deviation (0.7%).</div></div><div><h3>Conclusion</h3><div>There was a significant difference in the DVH metrics studied when tissue heterogeneities and patient-specific scattering are accounted for in high dose rate breast brachytherapy. However, for the studied patient cohort, the clinical coverage goal (PTV V_90%), had the smallest deviation.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100737"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508690","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":"Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk","authors":"Keeva Moran,&nbsp;Claire Poole,&nbsp;Sarah Barrett","doi":"10.1016/j.phro.2025.100736","DOIUrl":"10.1016/j.phro.2025.100736","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Delineation of target volumes (TVs) and organs at risk (OARs) is a resource intensive process in lung radiation therapy and, despite the introduction of some auto-contouring, inter-observer variability remains a challenge. Deep learning algorithms may prove an efficient alternative and this review aims to map the evidence base on the use of deep learning algorithms for TV and OAR delineation in the radiation therapy planning process for lung cancer patients.</div></div><div><h3>Materials and methods</h3><div>A literature search identified studies relating to deep learning. Manual contouring and deep learning auto-contours were evaluated against one another for accuracy, inter-observer variability, contouring time and dose-volume effects. A total of 40 studies were included for review.</div></div><div><h3>Results</h3><div>Thirty nine out of 40 studies investigated the accuracy of deep learning auto-contours and determined that they were of a comparable accuracy to manual contours. Inter-observer variability outcomes were heterogeneous in the seven relevant studies identified. Twenty-four studies analysed the time saving associated with deep learning auto-contours and reported a significant time reduction in comparison to manual contours. The eight studies that conducted a dose-volume metric evaluation on deep learning auto-contours identified negligible effect on treatment plans.</div></div><div><h3>Conclusion</h3><div>The accuracy and time-saving capacity of deep learning auto-contours in comparison to manual contours has been extensively studied. However, additional research is required in the areas of inter-observer variability and dose-volume metric evaluation to further substantiate its clinical use.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100736"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translation of dynamic contrast-enhanced imaging onto a magnetic resonance-guided linear accelerator in patients with head and neck cancer","authors":"Michael J. Dubec ,&nbsp;Michael Berks ,&nbsp;James Price ,&nbsp;Lisa McDaid ,&nbsp;John Gaffney ,&nbsp;Ross A. Little ,&nbsp;Susan Cheung ,&nbsp;Marcel van Herk ,&nbsp;Ananya Choudhury ,&nbsp;Julian C. Matthews ,&nbsp;Andrew McPartlin ,&nbsp;Geoff J.M. Parker ,&nbsp;David L. Buckley ,&nbsp;James P.B. O’Connor","doi":"10.1016/j.phro.2024.100689","DOIUrl":"10.1016/j.phro.2024.100689","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Magnetic resonance imaging – linear accelerator (MRI-linac) systems permit imaging of tumours to guide treatment. Dynamic contrast enhanced (DCE)-MRI allows investigation of tumour perfusion. We assessed the feasibility of performing DCE-MRI on a 1.5 T MRI-linac in patients with head and neck cancer (HNC) and measured biomarker repeatability and sensitivity to radiotherapy effects.</div></div><div><h3>Materials and methods</h3><div>Patients were imaged on a 1.5 T MRI-linac or a 1.5 T diagnostic MR system twice before treatment. DCE-MRI parameters including K^trans were calculated, with the optimum pharmacokinetic model identified using corrected Akaike information criterion. Repeatability was assessed by within-subject coefficient of variation (wCV). Treatment effects were assessed as change measured at week 2 of radiotherapy.</div></div><div><h3>Results</h3><div>14 patients were recruited (6 scanned on diagnostic MR and 8 on MRI-linac), with a total of 24 lesions. Baseline K^trans estimates were comparable on both MR systems; 0.13 [95 %CI: 0.10 to 0.16] min⁻¹ (diagnostic MR) and 0.15 [0.12 to 0.18] min⁻¹ (MRI-linac). wCV values were 22.6 % [95 % CI: 16.2 to 37.3 %] (diagnostic MR) and 11.7 % [8.4 to 19.3 %] (MRI-linac). Combined cohort increase in K^trans was significant (p &lt; 0.01). Similar results were seen for other DCE-MRI parameters.</div></div><div><h3>Conclusions</h3><div>DCE-MRI is feasible on a 1.5 T MRI-linac system in patients with HNC. Parameter estimates, repeatability, and sensitivity to treatment were similar to those measured on a conventional diagnostic MR system. These data support performing DCE-MRI in studies on the MRI-linac to assess treatment response and adaptive guidance based on tumour perfusion.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100689"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}