Physics and Imaging in Radiation Oncology最新文献

{"title":"Breakthroughs in modern brachytherapy are transforming cancer care","authors":"Georgina Fröhlich, Alfonso Gomez-Iturriaga, Gemma Eminowicz, Christoph Bert, Åsa Carlsson Tedgren, Alexandra J. Stewart, Bernd Wisgrill, Ludvig P. Muren","doi":"10.1016/j.phro.2025.100783","DOIUrl":"10.1016/j.phro.2025.100783","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100783"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134588","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":"Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning","authors":"Yiling Wang ,&nbsp;Elia Lombardo ,&nbsp;Jie Wang ,&nbsp;Yu Fan ,&nbsp;Yue Zhao ,&nbsp;Stefanie Corradini ,&nbsp;Claus Belka ,&nbsp;Marco Riboldi ,&nbsp;Christopher Kurz ,&nbsp;Guillaume Landry","doi":"10.1016/j.phro.2025.100789","DOIUrl":"10.1016/j.phro.2025.100789","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Deep learning-based tumor tracking is promising for real-time magnetic-resonance-imaging (MRI)-guided radiotherapy. We investigate the applicability of a tumor tracking model developed for 0.35 T MRI-linac sagittal cine-MRI for 1.5 T interleaved orthogonal cine-MRI and implement transfer learning to further improve its performance.</div></div><div><h3>Materials and methods</h3><div>We collected 3600 cine-MRI frames in sagittal, coronal and axial planes from 24 patients (validation 10, testing 14) treated on a 1.5 T MRI-linac, where two expert clinicians manually segmented target labels. A transformer-based deformation model trained on 0.35T MRI-linac images (baseline model, BL) was evaluated and used as a starting point to train patient-specific (PS) models. The Dice similarity coefficient (DSC) and the surface distance (50th and 95th percentiles, SD50%, SD95%) were used to compare the obtained target segmentations with the ground truth labels. The percentage of negative Jacobian determinant values (NegJ), accounting for the folding pixel ratio, was determined.</div></div><div><h3>Results</h3><div>Outperformed by all the PS models, the BL model averaged in a DSC of 0.85, SD50% of 1.9 mm, SD95% of 5.9 mm and NegJ of 0.45 % in testing. The best PS model averaged in a DSC of 0.90, SD50% of 1.3 mm, SD95% of 3.9 mm and NegJ of 0.02 % in testing.</div></div><div><h3>Conclusion</h3><div>We have found the 0.35 T model trained on sagittal cine-MRIs cannot be directly applied to a 1.5 T interleaved orthogonal cine-MRI system. However, PS transfer learning could improve the target tracking performance and reach an accuracy comparable to the inter-observer variability.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100789"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203432","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":"Enhancing patient-specific deep learning based segmentation for abdominal magnetic resonance imaging-guided radiation therapy: A framework conditioned on prior segmentation","authors":"Francesca De Benetti ,&nbsp;Nikolaos Delopoulos ,&nbsp;Claus Belka ,&nbsp;Stefanie Corradini ,&nbsp;Nassir Navab ,&nbsp;Thomas Wendler ,&nbsp;Shadi Albarqouni ,&nbsp;Guillaume Landry ,&nbsp;Christopher Kurz","doi":"10.1016/j.phro.2025.100766","DOIUrl":"10.1016/j.phro.2025.100766","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>Conventionally, the contours annotated during magnetic resonance-guided radiation therapy (MRgRT) planning are manually corrected during the RT fractions, which is a time-consuming task. Deep learning-based segmentation can be helpful, but the available patient-specific approaches require training at least one model per patient, which is computationally expensive. In this work, we introduced a novel framework that integrates fraction MR volumes and planning segmentation maps to generate robust fraction MR segmentations without the need for patient-specific retraining.</div></div><div><h3>Materials and methods:</h3><div>The dataset included 69 patients (222 fraction MRs in total) treated with MRgRT for abdominal cancers with a 0.35 T MR-Linac, and annotations for eight clinically relevant abdominal structures (aorta, bowel, duodenum, left kidney, right kidney, liver, spinal canal and stomach). In the framework, we implemented two alternative models capable of generating patient-specific segmentations using the planning segmentation as prior information. The first one is a 3D UNet with dual-channel input (i.e. fraction MR and planning segmentation map) and the second one is a modified 3D UNet with double encoder for the same two inputs.</div></div><div><h3>Results:</h3><div>On average, the two models with prior anatomical information outperformed the conventional population-based 3D UNet with an increase in Dice similarity coefficient <span><math><mrow><mo>&gt;</mo><mn>4</mn><mspace></mspace><mtext>%</mtext></mrow></math></span>. In particular, the dual-channel input 3D UNet outperformed the one with double encoder, especially when the alignment between the two input channels is satisfactory.</div></div><div><h3>Conclusion:</h3><div>The proposed workflow was able to generate accurate patient-specific segmentations while avoiding training one model per patient and allowing for a seamless integration into clinical practice.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100766"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918321","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":"Reconstructed dose and geometric coverage for tight margins using intrafraction re-planning on an integrated magnetic resonance imaging and linear accelerator system for prostate cancer patients","authors":"Ingeborg van den Berg ,&nbsp;Cornel Zachiu ,&nbsp;Eline N. de Groot-van Breugel ,&nbsp;Thomas Willigenburg ,&nbsp;Gijsbert H. Bol ,&nbsp;Jan J.W. Lagendijk ,&nbsp;Bas W. Raaymakers ,&nbsp;Harm H.E. van Melick ,&nbsp;Cornelis A.T. van den Berg ,&nbsp;Jochem R.N. van der Voort van Zyp ,&nbsp;Johannes C.J. de Boer","doi":"10.1016/j.phro.2025.100776","DOIUrl":"10.1016/j.phro.2025.100776","url":null,"abstract":"<div><h3>Background and purpose</h3><div>A sub-fractionation workflow enables a substantial reduction in planning target volume (PTV) margin in prostate cancer (PCa) patients by reducing systematic motion during magnetic resonance (MR)-guided radiotherapy. This study assessed geometric and reconstructed dose outcomes in patients treated with a tight-margin sub-fractionation workflow on a combined linear accelerator with a 1.5 T MRI scanner (MR-Linac).</div></div><div><h3>Materials and methods</h3><div>We evaluated the sub-fractionation workflow with tight margins (2–3 mm) on 128 PCa patients who completed treatment with 5 × 7.25 Gy (36.25 Gy total dose). A traffic light protocol was applied based on residual motions to detect patients with unexpectedly large motions. When ’red’ traffic light criteria were met, plans with larger margins (5 mm isotropic) were adopted for subsequent fractions. Intra- and inter-fraction dose accumulation was performed via an in-house developed deformable image registration algorithm.</div></div><div><h3>Results</h3><div>A total of 89 % (114/128) of patients completed treatment with the initial tight margins. The mean 3D intrafraction shifts were 1.0 mm (SD: 0.6 mm) in the group with the tight margins and 1.9 mm (SD: 1.5 mm) in the patient group who switched to large margins. The median accumulated D99% was 34.9 Gy (interquartile range: 34.0–35.3 Gy) for patients with prostate shifts who switched to larger margins. In 57 % (8/14) of these patients, the accumulated D99% was above the threshold of 34.4 Gy.</div></div><div><h3>Conclusions</h3><div>Tight margins of 2–3 mm can be safely applied for at least 95 % (122/128) of the PCa patients undergoing a sub-fractionation workflow on a 1.5 T MR-linac.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100776"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070054","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":"Uncertainties in outcome modelling in radiation oncology","authors":"Lukas Dünger ,&nbsp;Emily Mäusel ,&nbsp;Alex Zwanenburg ,&nbsp;Steffen Löck","doi":"10.1016/j.phro.2025.100774","DOIUrl":"10.1016/j.phro.2025.100774","url":null,"abstract":"<div><div>Outcome models predicting e.g. survival, tumour control or radiation-induced toxicities play an important role in the field of radiation oncology. These models aim to support the clinical decision making and pave the way towards personalised treatment. Both validity and reliability of their output are required to facilitate clinical integration. However, models are influenced by uncertainties, arising from data used for model development and model parameters, among others. Therefore, quantifying model uncertainties and addressing their causes promotes the creation of models that are sufficiently reliable for clinical use. This topical review aims to summarise different types and possible sources of uncertainties, presents uncertainty quantification methods applicable to various modelling approaches, and highlights central challenges that need to be addressed in the future.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100774"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070698","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 external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy","authors":"Maria Giulia Ubeira-Gabellini ,&nbsp;Gabriele Palazzo ,&nbsp;Martina Mori ,&nbsp;Alessia Tudda ,&nbsp;Luciano Rivetti ,&nbsp;Elisabetta Cagni ,&nbsp;Roberta Castriconi ,&nbsp;Valeria Landoni ,&nbsp;Eugenia Moretti ,&nbsp;Aldo Mazzilli ,&nbsp;Caterina Oliviero ,&nbsp;Lorenzo Placidi ,&nbsp;Giulia Rambaldi Guidasci ,&nbsp;Cecilia Riani ,&nbsp;Andrei Fodor ,&nbsp;Nadia Gisella Di Muzio ,&nbsp;Robert Jeraj ,&nbsp;Antonella del Vecchio ,&nbsp;Claudio Fiorino","doi":"10.1016/j.phro.2025.100749","DOIUrl":"10.1016/j.phro.2025.100749","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>In order to optimize the radiotherapy treatment and minimize toxicities, organs-at-risk (OARs) and clinical target volume (CTV) must be segmented. Deep Learning (DL) techniques show significant potential for performing this task effectively. The availability of a large single-institute data sample, combined with additional numerous multi-centric data, makes it possible to develop and validate a reliable CTV segmentation model.</div></div><div><h3>Materials and methods:</h3><div>Planning CT data of 1822 patients were available (861 from a single center for training and 961 from 8 centers for validation). A preprocessing step, aimed at standardizing all the images, followed by a 3D-Unet capable of segmenting both right and left CTVs was implemented. The metrics used to evaluate the performance were the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and its 95th percentile variant (HD_95) and the Average Surface Distance (ASD).</div></div><div><h3>Results:</h3><div>The segmentation model achieved high performance on the validation set (DSC: 0.90; HD: 20.5 mm; HD_95: 10.0 mm; ASD: 2.1 mm; epoch 298). Furthermore, the model predicted smoother contours than the clinical ones along the cranial–caudal axis in both directions. When applied to internal and external data the same metrics demonstrated an overall agreement and model transferability for all but one (Inst 9) center.</div></div><div><h3>Conclusion:</h3><div>. A 3D-Unet for CTV segmentation trained on a large single institute cohort consisting of planning CTs and manual segmentations was built and externally validated, reaching high performance.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100749"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767951","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":"Dose assessment in moving targets and organs at risk during carbon ion therapy for pancreatic cancer with respiratory gating","authors":"Christina Stengl ,&nbsp;Jeppe B. Christensen ,&nbsp;Iván D. Muñoz ,&nbsp;Alexander Neuholz ,&nbsp;Stephan Brons ,&nbsp;Eduardo G. Yukihara ,&nbsp;Jakob Liermann ,&nbsp;Oliver Jäkel ,&nbsp;José Vedelago","doi":"10.1016/j.phro.2025.100775","DOIUrl":"10.1016/j.phro.2025.100775","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Carbon ion radiotherapy (CIRT) has demonstrated promising treatment outcomes for pancreatic cancer. However, breathing-induced organ motion can compromise the efficacy of the treatment, leading to under- or over-dosage within the target and organs at risk (OARs). In this work, the dose during CIRT was simultaneously measured at the target and OARs using an anthropomorphic phantom to evaluate the effectiveness of respiratory gating for compensating breathing motion.</div></div><div><h3>Materials and methods</h3><div>The <u>P</u>ancreas <u>P</u>hantom for <u>I</u>on b<u>e</u>am <u>T</u>herapy (PPIeT) was irradiated with carbon ions. The phantom features a pancreas with a virtual tumour and OARs including a duodenum, kidneys, a spine and a spinal cord. Breathing-induced organ motion was imitated with amplitudes of 0 mm (control), 5 mm, 10 mm and 20 mm while irradiating with and without gating. Dose measurements were performed using an ionisation chamber and passive detectors.</div></div><div><h3>Results</h3><div>The prescribed uniform dose of 1.37 Gy in the virtual tumour was experimentally validated for the control. Breathing-induced motion of 20 mm led to a 75 % dose coverage at the target improving to 91 % with gating. For the OARs, the mean dose varied according to the organ, with gating showing no significant differences.</div></div><div><h3>Conclusions</h3><div>Accurate CIRT dosimetry with variable breathing-induced motions can be conducted with PPIeT for a pancreatic tumour and the OARs. Gating mitigated the effects of breathing-induced motion in the tumour.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100775"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941762","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 physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans","authors":"Zihan Sun ,&nbsp;Yongheng Yan ,&nbsp;Yuanhua Chen ,&nbsp;Guorong Yao ,&nbsp;Jiazhou Wang ,&nbsp;Weigang Hu ,&nbsp;Zhongjie Lu ,&nbsp;Senxiang Yan","doi":"10.1016/j.phro.2025.100779","DOIUrl":"10.1016/j.phro.2025.100779","url":null,"abstract":"<div><h3>Background and purpose</h3><div>We aimed to develop a physics-informed deep learning model for beam dose prediction in intensity-modulated radiation therapy (IMRT) for patients with nasopharyngeal cancer.</div></div><div><h3>Materials and methods</h3><div>A total of 100 nine-beam IMRT cases are enrolled in this study retrospectively, divided into training set (72), validation set (8), and test set (20). CT images and contour inputs are preprocessed to generate multiple feature maps for each beam angle, incorporating the dose fall-off principles in water for 6MV photons. Four beam dose prediction models using different loss are built using the U-Net framework to predict each beam dose simultaneously. Beam dose mean absolute error (MAE), beam dose gradient Euclidean distance, total dose MAE, and total dose gradient Euclidean distance are calculated to evaluate model performance.</div></div><div><h3>Results</h3><div>The dose prediction model with beam dose loss, gradient loss, and masked loss achieves total dose MAE of 2.92 Gy, total dose gradient Euclidean distance of 1.35, beam dose MAE of 0.96 Gy, and beam dose gradient Euclidean distance of 0.30.</div></div><div><h3>Conclusions</h3><div>This study proposes a physics-informed deep learning network specifically for the task of beam dose prediction. Additionally, this study addresses the interpretability challenges in deep learning models by employing a crosshair sampling scheme to validate the relationships between input and output channels.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100779"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123244","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":"Effects of intrafractional diaphragm motion on dose perturbation in stereotactic body radiation therapy for lower thoracic vertebrae","authors":"Fumiyasu Matsubayashi,&nbsp;Kosuke Matsuura,&nbsp;Yasushi Ito,&nbsp;Yasuo Yoshioka","doi":"10.1016/j.phro.2025.100780","DOIUrl":"10.1016/j.phro.2025.100780","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>This study aimed to evaluate the impact of intrafractional diaphragm motion (IFDM) on dose accuracy in stereotactic body radiation therapy (SBRT) for lower thoracic vertebrae.</div></div><div><h3>Materials and Methods</h3><div>A retrospective analysis was conducted on 10 patients who underwent SBRT using volumetric-modulated arc therapy (SBRT-VMAT) for the lower thoracic vertebrae. For all patients, dynamic dose calculation (DDC) was performed, incorporating IFDM using arc-divided VMAT plans, respiratory waveforms, and four-dimensional computed tomography (4DCT). The DDC results were compared with doses calculated using time-averaging CT (AveCT) and individual-phase CT scans. Diaphragm motion was quantified using 4DCT, and the correlation between IFDM and dose perturbation was assessed.</div></div><div><h3>Results</h3><div>The minimum gross tumor volume (GTV) dose was overestimated by 1.8 % in phase 0 % and underestimated by − 1.0 % in phase 50 %. A statistically significant correlation was observed between dose variation and the magnitude of IFDM. In the case with the greatest magnitude of diaphragm motion, a 4.3 % variation in GTV was observed compared with the DDC. By contrast, mid-ventilation CT and AveCT showed a mean dose variation of &lt; 0.7 %.</div></div><div><h3>Conclusion</h3><div>This study incorporated IFDM into dose calculation for SBRT-VMAT. Static planning based on CT scans acquired at a specific phase may result in unexpected dose variations. Mid-ventilation CT and AveCT demonstrated utility in mitigating dose variations associated with IFDM. Considering the correlation between dose variation and diaphragm motion magnitude is crucial for developing effective dose perturbation strategies for IFDM.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100780"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070347","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":"Clinical implementation of patient-specific quality assurance for synthetic computed tomography","authors":"Francesca Nella ,&nbsp;Stephanie Tanadini-Lang,&nbsp;Riccardo Dal Bello","doi":"10.1016/j.phro.2025.100764","DOIUrl":"10.1016/j.phro.2025.100764","url":null,"abstract":"<div><h3>Background and purpose</h3><div>In a magnetic resonance (MR) only planning workflow, MR image is the sole dataset acquired. In order to calculate the dose deposition, a synthetic CT (sCT) is generated to substitute the planning computed tomography (CT). This study aimed to establish acceptance criteria for the clinical implementation of patient-specific quality assurance (PSQA) for sCT.</div></div><div><h3>Materials and methods</h3><div>A retrospective study was conducted on 60. 30 patients underwent a CT scan in treatment position and an MR in diagnostic position. 30 patients had both CT and MR images acquired in treatment position. For the latter group, a sCT for dose calculation was generated and compared against three PSQA methods: recalculation on (A) water override of the body, (B) tissue classes with bulk density overrides and (C) planning CT. The relative dose differences (ΔD [%]) between the sCT and the PSQA methos were evaluated.</div></div><div><h3>Results</h3><div>ΔD for PTV Dmean for method (A) were within 3% for pelvis and 4% for brain cohorts, with standard deviations below 1%. Methods (B) and (C) remained within 2% and 1%, respectively, with deviations up to 1%.</div></div><div><h3>Conclusion</h3><div>The present study proposes a robust PSQA method for MR-only planning. Method (A) is a valuable tool for identifying potential large outliers for Dmean deviations (&gt; 5 %) and it is proposed as the routine PSQA. Method (B) can be used for pelvis cases to improve detection to the 2 % level if method (A) fails. If both (A) and (B) fail, method (C) can be used as a fall-back.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100764"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791666","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}