Physics and Imaging in Radiation Oncology最新文献

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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 在综合磁共振成像和线性加速器系统上重建剂量和几何覆盖对前列腺癌患者的狭窄边缘
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100776
Ingeborg van den Berg , Cornel Zachiu , Eline N. de Groot-van Breugel , Thomas Willigenburg , Gijsbert H. Bol , Jan J.W. Lagendijk , Bas W. Raaymakers , Harm H.E. van Melick , Cornelis A.T. van den Berg , Jochem R.N. van der Voort van Zyp , Johannes C.J. de Boer
{"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}
引用次数: 0
Uncertainties in outcome modelling in radiation oncology 放射肿瘤学结果模型的不确定性
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100774
Lukas Dünger , Emily Mäusel , Alex Zwanenburg , Steffen Löck
{"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}
引用次数: 0
Clinical implementation of patient-specific quality assurance for synthetic computed tomography 合成计算机断层扫描患者特异性质量保证的临床实施
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100764
Francesca Nella , Stephanie Tanadini-Lang, Riccardo Dal Bello
{"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}
引用次数: 0
Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy 基于深度学习的全乳房放疗临床靶体积分割模型的开发与外部多中心验证
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100749
Maria Giulia Ubeira-Gabellini , Gabriele Palazzo , Martina Mori , Alessia Tudda , Luciano Rivetti , Elisabetta Cagni , Roberta Castriconi , Valeria Landoni , Eugenia Moretti , Aldo Mazzilli , Caterina Oliviero , Lorenzo Placidi , Giulia Rambaldi Guidasci , Cecilia Riani , Andrei Fodor , Nadia Gisella Di Muzio , Robert Jeraj , Antonella del Vecchio , Claudio Fiorino
{"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}
引用次数: 0
Dose assessment in moving targets and organs at risk during carbon ion therapy for pancreatic cancer with respiratory gating 呼吸门控胰腺癌碳离子治疗过程中运动靶和危险器官的剂量评估
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100775
Christina Stengl , Jeppe B. Christensen , Iván D. Muñoz , Alexander Neuholz , Stephan Brons , Eduardo G. Yukihara , Jakob Liermann , Oliver Jäkel , José Vedelago
{"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}
引用次数: 0
Predicting the hypoxic volume of head and neck tumors from fluorodeoxyglucose positron emission tomography images using artificial intelligence 利用人工智能从氟脱氧葡萄糖正电子发射断层扫描图像预测头颈部肿瘤的缺氧容量
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100769
Wei Zhao , Milan Grkovski , Heiko Schoder , Aditya P. Apte , John Humm , Nancy Y. Lee , Joseph O. Deasy , Harini Veeraraghavan
{"title":"Predicting the hypoxic volume of head and neck tumors from fluorodeoxyglucose positron emission tomography images using artificial intelligence","authors":"Wei Zhao ,&nbsp;Milan Grkovski ,&nbsp;Heiko Schoder ,&nbsp;Aditya P. Apte ,&nbsp;John Humm ,&nbsp;Nancy Y. Lee ,&nbsp;Joseph O. Deasy ,&nbsp;Harini Veeraraghavan","doi":"10.1016/j.phro.2025.100769","DOIUrl":"10.1016/j.phro.2025.100769","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Tumor hypoxia is linked to lower local control rates and increased distant disease progression during head and neck (HN) radiotherapy. <sup>18</sup>F-fluoromisonidazole (<sup>18</sup>F-FMISO) positron emission tomography (PET) imaging measured hypoxia can aid dose selection for HN patients, but its availability is limited. Hence, we tested the hypothesis that an artificial intelligence (AI) model could synthesize <sup>18</sup>F-FMISO-like images from routinely acquired <sup>18</sup>F-fluorodeoxyglucose (<sup>18</sup>F-FDG) PET images in order to predict primary tumor or metastatic lymph node hypoxic volumes.</div></div><div><h3>Materials and methods</h3><div>One hundred and thirty-four (training = 84, validation = 13, testing = 21, additional testing = 16) HN carcinoma patients, treated with chemoradiotherapy between 2011 and 2018 and scanned at treatment baseline with <sup>18</sup>F-FDG PET/computed tomography (CT) and <sup>18</sup>F-FMISO dynamic PET/CT, were analyzed. A pix2pix-architecture-based generative adversarial network was trained to yield 2D voxel-wise FMISO hypoxia images of target-to-blood ratios (TBRs) directly from the <sup>18</sup>F-FDG PET/CT image slices. The hypoxic volume was defined consistent with clinical procedure as the malignant volume with TBR values above 1.2. The AI model hypoxia predictions were compared against scaled <sup>18</sup>F-FDG PET values.</div></div><div><h3>Results</h3><div>The AI model hypoxic volume predictions were well-correlated with <sup>18</sup>F-FMISO hypoxic volumes on the held-out test subjects (Pearson correlation testing R = 0.96, additional testing R = 0.91, p &lt; 0.001). Predictions from globally scaled <sup>18</sup>F-FDG PET images also produced a significantly correlated but worse prediction.</div></div><div><h3>Conclusion</h3><div>Voxel-wise prediction of hypoxia for HN cancers from a 2D deep learning model using FDG-PET images as inputs was shown to be feasible. Testing on larger institutional and multi-institutional cohorts is required to establish generalizability.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100769"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928940","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}
引用次数: 0
Effects of intrafractional diaphragm motion on dose perturbation in stereotactic body radiation therapy for lower thoracic vertebrae 下胸椎立体定向放射治疗中膈肌运动对剂量摄动的影响
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100780
Fumiyasu Matsubayashi, Kosuke Matsuura, Yasushi Ito, Yasuo Yoshioka
{"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}
引用次数: 0
A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans 用于预测调强放疗治疗计划的光束剂量分布的物理信息深度学习模型
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100779
Zihan Sun , Yongheng Yan , Yuanhua Chen , Guorong Yao , Jiazhou Wang , Weigang Hu , Zhongjie Lu , Senxiang Yan
{"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}
引用次数: 0
Role of functional mapping on Gallium-68 perfusion positron emission tomography and computed tomographic imaging (PET/CT) to assess the risk of long-term radiation-induced lung toxicity after stereotactic body radiation therapy 镓-68灌注正电子发射断层扫描和计算机断层扫描(PET/CT)的功能定位在评估立体定向全身放射治疗后长期辐射致肺毒性风险中的作用
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-04-01 DOI: 10.1016/j.phro.2025.100786
François Lucia , David Bourhis , Frédérique Blanc-Béguin , Gaëlle Goasduff , Mohamed Hamya , Simon Hennebicq , Maëlle Mauguen , Romain Floch , Margaux Geier , Ulrike Schick , Maëlys Consigny , Olivier Pradier , Grégoire Le Gal , Pierre-Yves Salaun , Vincent Bourbonne , Pierre-Yves Le Roux
{"title":"Role of functional mapping on Gallium-68 perfusion positron emission tomography and computed tomographic imaging (PET/CT) to assess the risk of long-term radiation-induced lung toxicity after stereotactic body radiation therapy","authors":"François Lucia ,&nbsp;David Bourhis ,&nbsp;Frédérique Blanc-Béguin ,&nbsp;Gaëlle Goasduff ,&nbsp;Mohamed Hamya ,&nbsp;Simon Hennebicq ,&nbsp;Maëlle Mauguen ,&nbsp;Romain Floch ,&nbsp;Margaux Geier ,&nbsp;Ulrike Schick ,&nbsp;Maëlys Consigny ,&nbsp;Olivier Pradier ,&nbsp;Grégoire Le Gal ,&nbsp;Pierre-Yves Salaun ,&nbsp;Vincent Bourbonne ,&nbsp;Pierre-Yves Le Roux","doi":"10.1016/j.phro.2025.100786","DOIUrl":"10.1016/j.phro.2025.100786","url":null,"abstract":"<div><h3>Background and purpose</h3><div>To compare the performance of anatomic and functional dosimetric parameters based on Gallium-68 lung perfusion positron emission tomography and computed tomographic imaging (PET/CT) imaging to predict the risk of symptomatic long-term radiation-induced lung toxicity (RILT) in patients with lung tumors treated with stereotactic body radiation therapy (SBRT).</div></div><div><h3>Materials and methods</h3><div>We have performed a prospective study in patients treated with SBRT. Mean dose (MD) and volumes receiving xGy were calculated in five lung volumes: the conventional anatomical volume (AV) delineated on CT images, three lung functional volumes defined on lung perfusion PET imaging (FV50%, FV70%, FV90%, i.e. the minimal volume containing 50 %, 70 % and 90 % of the total activity within the AV), and a low functional volume (LFV = AV-FV90%). The primary endpoint of this analysis was grade ≥2 long-term RILT at 12 months as assessed with NCI CTCAE v.5. The predictive value of anatomical and functional dose volume parameters was evaluated by comparing patients with and without long-term RILT.</div></div><div><h3>Results</h3><div>Out of the 59 patients included, 50 were still alive at 12 months and 9 (18 %) had grade ≥2 long-term RILT. The MD and the VxGy in the AV and LFV were not statistically different in patients with and without long-term RILT (p &gt; 0.05). All functional parameters in FV50% and FV70% were significantly higher in long-term RILT patients (p &lt; 0.05).</div></div><div><h3>Discussion</h3><div>The predictive value of PET perfusion-based functional parameters outperforms the standard CT-based dose-volume parameters for the risk of grade ≥2 long-term RILT.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100786"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106108","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}
引用次数: 0
Ultra-low field magnetic resonance breast imaging in prone and seated positions for radiation therapy 俯卧位和坐位的超低场磁共振乳房成像用于放射治疗
IF 3.4
Physics and Imaging in Radiation Oncology Pub Date : 2025-03-26 DOI: 10.1016/j.phro.2025.100758
Friderike K. Longarino , Sheng Shen , Neha Koonjoo , Torben P.P. Hornung , Rachel B. Jimenez , Elie K. Mehanna , John T. Burge , Zoelle Wilson , Kathryn E. Keenan , Thomas R. Bortfeld , Matthew S. Rosen , Susu Yan
{"title":"Ultra-low field magnetic resonance breast imaging in prone and seated positions for radiation therapy","authors":"Friderike K. Longarino ,&nbsp;Sheng Shen ,&nbsp;Neha Koonjoo ,&nbsp;Torben P.P. Hornung ,&nbsp;Rachel B. Jimenez ,&nbsp;Elie K. Mehanna ,&nbsp;John T. Burge ,&nbsp;Zoelle Wilson ,&nbsp;Kathryn E. Keenan ,&nbsp;Thomas R. Bortfeld ,&nbsp;Matthew S. Rosen ,&nbsp;Susu Yan","doi":"10.1016/j.phro.2025.100758","DOIUrl":"10.1016/j.phro.2025.100758","url":null,"abstract":"<div><h3>Background &amp; purpose</h3><div>The aim of this first-in-human study was to investigate the potential of ultra-low field (ULF) magnetic resonance imaging (MRI) at 6.5<!--> <!-->mT for breast imaging in healthy female participants in prone and seated positions for radiation therapy, especially compact proton therapy systems.</div></div><div><h3>Materials &amp; methods</h3><div>An experimental setup for breast imaging in prone and seated positions utilizing an ULF MRI scanner and a conical RF coil was developed. ULF MR images of the left breast of ten healthy women were acquired in prone and seated positions using a 3D balanced steady-state free precession sequence without the use of contrast agents. The visibility of the breast outline, chest wall, and cardiac silhouette in prone and seated position ULF breast MR images was evaluated by two radiation oncologists (ROs) and two radiation therapists (RTTs), respectively.</div></div><div><h3>Results</h3><div>ULF breast MRI obtained at 6.5<!--> <!-->mT can show breast outline, chest wall, and cardiac silhouette in prone and seated positions. ULF prone/seated images were found to be acceptable by the ROs (RTTs) for treatment planning (setup) purposes in 100%/95% (95%/85%) of cases for breast outline visibility, in 70%/50% (75%/70%) of cases for chest wall visibility, and in 65%/65% (0%/10%) of cases for cardiac silhouette visibility.</div></div><div><h3>Conclusions</h3><div>This proof-of-concept study demonstrated that breast imaging is feasible in prone and seated positions utilizing ULF MRI and partially suitable for treatment planning and setup in proton therapy. Yet an increased spatio-temporal resolution is required for applications to MRI-guided proton therapy. ULF MRI may enable position monitoring and adaptive treatment procedures in radiation therapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100758"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725298","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}
引用次数: 0
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