Physics and Imaging in Radiation Oncology最新文献_第2页

Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients 肿瘤条件下的患者间登记使用计划计算机断层扫描进行基于体素的分析以预测肺癌患者的放射性肺炎

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01 DOI: 10.1016/j.phro.2026.100907

Chloe Min Seo Choi , Jue Jiang , Nikhil P. Mankuzhy , Nishant Nadkarni , Sudharsan Madhavan , Abraham J. Wu , Joseph O. Deasy , Maria Thor , Andreas Rimner , Harini Veeraraghavan

Background and purpose

Deformable image registration (DIR) for voxel-based analysis (VBA) can be challenging in patients with non-small cell lung cancer (NSCLC) due to large variations in tumor size and location. This study aimed to assess whether a tumor-preserving inter-patient DIR approach improves VBA-based prediction of radiation pneumonitis (RP).

Methods and materials

Three DIR methods were evaluated: deep learning-based Tumor-Aware Recurrent Registration (TRACER) and Patient-Specific Context and Shape (PACS), trained on a public dataset of 268 locally-advanced (LA) NSCLC patients, and iterative Symmetric Normalization (SyN). All methods were tested on 240 patients with LA-NSCLC. Geometric, dosimetric, and tumor preservation metrics were compared using the Wilcoxon signed-rank test. VBA was conducted with each DIR method to identify cohort-relevant regions (CRRs). Machine learning models incorporating clinical, dosimetric, and CRR dose features were used to predict grade 2 or higher RP.

Results

TRACER best preserved tumor volume (1.39 %) and organ doses (mean 0.08 Gy) compared with PACS and SyN (p < 0.001). PACS showed higher geometric but worse dose preservation accuracy than TRACER. All DIR-based VBA methods identified the right lung as the CRR associated with RP. TRACER-derived CRR had slightly higher RP predictive performance (AUC 0.78 vs PACS 0.73 vs SyN 0.71), and outperformed the MLD-based ML model (AUC = 0.78 vs 0.69, p = 0.04; specificity = 0.62 vs 0.48).

Conclusions

TRACER improved registration accuracy, with better tumor volume preservation and reduced OAR dose impact. Incorporating VBA-derived dose enhanced RP prediction accuracy compared with using MLD. CRRs identified through VBA were robust to the choice of DIR.

背景和目的由于肿瘤大小和位置的巨大差异，非小细胞肺癌（NSCLC）患者的基于体素分析（VBA）的可变形图像配准（DIR）可能具有挑战性。本研究旨在评估保留肿瘤的患者间DIR方法是否能改善基于vba的放射性肺炎（RP）预测。方法和材料评估了三种DIR方法：基于深度学习的肿瘤感知复发登记（TRACER）和患者特异性上下文和形状（PACS），在268例局部晚期（LA） NSCLC患者的公共数据集上训练，以及迭代对称归一化（SyN）。所有方法在240例LA-NSCLC患者中进行了测试。使用Wilcoxon符号秩检验比较几何、剂量学和肿瘤保存指标。采用每种DIR方法进行VBA以确定队列相关区域（CRRs）。结合临床、剂量学和CRR剂量特征的机器学习模型用于预测2级或更高级别的RP。结果与PACS和SyN相比，stracer能更好地保存肿瘤体积（1.39%）和器官剂量（平均0.08 Gy）（p < 0.001）。PACS的几何保存精度高于TRACER，但剂量保存精度较差。所有基于dir的VBA方法均将右肺确定为与RP相关的CRR。tracer衍生的CRR具有稍高的RP预测性能（AUC 0.78 vs PACS 0.73 vs SyN 0.71），并且优于基于mld的ML模型（AUC = 0.78 vs 0.69, p = 0.04；特异性= 0.62 vs 0.48）。结论stracer可提高配准精度，更好地保留肿瘤体积，降低OAR剂量影响。与使用MLD相比，结合vba衍生剂量可提高RP预测的准确性。通过VBA识别的crr对DIR的选择具有鲁棒性。

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引用次数: 0

Quality assurance of online adaptive radiotherapy workflows using film dosimetry in a 3D printed thorax anthropomorphic phantom 在3D打印胸腔拟人模型中使用胶片剂量法在线自适应放疗工作流程的质量保证

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01 DOI: 10.1016/j.phro.2026.100909

Daan Hoffmans , Koen Nelissen , Eva Versteijne , Wilko Verbakel

Background and purpose

Quality Assurance for online adaptive radiotherapy (oART) can be challenging. Several tests can demonstrate the dosimetric and position accuracy, but commercial phantoms are often not anatomically representative. The aim of this study was to investigate the accuracy of cone-beam computed tomography guided oART palliative and breast cancer trials by using a 3D printed thorax anthropomorphic phantom.

Materials and methods

An anthropomorphic phantom was 3D printed for this study which accommodates film through the spine, breast, heart, and lungs. Dose was measured for spine and breast treatment plans, whilst variations were simulated which can occur during treatment. Measurements were compared to calculated dose on the planning (pCT) and synthetic computed tomography (sCT) using gamma pass rate criteria of minimal 95 % (for gamma of 4 %/2 mm). Differences between the mean gamma were tested for significance.

Results

Measurements done with positional and target volume changes showed no significant difference between the gamma analyses for the pCT and sCT (p = 0.15), indicating a robust and safe workflow. For extreme variations, difference was found between gamma analyses for the pCT and sCT (p = 0.051). Pass rates were all >95 %, except for three measurements in which the sCT showed density errors up to 1000 Hounsfield Units.

Conclusions

This QA approach for oART, which used film measurements in a custom 3D-printed anthropomorphic phantom was able to validate the accuracy of the oART workflow when anatomical deviations arise and could be suitable as end-to-end test in the future.

背景和目的在线适应性放疗（oART）的质量保证具有挑战性。几个测试可以证明剂量学和位置的准确性，但商业模型往往不具有解剖学代表性。本研究的目的是通过使用3D打印的胸腔拟人化幻影来研究锥束计算机断层扫描引导的oART姑息治疗和乳腺癌试验的准确性。材料和方法本研究使用3D打印的拟人化假体，该假体可通过脊柱、乳房、心脏和肺部容纳薄膜。测量了脊柱和乳房治疗方案的剂量，同时模拟了治疗过程中可能发生的变化。测量结果与计划（pCT）和合成计算机断层扫描（sCT）上的计算剂量进行比较，使用至少95%的伽马通过率标准（伽马为4% / 2mm）。对平均值之间的差异进行显著性检验。结果位置和靶体积变化的测量结果显示pCT和sCT的伽马分析之间没有显著差异（p = 0.15），表明工作流程稳健且安全。对于极端的变异，pCT和sCT的伽马分析之间存在差异（p = 0.051）。通过率均为95%，除了三个测量中sCT显示密度误差高达1000霍斯菲尔德单位。oART的这种QA方法，在定制的3d打印拟人化幻影中使用薄膜测量，能够在解剖偏差出现时验证oART工作流程的准确性，并且可以适用于未来的端到端测试。

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引用次数: 0

Deep learning and dual-radiomics model incorporating brachytherapy applicator type to predict radiation-induced acute rectal injury in cervical cancer patients 深度学习和结合近距离治疗应用器类型的双放射组学模型预测宫颈癌患者放射性急性直肠损伤

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01 DOI: 10.1016/j.phro.2026.100908

Boda Ning , Zhengxian Li , Deyang Yu , Chenyu Li , Qi Liu , Yanling Bai

Background and purpose

Radiation-induced acute rectal injury (RARI) is a common early toxicity after radiotherapy for cervical cancer (CC) and remains difficult to predict before treatment, which can adversely affect life quality of patients. We aimed to develop a combined dual-radiomics and deep learning (DL) model to improve the prediction of RARI in CC patients treated with radiotherapy.

Materials and methods

This retrospective study included 200 CC patients from one hospital, randomly divided into training (n = 160), internal validation (n = 40) cohorts and external validation (n = 40) from another hospital. Patients were classified as RARI (CTCAE v5.0 grade ≥ 2) or Non-RARI (grade < 2). Radiomic and dosiomic features were extracted from CT images and dose distributions, and DL features were learned using 3D CNNs. The performance of radiomics, dosiomics, DL and hybrid features models for RARI prediction was compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC).

Results

For radiomics combining dosiomics, XGBoost achieved the best performance with AUCs of 0.786 and 0.755 in internal and external validation cohorts, respectively. For DL, Resnet_with_CBAM achieved the best performance in the input of combining CT and dose distribution with AUCs of 0.786 and 0.773 in internal and external validation cohorts, respectively. Nomogram integrating radiomics, dosiomics, DL features, and clinical factor improved the AUC to 0.810, 0.803 in internal and external validation cohorts, respectively.

Conclusion

The nomogram integrating radiomics, dosiomics, DL, and clinical factors can improve the predictive performance for RARI in CC patients followed by radiotherapy.

背景与目的放射引起的急性直肠损伤（RARI）是宫颈癌（CC）放疗后常见的早期毒性，治疗前难以预测，影响患者的生活质量。我们的目标是建立一个联合的双放射组学和深度学习（DL）模型，以提高对放射治疗的CC患者RARI的预测。材料与方法本回顾性研究纳入一家医院的200例CC患者，随机分为训练组（n = 160）、内部验证组（n = 40）和另一家医院的外部验证组（n = 40）。患者分为RARI （CTCAE v5.0分级≥2级）和非RARI（分级<； 2）。从CT图像和剂量分布中提取放射组学和剂量组学特征，并使用3D cnn学习DL特征。采用受试者工作特征（ROC）曲线和曲线下面积（AUC）测量，比较放射组学、剂量组学、DL和混合特征模型预测RARI的性能。结果对于放射组学联合剂量组学，XGBoost在内部和外部验证队列中的auc分别为0.786和0.755，表现最佳。对于DL， Resnet_with_CBAM在CT与剂量分布相结合的输入中表现最佳，在内部验证队列和外部验证队列中的auc分别为0.786和0.773。整合放射组学、剂量组学、DL特征和临床因素的Nomogram将内部验证队列和外部验证队列的AUC分别提高至0.810和0.803。结论综合放射组学、剂量组学、DL和临床因素的nomogram放射组学可提高对CC患者放疗后RARI的预测能力。

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引用次数: 0

Accuracy of reirradiation dose constraints for the mandible and carotids 下颌骨和颈动脉再照射剂量限制的准确性

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-21 DOI: 10.1016/j.phro.2025.100897

Sara Bornedal , Jeehong Lee , Tim Melhus , Anna Embring , Eva Onjukka

When reirradiation dose constraints are derived using accumulated dose, the underlying image registrations contribute to the uncertainty. We performed a structure-based evaluation of deformable image registrations, to estimate the uncertainty in previously published dose constraints related to carotid blowout and osteoradionecrosis after head and neck reirradiation. With the workflow of the current analysis, the uncertainty was small in the majority of the cases (<4 Gy in accumulated equivalent dose in 2-Gy fractions), but with substantial outliers resulting from anatomical alterations. Our previously suggested dose constraints appear to be reliable with regard to the underlying image registrations.

当使用累积剂量推导再照射剂量约束时，底层图像配准会增加不确定性。我们对变形图像配准进行了基于结构的评估，以估计先前公布的头颈部再照射后颈动脉爆裂和骨放射性坏死相关剂量限制的不确定性。根据目前的分析工作流程，大多数病例的不确定性很小（2-Gy分数中累积等效剂量为4 Gy），但由于解剖改变，存在大量异常值。我们先前建议的剂量限制对于潜在的图像配准似乎是可靠的。

引用次数: 0

Dosimetric effect of abdominal compression in online adaptive planning for abdominal cancers treated with a 1.5 Tesla magnetic resonance-guided linear accelerator 1.5特斯拉磁共振引导直线加速器治疗腹部肿瘤在线自适应计划中腹部压缩的剂量学效应

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-21 DOI: 10.1016/j.phro.2025.100899

Moghadaseh Khaleghibizaki , Angela Sobremonte , Luis Perles , Surendra Prajapati , Ergys Subashi , Yao Ding , Kristy Brock , Roya Barati , Eugene Koay , Chad Tang , Jinzhong Yang

Compression belts (CBs) are sometimes used to reduce respiratory motion during stereotactic body radiotherapy of abdominal cancers with magnetic resonance (MR)-guided online adaptive planning. This study evaluated the dosimetric effects of overriding the relative electron density (ED) value of CBs in creating synthetic computed tomography (CT) scans for MR-guided adaptive planning. We evaluated plans for 12 patients with abdominal cancer and identified that ED values between 0.2 and 0.3 achieved the best approximation of CB ED in dose calculation. Our study presented an approach to estimate appropriate ED overrides for CBs in MR-guided online adaptive planning.

压缩带（CBs）有时被用来减少呼吸运动在立体定向放射治疗腹部癌症与磁共振（MR）引导在线自适应规划。本研究评估了覆盖相对电子密度（ED）值的CBs在为磁共振引导的适应性规划创建合成计算机断层扫描（CT）时的剂量学效应。我们对12例腹部肿瘤患者的治疗方案进行了评估，发现在剂量计算中，ED值在0.2和0.3之间最接近CB ED。我们的研究提出了一种方法来估计在mr引导的在线适应性规划中CBs的适当ED覆盖。

引用次数: 0

Contour-informed inter-patient deformable registration for more reliable voxel-based analysis of Head-and-Neck cancer patients 轮廓信息的患者间可变形注册，用于更可靠的头颈癌患者体素分析

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-20 DOI: 10.1016/j.phro.2025.100898

Xingyue Ruan , Xia Li , Muheng Li , Barbara Bachtiary , Antony Lomax , Zhiling Chen , Ye Zhang

Background and purpose

The registration of individual dose distributions to a reference anatomy represents a key step in voxel-based analysis (VBA), a tool for spatially informed dose–response assessment. Accurate deformable image registration (DIR) is essential for addressing anatomical variability across patients. To improve both global and region-specific alignment, we enhanced our in-house DIR algorithm (CPT-DIR) by incorporating contour-informed regularization.

Materials and methods

We evaluated contour-informed CPT-DIR using CT images from 37 Head-and-Neck patients, with seven cases providing ground-truth dose distribution for dose warping validation. Organs at risk (OARs) were delineated manually, with bone contours auto-generated using TotalSegmentator. Contour-informed constraints (Dice Similarity) were integrated to enhance registration in clinically relevant regions. The global registration results were evaluated using MAE, SSIM and PSNR. Geometric accuracy and warped dose accuracy were assessed using Dice Similarity Coefficient (DSC) and Dose-Organ Overlap (DOO). The performance of CPT-DIR, with and without constraints, was benchmarked against conventional B-spline.

Results

CPT-DIR achieved superior accuracy with a MAE of 98.9 ± 6.3 HU, lower than 179.1 ± 17.8 HU for B-spline. Incorporating brainstem contours as regularization improved the DSC from 0.604 ± 0.116 to 0.878 ± 0.017 and DOO from 0.430 ± 0.117 to 0.753 ± 0.043 for brainstem. For the remaining OARs, the enhanced CPT-DIR consistently achieved higher DSC and DOO metrics.

Conclusions

The integration of contour-informed regularization in CPT-DIR improved DIR accuracy, particularly in anatomically and dosimetrically relevant regions. This enhanced spatial alignment demonstrated strong potential for advancing reliable inter-patient dosimetric studies in HN radiotherapy.

背景和目的将个体剂量分布注册到参考解剖结构中是基于体素的分析（VBA）的关键步骤，这是一种空间知情剂量反应评估工具。准确的可变形图像配准（DIR）对于解决患者解剖差异至关重要。为了改善全局和特定区域的对齐，我们通过结合轮廓通知正则化来增强我们的内部DIR算法（CPT-DIR）。材料和方法我们使用来自37例头颈部患者的CT图像评估轮廓知情的CPT-DIR，其中7例提供了用于剂量扭曲验证的真实剂量分布。危险器官（OARs）是手动划定，骨轮廓自动生成使用TotalSegmentator。整合轮廓信息约束（骰子相似度）以增强临床相关区域的注册。采用MAE、SSIM和PSNR对全局配准结果进行评价。使用骰子相似系数（DSC）和剂量-器官重叠（DOO）评估几何精度和翘曲剂量精度。CPT-DIR的性能，有和没有约束，是针对传统的b样条基准。结果scpt - dir的准确率为98.9±6.3 HU，低于b样条的179.1±17.8 HU。将脑干轮廓进行正则化后，脑干DSC从0.604±0.116提高到0.878±0.017，DOO从0.430±0.117提高到0.753±0.043。对于剩余的桨，增强型CPT-DIR始终获得更高的DSC和DOO指标。结论在CPT-DIR中整合轮廓信息正则化提高了DIR的准确性，特别是在解剖学和剂量学相关区域。这种增强的空间对齐显示了在HN放疗中推进可靠的患者间剂量学研究的强大潜力。

{"title":"Contour-informed inter-patient deformable registration for more reliable voxel-based analysis of Head-and-Neck cancer patients","authors":"Xingyue Ruan , Xia Li , Muheng Li , Barbara Bachtiary , Antony Lomax , Zhiling Chen , Ye Zhang","doi":"10.1016/j.phro.2025.100898","DOIUrl":"10.1016/j.phro.2025.100898","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The registration of individual dose distributions to a reference anatomy represents a key step in voxel-based analysis (VBA), a tool for spatially informed dose–response assessment. Accurate deformable image registration (DIR) is essential for addressing anatomical variability across patients. To improve both global and region-specific alignment, we enhanced our in-house DIR algorithm (CPT-DIR) by incorporating contour-informed regularization.</div></div><div><h3>Materials and methods</h3><div>We evaluated contour-informed CPT-DIR using CT images from 37 Head-and-Neck patients, with seven cases providing ground-truth dose distribution for dose warping validation. Organs at risk (OARs) were delineated manually, with bone contours auto-generated using TotalSegmentator. Contour-informed constraints (Dice Similarity) were integrated to enhance registration in clinically relevant regions. The global registration results were evaluated using MAE, SSIM and PSNR. Geometric accuracy and warped dose accuracy were assessed using Dice Similarity Coefficient (DSC) and Dose-Organ Overlap (DOO). The performance of CPT-DIR, with and without constraints, was benchmarked against conventional B-spline.</div></div><div><h3>Results</h3><div>CPT-DIR achieved superior accuracy with a MAE of 98.9 ± 6.3 HU, lower than 179.1 ± 17.8 HU for B-spline. Incorporating brainstem contours as regularization improved the DSC from 0.604 ± 0.116 to 0.878 ± 0.017 and DOO from 0.430 ± 0.117 to 0.753 ± 0.043 for brainstem. For the remaining OARs, the enhanced CPT-DIR consistently achieved higher DSC and DOO metrics.</div></div><div><h3>Conclusions</h3><div>The integration of contour-informed regularization in CPT-DIR improved DIR accuracy, particularly in anatomically and dosimetrically relevant regions. This enhanced spatial alignment demonstrated strong potential for advancing reliable inter-patient dosimetric studies in HN radiotherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100898"},"PeriodicalIF":3.3,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842008","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

Implementing a fully computed tomography-free online adaptive palliative radiotherapy: a one-visit workflow 实施完全免计算机断层扫描的在线自适应姑息放疗：一次访问工作流程

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-20 DOI: 10.1016/j.phro.2025.100896

Ashaya T. Jaglal , Koen J. Nelissen , Angelique R.W. van Vlaenderen , Amy L. de la Fuente , Famke L. Schneiders , Peter S.N. van Rossum , Jan Wiersma , Wilko F.A.R. Verbakel , Suresh Senan , Jorrit Visser , Eva Versteijne

Background and purpose

Same-day palliative radiotherapy requires rapid workflows, but conventional computed tomography (CT)-based workflows cause delays and strain resources. Advances in cone-beam CT (CBCT) enabled accurate dose calculation and planning without a planning CT. This study evaluated the feasibility and efficiency of a fully CT-free online adaptive workflow for same-day palliative radiotherapy using high-quality CBCT.

Methods and materials

This prospective study enrolled sixteen patients between January–May 2025, of whom fifteen completed same-day treatment. Eligible patients were referred for single-fraction palliative radiotherapy (8 Gy) to non-mobile target volumes. No planning CT was acquired; instead, a reference plan was generated on a phantom with standardized beam setups and planning objectives. On the treatment day, planning and delivery were performed on the Varian Ethos 2.0 platform using HyperSight CBCT, providing more accurate Hounsfield Unit imaging for automated organs at risk segmentation and target definition. Plans were adapted online and delivered while patients were on the couch. Workflow times, plan quality, and patient characteristics were studied.

Results

All fifteen treatments were delivered successfully. All plans met clinical objectives, with planning target volume coverage exceeding required thresholds. The CT-free workflow reduced median departmental time to 73 min, including 28 min in the treatment room, compared with 335 min in a conventional CT-based workflow. In one urgent case, referral-to-treatment time was 2.5 h. Repeated CBCTs were required in 7 patients.

Conclusions

A fully CT-free workflow for palliative radiotherapy is feasible and efficient, enabling same-day treatment, reduces departmental workload, and is well-suited for urgent cases requiring rapid intervention.

当日姑息性放疗需要快速的工作流程，但传统的基于计算机断层扫描（CT）的工作流程会导致延迟和资源紧张。锥形束CT （CBCT）的进步使精确的剂量计算和计划无需计划CT。本研究评估了使用高质量CBCT进行当日姑息性放疗的完全免ct在线自适应工作流程的可行性和效率。方法和材料这项前瞻性研究在2025年1月至5月期间招募了16例患者，其中15例完成了当天的治疗。符合条件的患者接受单次姑息放疗（8 Gy）至非移动靶体积。未获得计划CT；取而代之的是，参考平面图是在具有标准化光束设置和规划目标的模型上生成的。在治疗当天，在Varian Ethos 2.0平台上使用HyperSight CBCT进行计划和交付，为自动化危险器官分割和目标定义提供更准确的Hounsfield Unit成像。病人躺在沙发上的时候，计划在网上进行调整并交付。研究了工作流程时间、计划质量和患者特征。结果15例治疗均成功。所有计划均达到临床目标，计划目标体积覆盖范围超过要求的阈值。与传统的基于ct的工作流程的335分钟相比，无ct的工作流程将平均科室时间缩短至73分钟，其中治疗室时间为28分钟。在1例紧急病例中，转诊治疗时间为2.5小时。7例患者需要重复cbct。结论完全无ct的姑息性放疗工作流程可行且高效，可实现当日治疗，减少科室工作量，适合需要快速干预的紧急病例。

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引用次数: 0

Patient-specific factors associated with tumour motion in lung stereotactic body radiation therapy from real-time tumour tracking traces 从实时肿瘤跟踪痕迹分析肺立体定向全身放射治疗中与肿瘤运动相关的患者特异性因素

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-16 DOI: 10.1016/j.phro.2025.100895

Ashlesha Gill , Nicholas Bucknell , Mahsheed Sabet , Milad Mirzaei , Thomas Milan , Adriano Polpo , Pejman Rowshanfarzad

Background and purpose

Respiratory motion is a major source of geometric uncertainty in lung stereotactic body radiation therapy (SBRT), necessitating individualized motion management. The study aimed to examine the lung tumour motion against patient- and tumour-specific factors.

Materials and methods

Motion traces were obtained from 109 retrospective CyberKnife fiducial tracking lung treatments, recorded at 25 Hz through correlation of fiducial and external marker signals. Log files were collected and motion was quantified in the superior-inferior (SI), left–right (LR) and anterior-posterior (AP) directions. Each treatment delivery node (∼2700 per patient) was individually analysed. Clinical data included demographics, comorbidities, prior lung treatments, pulmonary function, and tumour location, size, and histology. Statistical analyses used multivariate and univariate approaches.

Results

Tumour location strongly predicted SI motion, with lower lobes showing up to an 8.2 mm greater motion than upper lobes. Previous surgery or radiotherapy moderately reduced LR motion (–0.5 mm), while tumour diameter showed a weak positive association with LR motion (+0.02 mm per mm). Percentage predicted forced expiratory volume in one second (FEV₁) showed moderate positive correlations with AP (+0.01 mm per %) and SI (+0.04 mm per %) motion. Body-mass index (BMI) weakly increased SI motion (+0.2 mm per kg/m²).

Conclusions

Tumour location primarily determined SI motion, with additional increases linked to better pulmonary function and higher BMI. LR motion was greater in patients without prior lung treatment and with larger tumours, while greater AP motion occurred with better pulmonary function. Lung motion variation was quantified to support sub-millimetre SBRT precision.

背景和目的在肺立体定向放射治疗（SBRT）中，呼吸运动是几何不确定性的主要来源，需要个性化的运动管理。该研究旨在检查肺部肿瘤运动对患者和肿瘤特异性因素的影响。材料和方法对109例回顾性射波刀基准跟踪肺部治疗，通过基准和外部标记信号的相关性在25 Hz下记录运动轨迹。收集日志文件，量化上下（SI）、左右（LR）和前后（AP）方向的运动。每个治疗递送节点（每位患者约2700个）被单独分析。临床资料包括人口统计学、合并症、既往肺治疗、肺功能、肿瘤位置、大小和组织学。统计分析采用多变量和单变量方法。结果肿瘤位置对SI运动有较强的预测作用，下叶比上叶运动大8.2 mm。先前的手术或放疗中度降低了LR运动（-0.5 mm），而肿瘤直径与LR运动呈弱正相关（+0.02 mm / mm）。预测一秒钟用力呼气量百分比（FEV1）与AP （+0.01 mm / %）和SI （+0.04 mm / %）运动呈中度正相关。身体质量指数（BMI）微弱增加SI运动（+0.2 mm / kg/m2）。结论：我们的位置主要决定了SI运动，额外的增加与更好的肺功能和更高的BMI有关。未接受过肺部治疗和肿瘤较大的患者LR运动更大，而肺功能较好的患者AP运动更大。肺运动变化被量化，以支持亚毫米SBRT精度。

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引用次数: 0

Development and validation of a novel patient-specific 3D-printed head and neck immobilization device for radiotherapy 一种用于放疗的新型患者特异性3d打印头颈部固定装置的开发和验证

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-16 DOI: 10.1016/j.phro.2025.100891

Bertrand Dewit , Ronald Peeters , Sandra Nuyts , Tom Depuydt

Accurate and reproducible immobilization is critical for effective radiotherapy in the head and neck region, but conventional thermoplastic masks are uncomfortable, labor-intensive and time-consuming to fabricate. A novel patient-specific 3D-printed immobilization device was developed using a digital CT-based workflow. The design targeted stable facial regions and was mechanically validated via simulations. Two nylon-based 3D-printed prototypes were preclinically evaluated on an anthropomorphic phantom, showing high dimensional accuracy, CT/MR compatibility, minimal target dose deviation (<2%), and submillimetric positional reproducibility. While the results demonstrate technical feasibility, clinical validations will be the next step to assess comfort, reproducibility, and workflow integration in radiotherapy.

准确和可重复的固定对于头颈部区域的有效放射治疗至关重要，但传统的热塑性口罩不舒服，劳动密集且耗时制造。使用基于数字ct的工作流程开发了一种新型的患者特异性3d打印固定装置。该设计针对稳定的面部区域，并通过仿真进行了机械验证。两个基于尼龙的3d打印原型在拟人化幻影上进行了临床前评估，显示出高尺寸精度，CT/MR兼容性，最小的目标剂量偏差（<2%）和亚毫米级的位置可重复性。虽然结果证明了技术上的可行性，但临床验证将是评估放疗的舒适性、可重复性和工作流程整合的下一步。

引用次数: 0

Application of radiomics-based image filtering to improve deformable image registration accuracy in thoracic images 基于放射学的图像滤波在胸部图像中提高变形图像配准精度的应用

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-16 DOI: 10.1016/j.phro.2025.100894

Yoshiro Ieko , Noriyuki Kadoya , Hisanori Ariga

Background and purpose

Deformable image registration (DIR) is an important technique in radiation therapy. To improve DIR accuracy, we applied radiomics-based image filtering as a preprocessing step before DIR.

Materials and methods

Thoracic four-dimensional computed tomography (CT) images of 10 patients with lung or esophageal cancer were examined. Peak-inhale and peak-exhale images were used for DIR. Before DIR, these images were converted into 90-voxel-based radiomics-based filtered images using extracted local radiomics features, respectively. On each filtered image, DIR between the peak-inhale and peak-exhale filtered images was performed. After DIR, the peak-inhale CT images were deformed to peak-exhale CT images using the displacement vector fields obtained from the DIR. The registration errors obtained from each radiomics-based DIR were calculated using landmark pairs and compared with conventional CT-based DIR using the same DIR parameters.

Results

In radiomics-based DIR, the lowest registration errors (95th percentile) for intensity and texture features were 0.96 mm (right-left), 1.35–1.38 mm (anterior-posterior), 2.04–2.13 mm (superior-inferior), and 2.49–2.57 mm (three-dimensional). For CT-based DIR, the corresponding registration errors were 1.31 mm, 1.72 mm, 3.45 mm, and 3.98 mm.

Conclusions

By applying radiomics-based image filtering before DIR as a preprocessing, the registration error was lower than that of conventional CT-based DIR, suggesting that using radiomics may improve the accuracy of DIR.

背景与目的形变图像配准是放射治疗中的一项重要技术。为了提高DIR的精度，我们在DIR前使用基于放射学的图像滤波作为预处理步骤。材料与方法对10例肺癌或食管癌患者的胸部四维计算机断层扫描（CT）图像进行分析。吸气峰和呼气峰图像用于DIR。在DIR之前，这些图像分别使用提取的局部放射组学特征转换为基于90体素的基于放射组学的滤波图像。在每个过滤图像上，在峰值吸气和峰值呼气过滤图像之间执行DIR。在DIR后，利用从DIR中获得的位移向量场将吸气峰CT图像变形为呼气峰CT图像。使用地标对计算每个基于放射组学的DIR得到的配准误差，并与使用相同DIR参数的基于ct的常规DIR进行比较。结果在基于放射组学的DIR中，强度和纹理特征的最小配准误差（95百分位数）分别为0.96 mm（左右）、1.35 ~ 1.38 mm（前后）、2.04 ~ 2.13 mm（上下）和2.49 ~ 2.57 mm（三维）。对于基于ct的DIR，相应的配准误差分别为1.31 mm、1.72 mm、3.45 mm和3.98 mm。结论通过在DIR前进行基于放射组学的图像滤波预处理，配准误差低于传统的基于ct的DIR，表明使用放射组学可以提高DIR的准确性。

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引用次数: 0